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OpenSimplex Noise Refactored for C#
/* OpenSimplex Noise in C#
* Ported from https://gist.github.com/KdotJPG/b1270127455a94ac5d19
* and heavily refactored to improve performance. */
using System;
using System.Collections.Generic;
using System.Linq;
using System.Runtime.CompilerServices;
namespace NoiseTest
{
public class OpenSimplexNoise
{
private const double STRETCH_2D = -0.211324865405187; //(1/Math.sqrt(2+1)-1)/2;
private const double STRETCH_3D = -1.0 / 6.0; //(1/Math.sqrt(3+1)-1)/3;
private const double STRETCH_4D = -0.138196601125011; //(1/Math.sqrt(4+1)-1)/4;
private const double SQUISH_2D = 0.366025403784439; //(Math.sqrt(2+1)-1)/2;
private const double SQUISH_3D = 1.0 / 3.0; //(Math.sqrt(3+1)-1)/3;
private const double SQUISH_4D = 0.309016994374947; //(Math.sqrt(4+1)-1)/4;
private const double NORM_2D = 1.0 / 47.0;
private const double NORM_3D = 1.0 / 103.0;
private const double NORM_4D = 1.0 / 30.0;
private byte[] perm;
private byte[] perm2D;
private byte[] perm3D;
private byte[] perm4D;
private static double[] gradients2D = new double[]
{
5, 2, 2, 5,
-5, 2, -2, 5,
5, -2, 2, -5,
-5, -2, -2, -5,
};
private static double[] gradients3D =
{
-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,
};
private static double[] gradients4D =
{
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,
};
private static Contribution2[] lookup2D;
private static Contribution3[] lookup3D;
private static Contribution4[] lookup4D;
static OpenSimplexNoise()
{
var base2D = new int[][]
{
new int[] { 1, 1, 0, 1, 0, 1, 0, 0, 0 },
new int[] { 1, 1, 0, 1, 0, 1, 2, 1, 1 }
};
var p2D = new int[] { 0, 0, 1, -1, 0, 0, -1, 1, 0, 2, 1, 1, 1, 2, 2, 0, 1, 2, 0, 2, 1, 0, 0, 0 };
var lookupPairs2D = new int[] { 0, 1, 1, 0, 4, 1, 17, 0, 20, 2, 21, 2, 22, 5, 23, 5, 26, 4, 39, 3, 42, 4, 43, 3 };
var contributions2D = new Contribution2[p2D.Length / 4];
for (int i = 0; i < p2D.Length; i += 4)
{
var baseSet = base2D[p2D[i]];
Contribution2 previous = null, current = null;
for (int k = 0; k < baseSet.Length; k += 3)
{
current = new Contribution2(baseSet[k], baseSet[k + 1], baseSet[k + 2]);
if (previous == null)
{
contributions2D[i / 4] = current;
}
else
{
previous.Next = current;
}
previous = current;
}
current.Next = new Contribution2(p2D[i + 1], p2D[i + 2], p2D[i + 3]);
}
lookup2D = new Contribution2[64];
for (var i = 0; i < lookupPairs2D.Length; i += 2)
{
lookup2D[lookupPairs2D[i]] = contributions2D[lookupPairs2D[i + 1]];
}
var base3D = new int[][]
{
new int[] { 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1 },
new int[] { 2, 1, 1, 0, 2, 1, 0, 1, 2, 0, 1, 1, 3, 1, 1, 1 },
new int[] { 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 2, 1, 1, 0, 2, 1, 0, 1, 2, 0, 1, 1 }
};
var p3D = new int[] { 0, 0, 1, -1, 0, 0, 1, 0, -1, 0, 0, -1, 1, 0, 0, 0, 1, -1, 0, 0, -1, 0, 1, 0, 0, -1, 1, 0, 2, 1, 1, 0, 1, 1, 1, -1, 0, 2, 1, 0, 1, 1, 1, -1, 1, 0, 2, 0, 1, 1, 1, -1, 1, 1, 1, 3, 2, 1, 0, 3, 1, 2, 0, 1, 3, 2, 0, 1, 3, 1, 0, 2, 1, 3, 0, 2, 1, 3, 0, 1, 2, 1, 1, 1, 0, 0, 2, 2, 0, 0, 1, 1, 0, 1, 0, 2, 0, 2, 0, 1, 1, 0, 0, 1, 2, 0, 0, 2, 2, 0, 0, 0, 0, 1, 1, -1, 1, 2, 0, 0, 0, 0, 1, -1, 1, 1, 2, 0, 0, 0, 0, 1, 1, 1, -1, 2, 3, 1, 1, 1, 2, 0, 0, 2, 2, 3, 1, 1, 1, 2, 2, 0, 0, 2, 3, 1, 1, 1, 2, 0, 2, 0, 2, 1, 1, -1, 1, 2, 0, 0, 2, 2, 1, 1, -1, 1, 2, 2, 0, 0, 2, 1, -1, 1, 1, 2, 0, 0, 2, 2, 1, -1, 1, 1, 2, 0, 2, 0, 2, 1, 1, 1, -1, 2, 2, 0, 0, 2, 1, 1, 1, -1, 2, 0, 2, 0 };
var lookupPairs3D = new int[] { 0, 2, 1, 1, 2, 2, 5, 1, 6, 0, 7, 0, 32, 2, 34, 2, 129, 1, 133, 1, 160, 5, 161, 5, 518, 0, 519, 0, 546, 4, 550, 4, 645, 3, 647, 3, 672, 5, 673, 5, 674, 4, 677, 3, 678, 4, 679, 3, 680, 13, 681, 13, 682, 12, 685, 14, 686, 12, 687, 14, 712, 20, 714, 18, 809, 21, 813, 23, 840, 20, 841, 21, 1198, 19, 1199, 22, 1226, 18, 1230, 19, 1325, 23, 1327, 22, 1352, 15, 1353, 17, 1354, 15, 1357, 17, 1358, 16, 1359, 16, 1360, 11, 1361, 10, 1362, 11, 1365, 10, 1366, 9, 1367, 9, 1392, 11, 1394, 11, 1489, 10, 1493, 10, 1520, 8, 1521, 8, 1878, 9, 1879, 9, 1906, 7, 1910, 7, 2005, 6, 2007, 6, 2032, 8, 2033, 8, 2034, 7, 2037, 6, 2038, 7, 2039, 6 };
var contributions3D = new Contribution3[p3D.Length / 9];
for (int i = 0; i < p3D.Length; i += 9)
{
var baseSet = base3D[p3D[i]];
Contribution3 previous = null, current = null;
for (int k = 0; k < baseSet.Length; k += 4)
{
current = new Contribution3(baseSet[k], baseSet[k + 1], baseSet[k + 2], baseSet[k + 3]);
if (previous == null)
{
contributions3D[i / 9] = current;
}
else
{
previous.Next = current;
}
previous = current;
}
current.Next = new Contribution3(p3D[i + 1], p3D[i + 2], p3D[i + 3], p3D[i + 4]);
current.Next.Next = new Contribution3(p3D[i + 5], p3D[i + 6], p3D[i + 7], p3D[i + 8]);
}
lookup3D = new Contribution3[2048];
for (var i = 0; i < lookupPairs3D.Length; i += 2)
{
lookup3D[lookupPairs3D[i]] = contributions3D[lookupPairs3D[i + 1]];
}
var base4D = new int[][]
{
new int[] { 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1 },
new int[] { 3, 1, 1, 1, 0, 3, 1, 1, 0, 1, 3, 1, 0, 1, 1, 3, 0, 1, 1, 1, 4, 1, 1, 1, 1 },
new int[] { 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 2, 1, 1, 0, 0, 2, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1, 1, 0, 2, 0, 1, 0, 1, 2, 0, 0, 1, 1 },
new int[] { 3, 1, 1, 1, 0, 3, 1, 1, 0, 1, 3, 1, 0, 1, 1, 3, 0, 1, 1, 1, 2, 1, 1, 0, 0, 2, 1, 0, 1, 0, 2, 1, 0, 0, 1, 2, 0, 1, 1, 0, 2, 0, 1, 0, 1, 2, 0, 0, 1, 1 }
};
var p4D = new int[] { 0, 0, 1, -1, 0, 0, 0, 1, 0, -1, 0, 0, 1, 0, 0, -1, 0, 0, -1, 1, 0, 0, 0, 0, 1, -1, 0, 0, 0, 1, 0, -1, 0, 0, -1, 0, 1, 0, 0, 0, -1, 1, 0, 0, 0, 0, 1, -1, 0, 0, -1, 0, 0, 1, 0, 0, -1, 0, 1, 0, 0, 0, -1, 1, 0, 2, 1, 1, 0, 0, 1, 1, 1, -1, 0, 1, 1, 1, 0, -1, 0, 2, 1, 0, 1, 0, 1, 1, -1, 1, 0, 1, 1, 0, 1, -1, 0, 2, 0, 1, 1, 0, 1, -1, 1, 1, 0, 1, 0, 1, 1, -1, 0, 2, 1, 0, 0, 1, 1, 1, -1, 0, 1, 1, 1, 0, -1, 1, 0, 2, 0, 1, 0, 1, 1, -1, 1, 0, 1, 1, 0, 1, -1, 1, 0, 2, 0, 0, 1, 1, 1, -1, 0, 1, 1, 1, 0, -1, 1, 1, 1, 4, 2, 1, 1, 0, 4, 1, 2, 1, 0, 4, 1, 1, 2, 0, 1, 4, 2, 1, 0, 1, 4, 1, 2, 0, 1, 4, 1, 1, 0, 2, 1, 4, 2, 0, 1, 1, 4, 1, 0, 2, 1, 4, 1, 0, 1, 2, 1, 4, 0, 2, 1, 1, 4, 0, 1, 2, 1, 4, 0, 1, 1, 2, 1, 2, 1, 1, 0, 0, 3, 2, 1, 0, 0, 3, 1, 2, 0, 0, 1, 2, 1, 0, 1, 0, 3, 2, 0, 1, 0, 3, 1, 0, 2, 0, 1, 2, 0, 1, 1, 0, 3, 0, 2, 1, 0, 3, 0, 1, 2, 0, 1, 2, 1, 0, 0, 1, 3, 2, 0, 0, 1, 3, 1, 0, 0, 2, 1, 2, 0, 1, 0, 1, 3, 0, 2, 0, 1, 3, 0, 1, 0, 2, 1, 2, 0, 0, 1, 1, 3, 0, 0, 2, 1, 3, 0, 0, 1, 2, 2, 3, 1, 1, 1, 0, 2, 1, 1, 1, -1, 2, 2, 0, 0, 0, 2, 3, 1, 1, 0, 1, 2, 1, 1, -1, 1, 2, 2, 0, 0, 0, 2, 3, 1, 0, 1, 1, 2, 1, -1, 1, 1, 2, 2, 0, 0, 0, 2, 3, 1, 1, 1, 0, 2, 1, 1, 1, -1, 2, 0, 2, 0, 0, 2, 3, 1, 1, 0, 1, 2, 1, 1, -1, 1, 2, 0, 2, 0, 0, 2, 3, 0, 1, 1, 1, 2, -1, 1, 1, 1, 2, 0, 2, 0, 0, 2, 3, 1, 1, 1, 0, 2, 1, 1, 1, -1, 2, 0, 0, 2, 0, 2, 3, 1, 0, 1, 1, 2, 1, -1, 1, 1, 2, 0, 0, 2, 0, 2, 3, 0, 1, 1, 1, 2, -1, 1, 1, 1, 2, 0, 0, 2, 0, 2, 3, 1, 1, 0, 1, 2, 1, 1, -1, 1, 2, 0, 0, 0, 2, 2, 3, 1, 0, 1, 1, 2, 1, -1, 1, 1, 2, 0, 0, 0, 2, 2, 3, 0, 1, 1, 1, 2, -1, 1, 1, 1, 2, 0, 0, 0, 2, 2, 1, 1, 1, -1, 0, 1, 1, 1, 0, -1, 0, 0, 0, 0, 0, 2, 1, 1, -1, 1, 0, 1, 1, 0, 1, -1, 0, 0, 0, 0, 0, 2, 1, -1, 1, 1, 0, 1, 0, 1, 1, -1, 0, 0, 0, 0, 0, 2, 1, 1, -1, 0, 1, 1, 1, 0, -1, 1, 0, 0, 0, 0, 0, 2, 1, -1, 1, 0, 1, 1, 0, 1, -1, 1, 0, 0, 0, 0, 0, 2, 1, -1, 0, 1, 1, 1, 0, -1, 1, 1, 0, 0, 0, 0, 0, 2, 1, 1, 1, -1, 0, 1, 1, 1, 0, -1, 2, 2, 0, 0, 0, 2, 1, 1, -1, 1, 0, 1, 1, 0, 1, -1, 2, 2, 0, 0, 0, 2, 1, 1, -1, 0, 1, 1, 1, 0, -1, 1, 2, 2, 0, 0, 0, 2, 1, 1, 1, -1, 0, 1, 1, 1, 0, -1, 2, 0, 2, 0, 0, 2, 1, -1, 1, 1, 0, 1, 0, 1, 1, -1, 2, 0, 2, 0, 0, 2, 1, -1, 1, 0, 1, 1, 0, 1, -1, 1, 2, 0, 2, 0, 0, 2, 1, 1, -1, 1, 0, 1, 1, 0, 1, -1, 2, 0, 0, 2, 0, 2, 1, -1, 1, 1, 0, 1, 0, 1, 1, -1, 2, 0, 0, 2, 0, 2, 1, -1, 0, 1, 1, 1, 0, -1, 1, 1, 2, 0, 0, 2, 0, 2, 1, 1, -1, 0, 1, 1, 1, 0, -1, 1, 2, 0, 0, 0, 2, 2, 1, -1, 1, 0, 1, 1, 0, 1, -1, 1, 2, 0, 0, 0, 2, 2, 1, -1, 0, 1, 1, 1, 0, -1, 1, 1, 2, 0, 0, 0, 2, 3, 1, 1, 0, 0, 0, 2, 2, 0, 0, 0, 2, 1, 1, 1, -1, 3, 1, 0, 1, 0, 0, 2, 0, 2, 0, 0, 2, 1, 1, 1, -1, 3, 1, 0, 0, 1, 0, 2, 0, 0, 2, 0, 2, 1, 1, 1, -1, 3, 1, 1, 0, 0, 0, 2, 2, 0, 0, 0, 2, 1, 1, -1, 1, 3, 1, 0, 1, 0, 0, 2, 0, 2, 0, 0, 2, 1, 1, -1, 1, 3, 1, 0, 0, 0, 1, 2, 0, 0, 0, 2, 2, 1, 1, -1, 1, 3, 1, 1, 0, 0, 0, 2, 2, 0, 0, 0, 2, 1, -1, 1, 1, 3, 1, 0, 0, 1, 0, 2, 0, 0, 2, 0, 2, 1, -1, 1, 1, 3, 1, 0, 0, 0, 1, 2, 0, 0, 0, 2, 2, 1, -1, 1, 1, 3, 1, 0, 1, 0, 0, 2, 0, 2, 0, 0, 2, -1, 1, 1, 1, 3, 1, 0, 0, 1, 0, 2, 0, 0, 2, 0, 2, -1, 1, 1, 1, 3, 1, 0, 0, 0, 1, 2, 0, 0, 0, 2, 2, -1, 1, 1, 1, 3, 3, 2, 1, 0, 0, 3, 1, 2, 0, 0, 4, 1, 1, 1, 1, 3, 3, 2, 0, 1, 0, 3, 1, 0, 2, 0, 4, 1, 1, 1, 1, 3, 3, 0, 2, 1, 0, 3, 0, 1, 2, 0, 4, 1, 1, 1, 1, 3, 3, 2, 0, 0, 1, 3, 1, 0, 0, 2, 4, 1, 1, 1, 1, 3, 3, 0, 2, 0, 1, 3, 0, 1, 0, 2, 4, 1, 1, 1, 1, 3, 3, 0, 0, 2, 1, 3, 0, 0, 1, 2, 4, 1, 1, 1, 1, 3, 3, 2, 1, 0, 0, 3, 1, 2, 0, 0, 2, 1, 1, 1, -1, 3, 3, 2, 0, 1, 0, 3, 1, 0, 2, 0, 2, 1, 1, 1, -1, 3, 3, 0, 2, 1, 0, 3, 0, 1, 2, 0, 2, 1, 1, 1, -1, 3, 3, 2, 1, 0, 0, 3, 1, 2, 0, 0, 2, 1, 1, -1, 1, 3, 3, 2, 0, 0, 1, 3, 1, 0, 0, 2, 2, 1, 1, -1, 1, 3, 3, 0, 2, 0, 1, 3, 0, 1, 0, 2, 2, 1, 1, -1, 1, 3, 3, 2, 0, 1, 0, 3, 1, 0, 2, 0, 2, 1, -1, 1, 1, 3, 3, 2, 0, 0, 1, 3, 1, 0, 0, 2, 2, 1, -1, 1, 1, 3, 3, 0, 0, 2, 1, 3, 0, 0, 1, 2, 2, 1, -1, 1, 1, 3, 3, 0, 2, 1, 0, 3, 0, 1, 2, 0, 2, -1, 1, 1, 1, 3, 3, 0, 2, 0, 1, 3, 0, 1, 0, 2, 2, -1, 1, 1, 1, 3, 3, 0, 0, 2, 1, 3, 0, 0, 1, 2, 2, -1, 1, 1, 1 };
var lookupPairs4D = new int[] { 0, 3, 1, 2, 2, 3, 5, 2, 6, 1, 7, 1, 8, 3, 9, 2, 10, 3, 13, 2, 16, 3, 18, 3, 22, 1, 23, 1, 24, 3, 26, 3, 33, 2, 37, 2, 38, 1, 39, 1, 41, 2, 45, 2, 54, 1, 55, 1, 56, 0, 57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 256, 3, 258, 3, 264, 3, 266, 3, 272, 3, 274, 3, 280, 3, 282, 3, 2049, 2, 2053, 2, 2057, 2, 2061, 2, 2081, 2, 2085, 2, 2089, 2, 2093, 2, 2304, 9, 2305, 9, 2312, 9, 2313, 9, 16390, 1, 16391, 1, 16406, 1, 16407, 1, 16422, 1, 16423, 1, 16438, 1, 16439, 1, 16642, 8, 16646, 8, 16658, 8, 16662, 8, 18437, 6, 18439, 6, 18469, 6, 18471, 6, 18688, 9, 18689, 9, 18690, 8, 18693, 6, 18694, 8, 18695, 6, 18696, 9, 18697, 9, 18706, 8, 18710, 8, 18725, 6, 18727, 6, 131128, 0, 131129, 0, 131130, 0, 131131, 0, 131132, 0, 131133, 0, 131134, 0, 131135, 0, 131352, 7, 131354, 7, 131384, 7, 131386, 7, 133161, 5, 133165, 5, 133177, 5, 133181, 5, 133376, 9, 133377, 9, 133384, 9, 133385, 9, 133400, 7, 133402, 7, 133417, 5, 133421, 5, 133432, 7, 133433, 5, 133434, 7, 133437, 5, 147510, 4, 147511, 4, 147518, 4, 147519, 4, 147714, 8, 147718, 8, 147730, 8, 147734, 8, 147736, 7, 147738, 7, 147766, 4, 147767, 4, 147768, 7, 147770, 7, 147774, 4, 147775, 4, 149509, 6, 149511, 6, 149541, 6, 149543, 6, 149545, 5, 149549, 5, 149558, 4, 149559, 4, 149561, 5, 149565, 5, 149566, 4, 149567, 4, 149760, 9, 149761, 9, 149762, 8, 149765, 6, 149766, 8, 149767, 6, 149768, 9, 149769, 9, 149778, 8, 149782, 8, 149784, 7, 149786, 7, 149797, 6, 149799, 6, 149801, 5, 149805, 5, 149814, 4, 149815, 4, 149816, 7, 149817, 5, 149818, 7, 149821, 5, 149822, 4, 149823, 4, 149824, 37, 149825, 37, 149826, 36, 149829, 34, 149830, 36, 149831, 34, 149832, 37, 149833, 37, 149842, 36, 149846, 36, 149848, 35, 149850, 35, 149861, 34, 149863, 34, 149865, 33, 149869, 33, 149878, 32, 149879, 32, 149880, 35, 149881, 33, 149882, 35, 149885, 33, 149886, 32, 149887, 32, 150080, 49, 150082, 48, 150088, 49, 150098, 48, 150104, 47, 150106, 47, 151873, 46, 151877, 45, 151881, 46, 151909, 45, 151913, 44, 151917, 44, 152128, 49, 152129, 46, 152136, 49, 152137, 46, 166214, 43, 166215, 42, 166230, 43, 166247, 42, 166262, 41, 166263, 41, 166466, 48, 166470, 43, 166482, 48, 166486, 43, 168261, 45, 168263, 42, 168293, 45, 168295, 42, 168512, 31, 168513, 28, 168514, 31, 168517, 28, 168518, 25, 168519, 25, 280952, 40, 280953, 39, 280954, 40, 280957, 39, 280958, 38, 280959, 38, 281176, 47, 281178, 47, 281208, 40, 281210, 40, 282985, 44, 282989, 44, 283001, 39, 283005, 39, 283208, 30, 283209, 27, 283224, 30, 283241, 27, 283256, 22, 283257, 22, 297334, 41, 297335, 41, 297342, 38, 297343, 38, 297554, 29, 297558, 24, 297562, 29, 297590, 24, 297594, 21, 297598, 21, 299365, 26, 299367, 23, 299373, 26, 299383, 23, 299389, 20, 299391, 20, 299584, 31, 299585, 28, 299586, 31, 299589, 28, 299590, 25, 299591, 25, 299592, 30, 299593, 27, 299602, 29, 299606, 24, 299608, 30, 299610, 29, 299621, 26, 299623, 23, 299625, 27, 299629, 26, 299638, 24, 299639, 23, 299640, 22, 299641, 22, 299642, 21, 299645, 20, 299646, 21, 299647, 20, 299648, 61, 299649, 60, 299650, 61, 299653, 60, 299654, 59, 299655, 59, 299656, 58, 299657, 57, 299666, 55, 299670, 54, 299672, 58, 299674, 55, 299685, 52, 299687, 51, 299689, 57, 299693, 52, 299702, 54, 299703, 51, 299704, 56, 299705, 56, 299706, 53, 299709, 50, 299710, 53, 299711, 50, 299904, 61, 299906, 61, 299912, 58, 299922, 55, 299928, 58, 299930, 55, 301697, 60, 301701, 60, 301705, 57, 301733, 52, 301737, 57, 301741, 52, 301952, 79, 301953, 79, 301960, 76, 301961, 76, 316038, 59, 316039, 59, 316054, 54, 316071, 51, 316086, 54, 316087, 51, 316290, 78, 316294, 78, 316306, 73, 316310, 73, 318085, 77, 318087, 77, 318117, 70, 318119, 70, 318336, 79, 318337, 79, 318338, 78, 318341, 77, 318342, 78, 318343, 77, 430776, 56, 430777, 56, 430778, 53, 430781, 50, 430782, 53, 430783, 50, 431000, 75, 431002, 72, 431032, 75, 431034, 72, 432809, 74, 432813, 69, 432825, 74, 432829, 69, 433032, 76, 433033, 76, 433048, 75, 433065, 74, 433080, 75, 433081, 74, 447158, 71, 447159, 68, 447166, 71, 447167, 68, 447378, 73, 447382, 73, 447386, 72, 447414, 71, 447418, 72, 447422, 71, 449189, 70, 449191, 70, 449197, 69, 449207, 68, 449213, 69, 449215, 68, 449408, 67, 449409, 67, 449410, 66, 449413, 64, 449414, 66, 449415, 64, 449416, 67, 449417, 67, 449426, 66, 449430, 66, 449432, 65, 449434, 65, 449445, 64, 449447, 64, 449449, 63, 449453, 63, 449462, 62, 449463, 62, 449464, 65, 449465, 63, 449466, 65, 449469, 63, 449470, 62, 449471, 62, 449472, 19, 449473, 19, 449474, 18, 449477, 16, 449478, 18, 449479, 16, 449480, 19, 449481, 19, 449490, 18, 449494, 18, 449496, 17, 449498, 17, 449509, 16, 449511, 16, 449513, 15, 449517, 15, 449526, 14, 449527, 14, 449528, 17, 449529, 15, 449530, 17, 449533, 15, 449534, 14, 449535, 14, 449728, 19, 449729, 19, 449730, 18, 449734, 18, 449736, 19, 449737, 19, 449746, 18, 449750, 18, 449752, 17, 449754, 17, 449784, 17, 449786, 17, 451520, 19, 451521, 19, 451525, 16, 451527, 16, 451528, 19, 451529, 19, 451557, 16, 451559, 16, 451561, 15, 451565, 15, 451577, 15, 451581, 15, 451776, 19, 451777, 19, 451784, 19, 451785, 19, 465858, 18, 465861, 16, 465862, 18, 465863, 16, 465874, 18, 465878, 18, 465893, 16, 465895, 16, 465910, 14, 465911, 14, 465918, 14, 465919, 14, 466114, 18, 466118, 18, 466130, 18, 466134, 18, 467909, 16, 467911, 16, 467941, 16, 467943, 16, 468160, 13, 468161, 13, 468162, 13, 468163, 13, 468164, 13, 468165, 13, 468166, 13, 468167, 13, 580568, 17, 580570, 17, 580585, 15, 580589, 15, 580598, 14, 580599, 14, 580600, 17, 580601, 15, 580602, 17, 580605, 15, 580606, 14, 580607, 14, 580824, 17, 580826, 17, 580856, 17, 580858, 17, 582633, 15, 582637, 15, 582649, 15, 582653, 15, 582856, 12, 582857, 12, 582872, 12, 582873, 12, 582888, 12, 582889, 12, 582904, 12, 582905, 12, 596982, 14, 596983, 14, 596990, 14, 596991, 14, 597202, 11, 597206, 11, 597210, 11, 597214, 11, 597234, 11, 597238, 11, 597242, 11, 597246, 11, 599013, 10, 599015, 10, 599021, 10, 599023, 10, 599029, 10, 599031, 10, 599037, 10, 599039, 10, 599232, 13, 599233, 13, 599234, 13, 599235, 13, 599236, 13, 599237, 13, 599238, 13, 599239, 13, 599240, 12, 599241, 12, 599250, 11, 599254, 11, 599256, 12, 599257, 12, 599258, 11, 599262, 11, 599269, 10, 599271, 10, 599272, 12, 599273, 12, 599277, 10, 599279, 10, 599282, 11, 599285, 10, 599286, 11, 599287, 10, 599288, 12, 599289, 12, 599290, 11, 599293, 10, 599294, 11, 599295, 10 };
var contributions4D = new Contribution4[p4D.Length / 16];
for (int i = 0; i < p4D.Length; i += 16)
{
var baseSet = base4D[p4D[i]];
Contribution4 previous = null, current = null;
for (int k = 0; k < baseSet.Length; k += 5)
{
current = new Contribution4(baseSet[k], baseSet[k + 1], baseSet[k + 2], baseSet[k + 3], baseSet[k + 4]);
if (previous == null)
{
contributions4D[i / 16] = current;
}
else
{
previous.Next = current;
}
previous = current;
}
current.Next = new Contribution4(p4D[i + 1], p4D[i + 2], p4D[i + 3], p4D[i + 4], p4D[i + 5]);
current.Next.Next = new Contribution4(p4D[i + 6], p4D[i + 7], p4D[i + 8], p4D[i + 9], p4D[i + 10]);
current.Next.Next.Next = new Contribution4(p4D[i + 11], p4D[i + 12], p4D[i + 13], p4D[i + 14], p4D[i + 15]);
}
lookup4D = new Contribution4[1048576];
for (var i = 0; i < lookupPairs4D.Length; i += 2)
{
lookup4D[lookupPairs4D[i]] = contributions4D[lookupPairs4D[i + 1]];
}
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private static int FastFloor(double x)
{
var xi = (int)x;
return x < xi ? xi - 1 : xi;
}
public OpenSimplexNoise()
: this(DateTime.Now.Ticks)
{
}
public OpenSimplexNoise(long seed)
{
perm = new byte[256];
perm2D = new byte[256];
perm3D = new byte[256];
perm4D = new byte[256];
var source = new byte[256];
for (int i = 0; i < 256; i++)
{
source[i] = (byte)i;
}
seed = seed * 6364136223846793005L + 1442695040888963407L;
seed = seed * 6364136223846793005L + 1442695040888963407L;
seed = seed * 6364136223846793005L + 1442695040888963407L;
for (int i = 255; i >= 0; i--)
{
seed = seed * 6364136223846793005L + 1442695040888963407L;
int r = (int)((seed + 31) % (i + 1));
if (r < 0)
{
r += (i + 1);
}
perm[i] = source[r];
perm2D[i] = (byte)(perm[i] & 0x0E);
perm3D[i] = (byte)((perm[i] % 24) * 3);
perm4D[i] = (byte)(perm[i] & 0xFC);
source[r] = source[i];
}
}
public double Evaluate(double x, double y)
{
var stretchOffset = (x + y) * STRETCH_2D;
var xs = x + stretchOffset;
var ys = y + stretchOffset;
var xsb = FastFloor(xs);
var ysb = FastFloor(ys);
var squishOffset = (xsb + ysb) * SQUISH_2D;
var dx0 = x - (xsb + squishOffset);
var dy0 = y - (ysb + squishOffset);
var xins = xs - xsb;
var yins = ys - ysb;
var inSum = xins + yins;
var hash =
(int)(xins - yins + 1) |
(int)(inSum) << 1 |
(int)(inSum + yins) << 2 |
(int)(inSum + xins) << 4;
var c = lookup2D[hash];
var value = 0.0;
while (c != null)
{
var dx = dx0 + c.dx;
var dy = dy0 + c.dy;
var attn = 2 - dx * dx - dy * dy;
if (attn > 0)
{
var px = xsb + c.xsb;
var py = ysb + c.ysb;
var i = perm2D[(perm[px & 0xFF] + py) & 0xFF];
var valuePart = gradients2D[i] * dx + gradients2D[i + 1] * dy;
attn *= attn;
value += attn * attn * valuePart;
}
c = c.Next;
}
return value * NORM_2D;
}
public double Evaluate(double x, double y, double z)
{
var stretchOffset = (x + y + z) * STRETCH_3D;
var xs = x + stretchOffset;
var ys = y + stretchOffset;
var zs = z + stretchOffset;
var xsb = FastFloor(xs);
var ysb = FastFloor(ys);
var zsb = FastFloor(zs);
var squishOffset = (xsb + ysb + zsb) * SQUISH_3D;
var dx0 = x - (xsb + squishOffset);
var dy0 = y - (ysb + squishOffset);
var dz0 = z - (zsb + squishOffset);
var xins = xs - xsb;
var yins = ys - ysb;
var zins = zs - zsb;
var inSum = xins + yins + zins;
var hash =
(int)(yins - zins + 1) |
(int)(xins - yins + 1) << 1 |
(int)(xins - zins + 1) << 2 |
(int)inSum << 3 |
(int)(inSum + zins) << 5 |
(int)(inSum + yins) << 7 |
(int)(inSum + xins) << 9;
var c = lookup3D[hash];
var value = 0.0;
while (c != null)
{
var dx = dx0 + c.dx;
var dy = dy0 + c.dy;
var dz = dz0 + c.dz;
var attn = 2 - dx * dx - dy * dy - dz * dz;
if (attn > 0)
{
var px = xsb + c.xsb;
var py = ysb + c.ysb;
var pz = zsb + c.zsb;
var i = perm3D[(perm[(perm[px & 0xFF] + py) & 0xFF] + pz) & 0xFF];
var valuePart = gradients3D[i] * dx + gradients3D[i + 1] * dy + gradients3D[i + 2] * dz;
attn *= attn;
value += attn * attn * valuePart;
}
c = c.Next;
}
return value * NORM_3D;
}
public double Evaluate(double x, double y, double z, double w)
{
var stretchOffset = (x + y + z + w) * STRETCH_4D;
var xs = x + stretchOffset;
var ys = y + stretchOffset;
var zs = z + stretchOffset;
var ws = w + stretchOffset;
var xsb = FastFloor(xs);
var ysb = FastFloor(ys);
var zsb = FastFloor(zs);
var wsb = FastFloor(ws);
var squishOffset = (xsb + ysb + zsb + wsb) * SQUISH_4D;
var dx0 = x - (xsb + squishOffset);
var dy0 = y - (ysb + squishOffset);
var dz0 = z - (zsb + squishOffset);
var dw0 = w - (wsb + squishOffset);
var xins = xs - xsb;
var yins = ys - ysb;
var zins = zs - zsb;
var wins = ws - wsb;
var inSum = xins + yins + zins + wins;
var hash =
(int)(zins - wins + 1) |
(int)(yins - zins + 1) << 1 |
(int)(yins - wins + 1) << 2 |
(int)(xins - yins + 1) << 3 |
(int)(xins - zins + 1) << 4 |
(int)(xins - wins + 1) << 5 |
(int)inSum << 6 |
(int)(inSum + wins) << 8 |
(int)(inSum + zins) << 11 |
(int)(inSum + yins) << 14 |
(int)(inSum + xins) << 17;
var c = lookup4D[hash];
var value = 0.0;
while (c != null)
{
var dx = dx0 + c.dx;
var dy = dy0 + c.dy;
var dz = dz0 + c.dz;
var dw = dw0 + c.dw;
var attn = 2 - dx * dx - dy * dy - dz * dz - dw * dw;
if (attn > 0)
{
var px = xsb + c.xsb;
var py = ysb + c.ysb;
var pz = zsb + c.zsb;
var pw = wsb + c.wsb;
var i = perm4D[(perm[(perm[(perm[px & 0xFF] + py) & 0xFF] + pz) & 0xFF] + pw) & 0xFF];
var valuePart = gradients4D[i] * dx + gradients4D[i + 1] * dy + gradients4D[i + 2] * dz + gradients4D[i + 3] * dw;
attn *= attn;
value += attn * attn * valuePart;
}
c = c.Next;
}
return value * NORM_4D;
}
private class Contribution2
{
public double dx, dy;
public int xsb, ysb;
public Contribution2 Next;
public Contribution2(double multiplier, int xsb, int ysb)
{
dx = -xsb - multiplier * SQUISH_2D;
dy = -ysb - multiplier * SQUISH_2D;
this.xsb = xsb;
this.ysb = ysb;
}
}
private class Contribution3
{
public double dx, dy, dz;
public int xsb, ysb, zsb;
public Contribution3 Next;
public Contribution3(double multiplier, int xsb, int ysb, int zsb)
{
dx = -xsb - multiplier * SQUISH_3D;
dy = -ysb - multiplier * SQUISH_3D;
dz = -zsb - multiplier * SQUISH_3D;
this.xsb = xsb;
this.ysb = ysb;
this.zsb = zsb;
}
}
private class Contribution4
{
public double dx, dy, dz, dw;
public int xsb, ysb, zsb, wsb;
public Contribution4 Next;
public Contribution4(double multiplier, int xsb, int ysb, int zsb, int wsb)
{
dx = -xsb - multiplier * SQUISH_4D;
dy = -ysb - multiplier * SQUISH_4D;
dz = -zsb - multiplier * SQUISH_4D;
dw = -wsb - multiplier * SQUISH_4D;
this.xsb = xsb;
this.ysb = ysb;
this.zsb = zsb;
this.wsb = wsb;
}
}
}
}
This is free and unencumbered software released into the public domain.
Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.
In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit
of the public at large and to the detriment of our heirs and
successors. We intend this dedication to be an overt act of
relinquishment in perpetuity of all present and future rights to this
software under copyright law.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
For more information, please refer to <http://unlicense.org/>
@ckoshikumo
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Hey, thanks for this gist. I'm trying to port the 2D part to C, but there's a part which I can't understand (probably because I don't really know C#). I wonder if you could point me to the right direction...

First, you declare base2d, which seems to be a [2][9] array-of-arrays. Then, you use the indexes stored in p2D to access the correct base2d row (baseSet = base2d[p2D[i]], line 86). But some of the indexes stored in p2D are outside the 0-1 range, which, as I understand it, should be the valid range for a [2] array. Shouldn't base2d[p2D[3]], for example, throw an IndexOutOfRangeException, since p2D[3] == -1, which is out of bounds?

I have no access to a C# compiler, so I can't really check, I'm trying to understand the code by reading it. But I can't figure this bit out!

Thanks for any help!

@Stormcaller
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Hey -- I'm not the author, I was just looking at noise generators and saw this. If/when the original author responds he can tell if I'm wrong but in the meantime, I'd like to share my understanding,
from lines 84 to 86:

for (int i = 0; i < p2D.Length; i += 4)
                var baseSet = base2D[p2D[i]];

This starts at p2D[0] and it goes 4, 8, 12, 16, 20

var p2D = new int[] { 0, 0, 1, -1, 0, 0, -1, 1, 0, 2, 1, 1, 1, 2, 2, 0, 1, 2, 0, 2, 1, 0, 0, 0 };

and p2D has 0 for the first three iterations and 1 for the rest.

{ 0<-, 0, 1, -1, 0<-, 0, -1, 1, 0<-, 2, 1, 1, 1<-, 2, 2, 0, 1<-, 2, 0, 2, 1<-, 0, 0, 0 };

I also did a simple benchmark by comparison to the other c# fork(no optimizations): https://gist.github.com/omgwtfgames/601497972e4e30fd9c5f
the speed difference was ~30% for 2D and ~50% for 4D noise. My benchmark is probably worthless as far as numbers go but it does prove that there is a significant difference between the two.

@ckoshikumo
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Oh, you're right, of course! I was so confused by the presence of out-of-range indexes that I didn't realized they would never be used. Thank you very much for the explanation.

Correction: they are used, just not as indexes. Line 101 uses the ints following the index as arguments to Contribution's constructor.

@digitalshadow
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Author

Sorry, I don't log in to github very often or I would have replied sooner.

A more detailed description can be found here if you're interested: https://www.reddit.com/r/proceduralgeneration/comments/2v2mgy/optimizing_opensimplex_part_2_hyperplanes_bit/

I only imagined people would be getting here through the reddit post, so I didn't think to put a detailed explanation of what's going on here.

Yeah, the data for p2D, p3D and p4D has indexes for base values mixed in with the offsets which can be negative so you won't get an index out of range.

@kpietraszko
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Is it alright to consider this gist as in public domain and to use it in commercial applications?

@EnlightenedOne
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Seconded on the need for a more explicit license on this port. Also any significant instability in using floats over doubles to speed things up marginally?

@digitalshadow
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Author

Added a license file as requested.

Using floats shouldn't cause any issues. In my tests the difference was rather insignificant, but it probably depends on your target platform.

@EnlightenedOne
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EnlightenedOne commented May 8, 2017

Thank you very much digitalshadow, if I should spot any optimisations or bugs I will flag them here, you are most generous! :)

@MarkuBu
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MarkuBu commented May 31, 2017

As far as I can see this is just the basic noise function. How can I add octaves, persistence, lacunarity or a scale value to this functions?

@EnlightenedOne
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One resource to start with if you want to get thrown in to applying this noise is:
http://studentgamedev.blogspot.co.uk/2013/09/unity-voxel-tutorial-part-3-perlin.html

@zelding
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zelding commented Nov 10, 2017

Wouldn't make the whole thing much faster if the Contribution classes were structs?

Also @MarkuBu You pretty much have to implement them by yourself; here is some help for that; it should be easy ;) https://www.youtube.com/watch?v=MRNFcywkUSA&list=PLFt_AvWsXl0eBW2EiBtl_sxmDtSgZBxB3&index=3

@Spongman
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Spongman commented Mar 6, 2019

if anyone's interested, i created a javascript version of this here: https://gist.github.com/Spongman/d3786384fe4c6ee5b2770473b15bce19

demo here: https://codepen.io/Spongman/pen/YgNOJe?editors=0010

a few things i changed:

  • i removed the linked-list structure from the ContributionX classes. this makes it possible to pre-allocate and reuse the Contribution classes that are otherwise duplicated in this line:
                    current = new Contribution3(baseSet[k], baseSet[k + 1], baseSet[k + 2], baseSet[k + 3]);

thus, in the 4D case, only 30 Contribution4 objects are now allocated.

  • replaced the linked list with an array
  • programmatically generate the gradients (genGradients()), instead of using constants.
  • reversed the lookup map definition to avoid duplicating the keys in the literal.
  • changed the random number algorithm to xoshiro128ss which doesn't rely on 64-bit integer math.

@KdotJPG
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KdotJPG commented Dec 29, 2019

Hi, I released two new noises and also updated the gradient set in OpenSimplex. https://github.com/KdotJPG/New-Simplex-Style-Gradient-Noise

I made a version of this with the updated gradient sets too, if you want to update your repo. https://gist.github.com/KdotJPG/f271080228b55056e6da70c73eb3e9b1

@JaapWijnen
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Does it make sense to also make a function generating 1d noise? I'm not sure where the normalization factors come from and some other values, so wouldn't know how to do this exactly.

@Lord-drageon
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Lord-drageon commented Jul 14, 2020

how exactly do you use this noise function? i dont understand quite how its used.
i mean i get that i need to access the double Evaluate, but im not sure how.

@Lord-drageon
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Lord-drageon commented Jul 14, 2020

nvm i figured it out: what you had to do was import the script into a new c# script, make a new script(i called it noisefilter2) and(at least in unity), write the following code

using NoiseTest;
using System.Collections;
using System.Collections.Generic;
using UnityEngine;

public class noisefilter2
{
OpenSimplexNoise noisetes = new OpenSimplexNoise();
public double evaluate(Vector3(or vector2 or vector4) poitn) {
double val = (noisetes.Evaluate(poitn.x, poitn.y, poitn.z)+1)/2;
return val;
}

}

what it does is it makes a new noise thingy(im not sure what the technical term is) and calls it noisetes(this is just an example name). then it makes an evaluate function that runs in a vector3(or, if you want, a vector2 or vector1) uses the noisetes to evaluate it, and returns the evaluated. then you just need to call it in a different function like so:

noisefilter2 noiFilter = new noisefilter2();

and later in that same script, (when you need the noise itself), call

double (whatever you want to call it)= noiFilter.evaluate(pass in a point here);

@KdotJPG
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KdotJPG commented Jul 16, 2020

@Lord-drageon That is correct! Also you might find cleaner output results from my successor to the OpenSimplex noise algorithm, here: https://github.com/KdotJPG/OpenSimplex2/blob/master/csharp/OpenSimplex2S.cs

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