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Any suggestions on how to get more 'jagged' results like linear interpolation gives with value noise using a faster algorithm concept like simplex noise?

Simplex noise I haven't spent enough time trying to understand and am instead just using what is posted here with some very slight modifications.

This is for 2D and is being done in C# currently. The purpose is for part of my terrain generation process.

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,
  131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,23,
  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};
  // 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), where
    // c = (3-sqrt(3))/6
    double x1 = x0 - i1 + G2; // Offsets for middle corner in (x,y) unskewed 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 corners
    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);
  }
share|improve this question
2  
Is there any chance that you could add the code inline to your question? I personally don't like having to download a file to answer someone's question. –  Polar May 30 '13 at 14:08
1  
The number one way to get less-smooth results is fractal noise - adding additional octaves of noise at smaller scales. You could also try abs(noise()) or 1 - abs(noise()) for sharp valleys or ridgelines respectively. Finally, have a look at Giliam de Carpentier's blog for some other ideas of using noise in procedural terrain generation. –  Nathan Reed May 30 '13 at 20:15
    
@Polar I added only what is needed for 2D noise since the code at the link shows multiple pages of code for 2d, 3d, and 4d cases. –  Tim Winter May 31 '13 at 0:47
1  
Depends on what the final effect you're after is. Sometimes a rand () call is sufficient. –  Darth Satan May 31 '13 at 7:18

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