package clisk.noise;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import mikera.util.Random;
/**
* Adapted by Mike Anderson from SunFlow renderer source code (Christopher Kulla)
*
* Noise function from Ken Perlin. Additional routines are provided to emulate
* standard Renderman calls. This code was adapted mainly from the mrclasses
* package by Gonzalo Garramuno (http://sourceforge.net/projects/mrclasses/).
*
* @link http://mrl.nyu.edu/~perlin/noise/
*/
public final class Perlin {
private static final double[] G1 = { -1, 1 };
private static final double[][] G2 = { { 1, 0 }, { -1, 0 }, { 0, 1 },
{ 0, -1 } };
private static final double[][] G3 = { { 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 }, { 1, 1, 0 }, { -1, 1, 0 },
{ 0, -1, 1 }, { 0, -1, -1 } };
private static final double[][] G4 = { { -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, 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, 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 },
{ 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 } };
private static final int[] pInitial = { 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, 26, 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, 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, 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, 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,
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, 26, 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, 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, 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, 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 };
private static int[] p = Arrays.copyOf(pInitial, pInitial.length);
public static final double snoise(double x) {
int xf = (int) Math.floor(x);
int X = xf & 255;
x -= xf;
double u = fade(x);
int A = p[X], B = p[X + 1];
return lerp(u, grad(p[A], x), grad(p[B], x - 1));
}
public static final double snoise(double x, double y) {
int xf = (int) Math.floor(x);
int yf = (int) Math.floor(y);
int X = xf & 255;
int Y = yf & 255;
x -= xf;
y -= yf;
double u = fade(x);
double v = fade(y);
int A = p[X] + Y, B = p[X + 1] + Y;
return lerp(v,
lerp(u, grad(p[A], x, y), grad(p[B], x - 1, y)),
lerp(u, grad(p[A + 1], x, y - 1), grad(p[B + 1], x - 1, y - 1)));
}
public static final double snoise(double x, double y, double z) {
int xf = (int) Math.floor(x);
int yf = (int) Math.floor(y);
int zf = (int) Math.floor(z);
int X = xf & 255;
int Y = yf & 255;
int Z = zf & 255;
x -= xf;
y -= yf;
z -= zf;
double u = fade(x);
double v = fade(y);
double w = fade(z);
int A = p[X] + Y, AA = p[A] + Z, AB = p[A + 1] + Z, B = p[X + 1] + Y, BA = p[B]
+ Z, BB = p[B + 1] + Z;
return lerp(
w,
lerp(v,
lerp(u, grad(p[AA], x, y, z), grad(p[BA], x - 1, y, z)),
lerp(u, grad(p[AB], x, y - 1, z),
grad(p[BB], x - 1, y - 1, z))),
lerp(v,
lerp(u, grad(p[AA + 1], x, y, z - 1),
grad(p[BA + 1], x - 1, y, z - 1)),
lerp(u, grad(p[AB + 1], x, y - 1, z - 1),
grad(p[BB + 1], x - 1, y - 1, z - 1))));
}
public static final double snoise(double x, double y, double z, double w) {
int xf = (int) Math.floor(x);
int yf = (int) Math.floor(y);
int zf = (int) Math.floor(z);
int wf = (int) Math.floor(w);
int X = xf & 255;
int Y = yf & 255;
int Z = zf & 255;
int W = wf & 255;
x -= xf;
y -= yf;
z -= zf;
w -= wf;
double u = fade(x);
double v = fade(y);
double t = fade(z);
double s = fade(w);
int A = p[X] + Y, AA = p[A] + Z, AB = p[A + 1] + Z, B = p[X + 1] + Y, BA = p[B]
+ Z, BB = p[B + 1] + Z, AAA = p[AA] + W, AAB = p[AA + 1] + W, ABA = p[AB]
+ W, ABB = p[AB + 1] + W, BAA = p[BA] + W, BAB = p[BA + 1] + W, BBA = p[BB]
+ W, BBB = p[BB + 1] + W;
return lerp(
s,
lerp(t,
lerp(v,
lerp(u, grad(p[AAA], x, y, z, w),
grad(p[BAA], x - 1, y, z, w)),
lerp(u, grad(p[ABA], x, y - 1, z, w),
grad(p[BBA], x - 1, y - 1, z, w))),
lerp(v,
lerp(u, grad(p[AAB], x, y, z - 1, w),
grad(p[BAB], x - 1, y, z - 1, w)),
lerp(u, grad(p[ABB], x, y - 1, z - 1, w),
grad(p[BBB], x - 1, y - 1, z - 1, w)))),
lerp(t,
lerp(v,
lerp(u, grad(p[AAA + 1], x, y, z, w - 1),
grad(p[BAA + 1], x - 1, y, z, w - 1)),
lerp(u,
grad(p[ABA + 1], x, y - 1, z, w - 1),
grad(p[BBA + 1], x - 1, y - 1, z, w - 1))),
lerp(v,
lerp(u,
grad(p[AAB + 1], x, y, z - 1, w - 1),
grad(p[BAB + 1], x - 1, y, z - 1, w - 1)),
lerp(u,
grad(p[ABB + 1], x, y - 1, z - 1, w - 1),
grad(p[BBB + 1], x - 1, y - 1, z - 1,
w - 1)))));
}
public static final double noise(double x) {
return 0.5 + 0.5 * snoise(x);
}
public static final double noise(double x, double y) {
return 0.5 + 0.5 * snoise(x, y);
}
public static final double noise(double x, double y, double z) {
return 0.5 + 0.5 * snoise(x, y, z);
}
public static final double noise(double x, double y, double z, double t) {
return 0.5 + 0.5 * snoise(x, y, z, t);
}
public static final double pnoise(double xi, double period) {
double x = (xi % period) + ((xi < 0) ? period : 0);
return ((period - x) * noise(x) + x * noise(x - period)) / period;
}
public static final double pnoise(double xi, double yi, double w, double h) {
double x = (xi % w) + ((xi < 0) ? w : 0);
double y = (yi % h) + ((yi < 0) ? h : 0);
double w_x = w - x;
double h_y = h - y;
double x_w = x - w;
double y_h = y - h;
return (noise(x, y) * (w_x) * (h_y) + noise(x_w, y) * (x) * (h_y)
+ noise(x_w, y_h) * (x) * (y) + noise(x, y_h) * (w_x) * (y))
/ (w * h);
}
public static final double pnoise(double xi, double yi, double zi, double w,
double h, double d) {
double x = (xi % w) + ((xi < 0) ? w : 0);
double y = (yi % h) + ((yi < 0) ? h : 0);
double z = (zi % d) + ((zi < 0) ? d : 0);
double w_x = w - x;
double h_y = h - y;
double d_z = d - z;
double x_w = x - w;
double y_h = y - h;
double z_d = z - d;
double xy = x * y;
double h_yXd_z = h_y * d_z;
double h_yXz = h_y * z;
double w_xXy = w_x * y;
return (noise(x, y, z) * (w_x) * h_yXd_z + noise(x, y_h, z) * w_xXy
* (d_z) + noise(x_w, y, z) * (x) * h_yXd_z + noise(x_w, y_h, z)
* (xy) * (d_z) + noise(x_w, y_h, z_d) * (xy) * (z)
+ noise(x, y, z_d) * (w_x) * h_yXz + noise(x, y_h, z_d) * w_xXy
* (z) + noise(x_w, y, z_d) * (x) * h_yXz)
/ (w * h * d);
}
public static final double pnoise(double xi, double yi, double zi, double ti,
double w, double h, double d, double p) {
double x = (xi % w) + ((xi < 0) ? w : 0);
double y = (yi % h) + ((yi < 0) ? h : 0);
double z = (zi % d) + ((zi < 0) ? d : 0);
double t = (ti % p) + ((ti < 0) ? p : 0);
double w_x = w - x;
double h_y = h - y;
double d_z = d - z;
double p_t = p - t;
double x_w = x - w;
double y_h = y - h;
double z_d = z - d;
double t_p = t - p;
double xy = x * y;
double d_zXp_t = (d_z) * (p_t);
double zXp_t = z * (p_t);
double zXt = z * t;
double d_zXt = d_z * t;
double w_xXy = w_x * y;
double w_xXh_y = w_x * h_y;
double xXh_y = x * h_y;
return (noise(x, y, z, t) * (w_xXh_y) * d_zXp_t + noise(x_w, y, z, t)
* (xXh_y) * d_zXp_t + noise(x_w, y_h, z, t) * (xy) * d_zXp_t
+ noise(x, y_h, z, t) * (w_xXy) * d_zXp_t
+ noise(x_w, y_h, z_d, t) * (xy) * (zXp_t)
+ noise(x, y, z_d, t) * (w_xXh_y) * (zXp_t)
+ noise(x, y_h, z_d, t) * (w_xXy) * (zXp_t)
+ noise(x_w, y, z_d, t) * (xXh_y) * (zXp_t)
+ noise(x, y, z, t_p) * (w_xXh_y) * (d_zXt)
+ noise(x_w, y, z, t_p) * (xXh_y) * (d_zXt)
+ noise(x_w, y_h, z, t_p) * (xy) * (d_zXt)
+ noise(x, y_h, z, t_p) * (w_xXy) * (d_zXt)
+ noise(x_w, y_h, z_d, t_p) * (xy) * (zXt)
+ noise(x, y, z_d, t_p) * (w_xXh_y) * (zXt)
+ noise(x, y_h, z_d, t_p) * (w_xXy) * (zXt)
+ noise(x_w, y, z_d, t_p) * (xXh_y) * (zXt))
/ (w * h * d * t);
}
public static final double spnoise(double xi, double period) {
double x = (xi % period) + ((xi < 0) ? period : 0);
return (((period - x) * snoise(x) + x * snoise(x - period)) / period);
}
public static final double spnoise(double xi, double yi, double w, double h) {
double x = (xi % w) + ((xi < 0) ? w : 0);
double y = (yi % h) + ((yi < 0) ? h : 0);
double w_x = w - x;
double h_y = h - y;
double x_w = x - w;
double y_h = y - h;
return ((snoise(x, y) * (w_x) * (h_y) + snoise(x_w, y) * (x) * (h_y)
+ snoise(x_w, y_h) * (x) * (y) + snoise(x, y_h) * (w_x) * (y)) / (w * h));
}
public static final double spnoise(double xi, double yi, double zi, double w,
double h, double d) {
double x = (xi % w) + ((xi < 0) ? w : 0);
double y = (yi % h) + ((yi < 0) ? h : 0);
double z = (zi % d) + ((zi < 0) ? d : 0);
double w_x = w - x;
double h_y = h - y;
double d_z = d - z;
double x_w = x - w;
double y_h = y - h;
double z_d = z - d;
double xy = x * y;
double h_yXd_z = h_y * d_z;
double h_yXz = h_y * z;
double w_xXy = w_x * y;
return ((snoise(x, y, z) * (w_x) * h_yXd_z + snoise(x, y_h, z) * w_xXy
* (d_z) + snoise(x_w, y, z) * (x) * h_yXd_z
+ snoise(x_w, y_h, z) * (xy) * (d_z) + snoise(x_w, y_h, z_d)
* (xy) * (z) + snoise(x, y, z_d) * (w_x) * h_yXz
+ snoise(x, y_h, z_d) * w_xXy * (z) + snoise(x_w, y, z_d) * (x)
* h_yXz) / (w * h * d));
}
public static final double spnoise(double xi, double yi, double zi, double ti,
double w, double h, double d, double p) {
double x = (xi % w) + ((xi < 0) ? w : 0);
double y = (yi % h) + ((yi < 0) ? h : 0);
double z = (zi % d) + ((zi < 0) ? d : 0);
double t = (ti % p) + ((ti < 0) ? p : 0);
double w_x = w - x;
double h_y = h - y;
double d_z = d - z;
double p_t = p - t;
double x_w = x - w;
double y_h = y - h;
double z_d = z - d;
double t_p = t - p;
double xy = x * y;
double d_zXp_t = (d_z) * (p_t);
double zXp_t = z * (p_t);
double zXt = z * t;
double d_zXt = d_z * t;
double w_xXy = w_x * y;
double w_xXh_y = w_x * h_y;
double xXh_y = x * h_y;
return ((snoise(x, y, z, t) * (w_xXh_y) * d_zXp_t
+ snoise(x_w, y, z, t) * (xXh_y) * d_zXp_t
+ snoise(x_w, y_h, z, t) * (xy) * d_zXp_t
+ snoise(x, y_h, z, t) * (w_xXy) * d_zXp_t
+ snoise(x_w, y_h, z_d, t) * (xy) * (zXp_t)
+ snoise(x, y, z_d, t) * (w_xXh_y) * (zXp_t)
+ snoise(x, y_h, z_d, t) * (w_xXy) * (zXp_t)
+ snoise(x_w, y, z_d, t) * (xXh_y) * (zXp_t)
+ snoise(x, y, z, t_p) * (w_xXh_y) * (d_zXt)
+ snoise(x_w, y, z, t_p) * (xXh_y) * (d_zXt)
+ snoise(x_w, y_h, z, t_p) * (xy) * (d_zXt)
+ snoise(x, y_h, z, t_p) * (w_xXy) * (d_zXt)
+ snoise(x_w, y_h, z_d, t_p) * (xy) * (zXt)
+ snoise(x, y, z_d, t_p) * (w_xXh_y) * (zXt)
+ snoise(x, y_h, z_d, t_p) * (w_xXy) * (zXt) + snoise(x_w, y,
z_d, t_p) * (xXh_y) * (zXt)) / (w * h * d * t));
}
public static final void seed(long seed) {
Random r = new Random(seed);
// Copy original p values into a list to shuffle
List<Integer> sp = new ArrayList<Integer>(pInitial.length);
for (int i = 0; i < pInitial.length; i++) {
sp.add(pInitial[i]);
}
// Shuffle it using the given seed
Collections.shuffle(sp, r);
// Copy the values into p. Preserve pInitial so that we can always
// shuffle it the same way with the same seed and get the same result
for (int i = 0; i < p.length; i++) {
p[i] = sp.get(i);
}
}
public static final void seed() {
seed((new Random()).nextLong());
}
private static final double fade(double t) {
return t * t * t * (t * (t * 6 - 15) + 10);
}
private static final double lerp(double t, double a, double b) {
return a + t * (b - a);
}
private static final double grad(int hash, double x) {
int h = hash & 0x1;
return x * G1[h];
}
private static final double grad(int hash, double x, double y) {
int h = hash & 0x3;
return x * G2[h][0] + y * G2[h][1];
}
private static final double grad(int hash, double x, double y, double z) {
int h = hash & 15;
return x * G3[h][0] + y * G3[h][1] + z * G3[h][2];
}
private static final double grad(int hash, double x, double y, double z, double w) {
int h = hash & 31;
return x * G4[h][0] + y * G4[h][1] + z * G4[h][2] + w * G4[h][3];
}
}