noise - 任何单纯形噪声教程或资源？

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我想创建一个类似地形的 3D 噪声生成器，经过一些研究后，我得出结论，Simplex Noise 是迄今为止最好的噪声类型。

我觉得这个名字很有误导性，因为我很难找到关于这个主题的资源，而且我找到的资源通常写得不好。

我基本上正在寻找的是一个很好的资源/教程，逐步解释单纯形噪声的工作原理，并解释如何将其实现到程序中。

我不是在寻找解释如何使用图书馆或其他东西的资源。

Answer 1

在教程推荐的基础上，我将尝试解释如何使用现有的 java 源来创建单个 Octave 音阶的单纯形噪声。
单纯噪声码
这部分代码由 Stefan Gustavson 创建并置于公共(public)领域。可以找到here .为方便起见，在此引用

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.util.Random;
import javax.imageio.ImageIO;


/*
 * A speed-improved simplex noise algorithm for 2D, 3D and 4D in Java.
 *
 * 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 author,
 * Stefan Gustavson. You may use it as you see fit, but
 * attribution is appreciated.
 *
 */

public class SimplexNoise_octave {  // Simplex noise in 2D, 3D and 4D
  
  public static int RANDOMSEED=0;
  private static int NUMBEROFSWAPS=400;  
  
  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_supply[] = {151,160,137,91,90,15, //this contains all the numbers between 0 and 255, these are put in a random order depending upon the seed
  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 short p[]=new short[p_supply.length];
  
  // To remove the need for index wrapping, double the permutation table length
  private short perm[] = new short[512];
  private short permMod12[] = new short[512];
  public SimplexNoise_octave(int seed) {
    p=p_supply.clone();
    
    if (seed==RANDOMSEED){
        Random rand=new Random();
        seed=rand.nextInt();
    }
    
    //the random for the swaps
    Random rand=new Random(seed);
    
    //the seed determines the swaps that occur between the default order and the order we're actually going to use
    for(int i=0;i<NUMBEROFSWAPS;i++){
        int swapFrom=rand.nextInt(p.length);
        int swapTo=rand.nextInt(p.length);
        
        short temp=p[swapFrom];
        p[swapFrom]=p[swapTo];
        p[swapTo]=temp;
    }
    
    
    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 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);
  }


  // 3D simplex noise
  public double noise(double xin, double yin, double zin) {
    double n0, n1, n2, n3; // Noise contributions from the four corners
    // Skew the input space to determine which simplex cell we're in
    double s = (xin+yin+zin)*F3; // Very nice and simple skew factor for 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 tetrahedron.
    // 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 corners
    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 double noise(double x, double y, double z, double w) {

    double n0, n1, n2, n3, n4; // Noise contributions from the five corners
    // Skew the (x,y,z,w) space to determine which cell of 24 simplices 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) space
    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 need to
    // determine the magnitude ordering of x0, y0, z0 and w0.
    // Six pair-wise comparisons are performed between each possible pair
    // of the four coordinates, and the results are used to rank the numbers.
    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 corner
    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 make any sense.
    // We use a thresholding to set the coordinates in turn from the largest 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 corners
    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;
    }
  }

}

坦率地说，我认为这整个类是一个带有公共(public)构造函数的黑匣子 public SimplexNoise_octave(int seed) , 和 3 个公共(public)方法 public double noise(double xin, double yin) , public double noise(double xin, double yin, double zin)和 public double noise(double x, double y, double z, double w) .
您可以像使用 perlin 噪声等效项一样使用这些方法。

SimplexNoise_octave(int seed)

为您想要的每个 Octave 创建 1 个 SimplexNoise_octave，每个都应该有自己的种子

public double noise(double xin, double yin)

调用以在这些坐标处获取该 Octave 音阶的特定噪声值。笔记;坐标应预先缩放(稍后更多)。其他 noise功能相同，但更高维度。
创建 Octave 音阶
就像在 perlin 噪声中一样，您通常会结合几个 Octave 音阶的噪声来创建分形噪声(这会为您提供类似地形的特征)。请注意，3D 地形高度是由 2D 噪声创建的。
使用以下比率组合几个 Octave 音阶

frequency = 2^i  
amplitude = persistence^i 

对于每个倍频程 (i)，您将输入坐标除以频率，然后将结果乘以幅度；这给出了类似地形的外观。持久性用于影响地形的外观，高持久性(接近 1)会产生多岩石的山地地形。低持久性(接近 0)给出缓慢变化的平坦地形。见 tag page更多细节。
如何使用它的示例如下所示:

import java.util.Random;

public class SimplexNoise {
    
    SimplexNoise_octave[] octaves;
    double[] frequencys;
    double[] amplitudes;
    
    int largestFeature;
    double persistence;
    int seed;
    
    public SimplexNoise(int largestFeature,double persistence, int seed){
        this.largestFeature=largestFeature;
        this.persistence=persistence;
        this.seed=seed;

        //recieves a number (eg 128) and calculates what power of 2 it is (eg 2^7)
        int numberOfOctaves=(int)Math.ceil(Math.log10(largestFeature)/Math.log10(2));
        
        octaves=new SimplexNoise_octave[numberOfOctaves];
        frequencys=new double[numberOfOctaves];
        amplitudes=new double[numberOfOctaves];
        
        Random rnd=new Random(seed);
        
        for(int i=0;i<numberOfOctaves;i++){
            octaves[i]=new SimplexNoise_octave(rnd.nextInt());

            frequencys[i] = Math.pow(2,i);
            amplitudes[i] = Math.pow(persistence,octaves.length-i);
          

        
            
        }
        
    }
    
    
    public double getNoise(int x, int y){
        
        double result=0;
        
        for(int i=0;i<octaves.length;i++){
          //double frequency = Math.pow(2,i);
          //double amplitude = Math.pow(persistence,octaves.length-i);
          
          result=result+octaves[i].noise(x/frequencys[i], y/frequencys[i])* amplitudes[i];
        }

        
        return result;
        
    }
    
    public double getNoise(int x,int y, int z){
        
        double result=0;
        
        for(int i=0;i<octaves.length;i++){
          double frequency = Math.pow(2,i);
          double amplitude = Math.pow(persistence,octaves.length-i);
          
          result=result+octaves[i].noise(x/frequency, y/frequency,z/frequency)* amplitude;
        }

        
        return result;
        
    }
} 

这将创建提供大小在 1 和 largestFeature 之间的特征的 Octave 音阶。 ，我发现这很有用，但 1 是最小的尺寸并没有什么特别之处，你可以修改它。它在 -1 和 1 之间输出，根据需要进行缩放。
用法
将使用此类的示例 main 方法如下

public static void main(String args[]){
    SimplexNoise simplexNoise=new SimplexNoise(100,0.1,5000);
    

    
    double xStart=0;
    double XEnd=500;
    double yStart=0;
    double yEnd=500;
    
    int xResolution=200;
    int yResolution=200;
    
    double[][] result=new double[xResolution][yResolution];
    
    for(int i=0;i<xResolution;i++){
        for(int j=0;j<yResolution;j++){
            int x=(int)(xStart+i*((XEnd-xStart)/xResolution));
            int y=(int)(yStart+j*((yEnd-yStart)/yResolution));
            result[i][j]=0.5*(1+simplexNoise.getNoise(x,y));
        }
    }
    
    ImageWriter.greyWriteImage(result);
    
    
    
}

此方法使用我自己的 ImageWriter 类只是将输出呈现到文件

import java.awt.Color;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;

public class ImageWriter {
    //just convinence methods for debug
    
    public static void greyWriteImage(double[][] data){
        //this takes and array of doubles between 0 and 1 and generates a grey scale image from them
        
        BufferedImage image = new BufferedImage(data.length,data[0].length, BufferedImage.TYPE_INT_RGB);

        for (int y = 0; y < data[0].length; y++)
        {
          for (int x = 0; x < data.length; x++)
          {
            if (data[x][y]>1){
                data[x][y]=1;
            }
            if (data[x][y]<0){
                data[x][y]=0;
            }
              Color col=new Color((float)data[x][y],(float)data[x][y],(float)data[x][y]); 
            image.setRGB(x, y, col.getRGB());
          }
        }
        
        try {
            // retrieve image
            File outputfile = new File("saved.png");
            outputfile.createNewFile();
            
            ImageIO.write(image, "png", outputfile);
        } catch (IOException e) {
            //o no! Blank catches are bad
            throw new RuntimeException("I didn't handle this very well");
        }
    }
    
    

}