bunch of noise stuff

simtools/sim2.hpp

@@ -32,14 +32,14 @@ private:
     Vec4 waterColor;
     
 public:
-    Sim2(int width = 512, int height = 512, uint32_t seed = 12345) 
+    Sim2(int width = 512, int height = 512, uint32_t seed = 42) 
         : gridWidth(width), gridHeight(height), scale(4.0f), octaves(4), 
           persistence(0.5f), lacunarity(2.0f), seed(seed), offset(0, 0),
           elevationMultiplier(1.0f), waterLevel(0.3f),
           landColor(0.2f, 0.8f, 0.2f, 1.0f),  // Green
           waterColor(0.2f, 0.3f, 0.8f, 1.0f)  // Blue
     {
-        noiseGenerator = std::make_unique<Noise2>(seed);
+        noiseGenerator = std::make_unique<Noise2>(seed,Noise2::WORLEY,Noise2::PRECOMPUTED);
         generateTerrain();
     }
     

simtools/sim22.hpp

@@ -13,6 +13,22 @@ class Sim2 {
 private:
     Noise2 noise;
     Grid2 terrainGrid;
-}
+    int width;
+    int height;
+
+    float scale;
+    int octaves;
+    float lacunarity;
+    int seed;
+    float elevationMult;
+    float waterLevel;
+    Vec4 landColor;
+    Vec4 waterColor;
+    float erosion;
+public:
+    Sim2(int width = 512, int height = 512, int seed = 42, float scale = 4) : 
+        width(width), height(height), scale(scale), octaves(4), seed(seed)
+    { }
+};
 
 #endif

util/noise2.hpp

@@ -6,20 +6,122 @@
 #include <random>
 #include <functional>
 #include <algorithm>
+#include <array>
+#include <vector>
+#include <unordered_map>
 
 struct Grad { float x, y; };
-std::array<Grad, 256> gradients;
 
 class Noise2 {
+public:
+    enum NoiseType {
+        PERLIN,
+        SIMPLEX,
+        VALUE,
+        WORLEY,
+        GABOR,
+        POISSON_DISK,
+        FRACTAL,
+        WAVELET,
+        GAUSSIAN,
+        CELLULAR
+    };
+    
+    enum GradientType {
+        HASH_BASED,
+        SIN_BASED,
+        DOT_BASED,
+        PRECOMPUTED
+    };
+
 private:
     std::mt19937 rng;
     std::uniform_real_distribution<float> dist;
-public:
-    Noise2(uint32_t seed = 0) : rng(seed), dist(0.0f, 1.0f) {}
     
-    // Set random seed
+    // Precomputed gradient directions for 8 directions
+    static constexpr std::array<Grad, 8> grads = {
+        Grad{1.0f, 0.0f}, 
+        Grad{0.707f, 0.707f}, 
+        Grad{0.0f, 1.0f}, 
+        Grad{-0.707f, 0.707f},
+        Grad{-1.0f, 0.0f}, 
+        Grad{-0.707f, -0.707f}, 
+        Grad{0.0f, -1.0f}, 
+        Grad{0.707f, -0.707f}
+    };
+
+    NoiseType currentType;
+    GradientType gradType;
+    uint32_t currentSeed;
+    
+    // Permutation table for Simplex noise
+    std::array<int, 512> perm;
+    
+    // For Worley noise
+    std::vector<Vec2> featurePoints;
+    
+    // For Gabor noise
+    float gaborFrequency;
+    float gaborBandwidth;
+    
+    // For wavelet noise
+    std::vector<float> waveletCoefficients;
+    
+public:
+    Noise2(uint32_t seed = 0, NoiseType type = PERLIN, GradientType gradType = PRECOMPUTED) : 
+            rng(seed), dist(0.0f, 1.0f), currentType(type), gradType(gradType),
+            currentSeed(seed), gaborFrequency(4.0f), gaborBandwidth(0.5f) 
+    {
+        initializePermutationTable(seed);
+        initializeFeaturePoints(64, seed); // Default 64 feature points
+        initializeWaveletCoefficients(32, seed); // 32x32 wavelet coefficients
+    }
+    
+    // Set random seed and reinitialize dependent structures
     void setSeed(uint32_t seed) {
+        currentSeed = seed;
         rng.seed(seed);
+        initializePermutationTable(seed);
+        initializeFeaturePoints(featurePoints.size(), seed);
+        initializeWaveletCoefficients(static_cast<int>(std::sqrt(waveletCoefficients.size())), seed);
+    }
+    
+    // Set noise type
+    void setNoiseType(NoiseType type) {
+        currentType = type;
+    }
+    
+    // Set gradient type
+    void setGradientType(GradientType type) {
+        gradType = type;
+    }
+    
+    // Main noise function that routes to the selected algorithm
+    float noise(float x, float y, int octaves = 1, float persistence = 0.5f, float lacunarity = 2.0f) {
+        switch (currentType) {
+            case PERLIN:
+                return perlinNoise(x, y, octaves, persistence, lacunarity);
+            case SIMPLEX:
+                return simplexNoise(x, y, octaves, persistence, lacunarity);
+            case VALUE:
+                return valueNoise(x, y, octaves, persistence, lacunarity);
+            case WORLEY:
+                return worleyNoise(x, y);
+            case GABOR:
+                return gaborNoise(x, y);
+            case POISSON_DISK:
+                return poissonDiskNoise(x, y);
+            case FRACTAL:
+                return fractalNoise(x, y, octaves, persistence, lacunarity);
+            case WAVELET:
+                return waveletNoise(x, y);
+            case GAUSSIAN:
+                return gaussianNoise(x, y);
+            case CELLULAR:
+                return cellularNoise(x, y);
+            default:
+                return perlinNoise(x, y, octaves, persistence, lacunarity);
+        }
     }
     
     // Generate simple value noise
@@ -56,7 +158,141 @@ public:
         return (total / maxValue + 1.0f) * 0.5f; // Normalize to [0,1]
     }
     
-    // Generate a grayscale noise grid
+    float simplexNoise(float x, float y, int octaves = 1, float persistence = 0.5f, float lacunarity = 2.0f) {
+        float total = 0.0f;
+        float frequency = 1.0f;
+        float amplitude = 1.0f;
+        float maxValue = 0.0f;
+        
+        for (int i = 0; i < octaves; i++) {
+            total += rawSimplexNoise(x * frequency, y * frequency) * amplitude;
+            maxValue += amplitude;
+            amplitude *= persistence;
+            frequency *= lacunarity;
+        }
+        
+        return (total / maxValue + 1.0f) * 0.5f;
+    }
+    
+    // Worley (cellular) noise
+    float worleyNoise(float x, float y) {
+        if (featurePoints.empty()) return 0.0f;
+        
+        // Find the closest and second closest feature points
+        float minDist1 = std::numeric_limits<float>::max();
+        float minDist2 = std::numeric_limits<float>::max();
+        
+        for (const auto& point : featurePoints) {
+            float dx = x - point.x;
+            float dy = y - point.y;
+            float dist = dx * dx + dy * dy; // Squared distance for performance
+            
+            if (dist < minDist1) {
+                minDist2 = minDist1;
+                minDist1 = dist;
+            } else if (dist < minDist2) {
+                minDist2 = dist;
+            }
+        }
+        
+        // Return distance to closest feature point (normalized)
+        return std::sqrt(minDist1);
+    }
+    
+    // Cellular noise variation
+    float cellularNoise(float x, float y) {
+        if (featurePoints.empty()) return 0.0f;
+        
+        float minDist1 = std::numeric_limits<float>::max();
+        float minDist2 = std::numeric_limits<float>::max();
+        
+        for (const auto& point : featurePoints) {
+            float dx = x - point.x;
+            float dy = y - point.y;
+            float dist = dx * dx + dy * dy;
+            
+            if (dist < minDist1) {
+                minDist2 = minDist1;
+                minDist1 = dist;
+            } else if (dist < minDist2) {
+                minDist2 = dist;
+            }
+        }
+        
+        // Cellular pattern: second closest minus closest
+        return std::sqrt(minDist2) - std::sqrt(minDist1);
+    }
+    
+    // Gabor noise
+    float gaborNoise(float x, float y) {
+        // Simplified Gabor noise - in practice this would be more complex
+        float gaussian = std::exp(-(x*x + y*y) / (2.0f * gaborBandwidth * gaborBandwidth));
+        float cosine = std::cos(2.0f * M_PI * gaborFrequency * (x + y));
+        
+        return gaussian * cosine;
+    }
+    
+    // Poisson disk noise
+    float poissonDiskNoise(float x, float y) {
+        // Sample Poisson disk distribution
+        // This is a simplified version - full implementation would use more sophisticated sampling
+        float minDist = std::numeric_limits<float>::max();
+        
+        for (const auto& point : featurePoints) {
+            float dx = x - point.x;
+            float dy = y - point.y;
+            float dist = std::sqrt(dx * dx + dy * dy);
+            minDist = std::min(minDist, dist);
+        }
+        
+        return 1.0f - std::min(minDist * 10.0f, 1.0f); // Invert and scale
+    }
+    
+    // Fractal noise (fractional Brownian motion)
+    float fractalNoise(float x, float y, int octaves = 8, float persistence = 0.5f, float lacunarity = 2.0f) {
+        float total = 0.0f;
+        float frequency = 1.0f;
+        float amplitude = 1.0f;
+        float maxValue = 0.0f;
+        
+        for (int i = 0; i < octaves; i++) {
+            total += improvedNoise(x * frequency, y * frequency) * amplitude;
+            maxValue += amplitude;
+            amplitude *= persistence;
+            frequency *= lacunarity;
+        }
+        
+        // Fractal noise often has wider range, so we don't normalize as strictly
+        return total;
+    }
+    
+    // Wavelet noise
+    float waveletNoise(float x, float y) {
+        // Simplified wavelet noise using precomputed coefficients
+        int ix = static_cast<int>(std::floor(x * 4)) % 32;
+        int iy = static_cast<int>(std::floor(y * 4)) % 32;
+        
+        if (ix < 0) ix += 32;
+        if (iy < 0) iy += 32;
+        
+        return waveletCoefficients[iy * 32 + ix];
+    }
+    
+    // Gaussian noise
+    float gaussianNoise(float x, float y) {
+        // Use coordinates to seed RNG for deterministic results
+        rng.seed(static_cast<uint32_t>(x * 1000 + y * 1000 + currentSeed));
+        
+        // Box-Muller transform for Gaussian distribution
+        float u1 = dist(rng);
+        float u2 = dist(rng);
+        float z0 = std::sqrt(-2.0f * std::log(u1)) * std::cos(2.0f * M_PI * u2);
+        
+        // Normalize to [0,1] range
+        return (z0 + 3.0f) / 6.0f; // Assuming 3 sigma covers most of the distribution
+    }
+    
+    // Generate a grayscale noise grid using current noise type
     Grid2 generateGrayNoise(int width, int height, 
                            float scale = 1.0f, 
                            int octaves = 1, 
@@ -72,7 +308,7 @@ public:
                 float nx = (x + offset.x) / width * scale;
                 float ny = (y + offset.y) / height * scale;
                 
-                float noiseValue = perlinNoise(nx, ny, octaves, persistence);
+                float noiseValue = noise(nx, ny, octaves, persistence);
                 
                 // Convert to position and grayscale color
                 Vec2 position(x, y);
@@ -126,15 +362,15 @@ public:
                 float nx = (x + offset.x) / width;
                 float ny = (y + offset.y) / height;
                 
-                // Generate separate noise for each channel
+                // Generate separate noise for each channel using current noise type
-                float r = perlinNoise(nx * scale.x, ny * scale.x, 
+                float r = noise(nx * scale.x, ny * scale.x, 
-                                     static_cast<int>(octaves.x), persistence.x);
+                               static_cast<int>(octaves.x), persistence.x);
-                float g = perlinNoise(nx * scale.y, ny * scale.y, 
+                float g = noise(nx * scale.y, ny * scale.y, 
-                                     static_cast<int>(octaves.y), persistence.y);
+                               static_cast<int>(octaves.y), persistence.y);
-                float b = perlinNoise(nx * scale.z, ny * scale.z, 
+                float b = noise(nx * scale.z, ny * scale.z, 
-                                     static_cast<int>(octaves.z), persistence.z);
+                               static_cast<int>(octaves.z), persistence.z);
-                float a = perlinNoise(nx * scale.w, ny * scale.w, 
+                float a = noise(nx * scale.w, ny * scale.w, 
-                                     static_cast<int>(octaves.w), persistence.w);
+                               static_cast<int>(octaves.w), persistence.w);
                 
                 Vec2 position(x, y);
                 Vec4 color(r, g, b, a);
@@ -164,7 +400,7 @@ public:
                 float ny = (y + offset.y) / height * scale;
                 
                 // Use multiple octaves for more natural terrain
-                float heightValue = perlinNoise(nx, ny, octaves, persistence);
+                float heightValue = noise(nx, ny, octaves, persistence);
                 
                 // Apply some curve to make it more terrain-like
                 heightValue = std::pow(heightValue, 1.5f);
@@ -203,7 +439,138 @@ public:
         return grid;
     }
     
+    // Generate specific noise type directly
+    Grid2 generateSpecificNoise(NoiseType type, int width, int height,
+                               float scale = 1.0f, int octaves = 1,
+                               float persistence = 0.5f, uint32_t seed = 0) {
+        NoiseType oldType = currentType;
+        currentType = type;
+        
+        auto grid = generateGrayNoise(width, height, scale, octaves, persistence, seed);
+        
+        currentType = oldType;
+        return grid;
+    }
+
 private:
+    // Initialize permutation table for Simplex noise
+    void initializePermutationTable(uint32_t seed) {
+        std::mt19937 localRng(seed);
+        std::uniform_int_distribution<int> intDist(0, 255);
+        
+        // Create initial permutation
+        std::array<int, 256> p;
+        for (int i = 0; i < 256; i++) {
+            p[i] = i;
+        }
+        
+        // Shuffle using Fisher-Yates
+        for (int i = 255; i > 0; i--) {
+            int j = intDist(localRng) % (i + 1);
+            std::swap(p[i], p[j]);
+        }
+        
+        // Duplicate for overflow
+        for (int i = 0; i < 512; i++) {
+            perm[i] = p[i & 255];
+        }
+    }
+    
+    // Initialize feature points for Worley/Poisson noise
+    void initializeFeaturePoints(int numPoints, uint32_t seed) {
+        std::mt19937 localRng(seed);
+        std::uniform_real_distribution<float> localDist(0.0f, 1.0f);
+        
+        featurePoints.clear();
+        featurePoints.reserve(numPoints);
+        
+        for (int i = 0; i < numPoints; i++) {
+            featurePoints.emplace_back(localDist(localRng), localDist(localRng));
+        }
+    }
+    
+    // Initialize wavelet coefficients
+    void initializeWaveletCoefficients(int size, uint32_t seed) {
+        std::mt19937 localRng(seed);
+        std::uniform_real_distribution<float> localDist(-1.0f, 1.0f);
+        
+        waveletCoefficients.resize(size * size);
+        for (int i = 0; i < size * size; i++) {
+            waveletCoefficients[i] = (localDist(localRng) + 1.0f) * 0.5f; // Normalize to [0,1]
+        }
+    }
+    
+    // Raw Simplex noise implementation
+    float rawSimplexNoise(float x, float y) {
+        // Skewing factors for 2D
+        const float F2 = 0.5f * (std::sqrt(3.0f) - 1.0f);
+        const float G2 = (3.0f - std::sqrt(3.0f)) / 6.0f;
+        
+        // Skew the input space
+        float s = (x + y) * F2;
+        int i = fastFloor(x + s);
+        int j = fastFloor(y + s);
+        
+        float t = (i + j) * G2;
+        float X0 = i - t;
+        float Y0 = j - t;
+        float x0 = x - X0;
+        float y0 = y - Y0;
+        
+        // Determine which simplex we're in
+        int i1, j1;
+        if (x0 > y0) {
+            i1 = 1; j1 = 0;
+        } else {
+            i1 = 0; j1 = 1;
+        }
+        
+        // Calculate other corners
+        float x1 = x0 - i1 + G2;
+        float y1 = y0 - j1 + G2;
+        float x2 = x0 - 1.0f + 2.0f * G2;
+        float y2 = y0 - 1.0f + 2.0f * G2;
+        
+        // Calculate contributions from each corner
+        float n0, n1, n2;
+        float t0 = 0.5f - x0*x0 - y0*y0;
+        if (t0 < 0) n0 = 0.0f;
+        else {
+            t0 *= t0;
+            n0 = t0 * t0 * grad(perm[i + perm[j]], x0, y0);
+        }
+        
+        float t1 = 0.5f - x1*x1 - y1*y1;
+        if (t1 < 0) n1 = 0.0f;
+        else {
+            t1 *= t1;
+            n1 = t1 * t1 * grad(perm[i + i1 + perm[j + j1]], x1, y1);
+        }
+        
+        float t2 = 0.5f - x2*x2 - y2*y2;
+        if (t2 < 0) n2 = 0.0f;
+        else {
+            t2 *= t2;
+            n2 = t2 * t2 * grad(perm[i + 1 + perm[j + 1]], x2, y2);
+        }
+        
+        return 70.0f * (n0 + n1 + n2);
+    }
+    
+    // Fast floor function
+    int fastFloor(float x) {
+        int xi = static_cast<int>(x);
+        return x < xi ? xi - 1 : xi;
+    }
+    
+    // Gradient function for Simplex noise
+    float grad(int hash, float x, float y) {
+        int h = hash & 7;
+        float u = h < 4 ? x : y;
+        float v = h < 4 ? y : x;
+        return ((h & 1) ? -u : u) + ((h & 2) ? -2.0f * v : 2.0f * v);
+    }
+    
     // Raw noise function (simple hash-based)
     float rawNoise(float x, float y) {
         // Simple hash function for deterministic noise
@@ -211,11 +578,11 @@ private:
         int yi = static_cast<int>(std::floor(y));
         
         // Use the RNG to generate consistent noise based on grid position
-        rng.seed(xi * 1619 + yi * 31337);
+        rng.seed(xi * 1619 + yi * 31337 + currentSeed);
         return dist(rng);
     }
     
-    // Improved noise function (Perlin-like)
+    // Improved noise function (Perlin-like) using selected gradient type
     float improvedNoise(float x, float y) {
         // Integer part
         int xi = static_cast<int>(std::floor(x));
@@ -229,7 +596,7 @@ private:
         float u = fade(xf);
         float v = fade(yf);
         
-        // Gradient noise from corners
+        // Gradient noise from corners using selected gradient calculation
         float n00 = gradNoise(xi, yi, xf, yf);
         float n01 = gradNoise(xi, yi + 1, xf, yf - 1);
         float n10 = gradNoise(xi + 1, yi, xf - 1, yf);
@@ -241,6 +608,76 @@ private:
         return lerp(x1, x2, v);
     }
     
+    // Gradient noise function using selected gradient type
+    float gradNoise(int xi, int yi, float xf, float yf) {
+        switch (gradType) {
+            case HASH_BASED:
+                return hashGradNoise(xi, yi, xf, yf);
+            case SIN_BASED:
+                return sinGradNoise(xi, yi, xf, yf);
+            case DOT_BASED:
+                return dotGradNoise(xi, yi, xf, yf);
+            case PRECOMPUTED:
+            default:
+                return precomputedGradNoise(xi, yi, xf, yf);
+        }
+    }
+    
+    // Fast gradient noise function using precomputed gradient directions
+    float precomputedGradNoise(int xi, int yi, float xf, float yf) {
+        // Generate deterministic hash from integer coordinates
+        int hash = (xi * 1619 + yi * 31337 + currentSeed);
+        
+        // Use hash to select from 8 precomputed gradient directions
+        int gradIndex = hash & 7; // 8 directions (0-7)
+        
+        // Dot product between distance vector and gradient
+        return xf * grads[gradIndex].x + yf * grads[gradIndex].y;
+    }
+    
+    // Hash-based gradient noise
+    float hashGradNoise(int xi, int yi, float xf, float yf) {
+        // Generate hash from coordinates
+        uint32_t hash = (xi * 1619 + yi * 31337 + currentSeed);
+        
+        // Use hash to generate gradient angle
+        hash = (hash << 13) ^ hash;
+        hash = (hash * (hash * hash * 15731 + 789221) + 1376312589);
+        float angle = (hash & 0xFFFF) / 65535.0f * 2.0f * M_PI;
+        
+        // Gradient vector
+        float gx = std::cos(angle);
+        float gy = std::sin(angle);
+        
+        // Dot product
+        return xf * gx + yf * gy;
+    }
+    
+    // Sine-based gradient noise
+    float sinGradNoise(int xi, int yi, float xf, float yf) {
+        // Use sine of coordinates to generate gradient
+        float angle = std::sin(xi * 12.9898f + yi * 78.233f + currentSeed) * 43758.5453f;
+        angle = angle - std::floor(angle); // Fractional part
+        angle *= 2.0f * M_PI;
+        
+        float gx = std::cos(angle);
+        float gy = std::sin(angle);
+        
+        return xf * gx + yf * gy;
+    }
+    
+    // Dot product based gradient noise
+    float dotGradNoise(int xi, int yi, float xf, float yf) {
+        // Simple dot product with random vector based on coordinates
+        float random = std::sin(xi * 127.1f + yi * 311.7f) * 43758.5453123f;
+        random = random - std::floor(random);
+        
+        Vec2 grad(std::cos(random * 2.0f * M_PI), std::sin(random * 2.0f * M_PI));
+        Vec2 dist(xf, yf);
+        
+        return grad.dot(dist);
+    }
+    
     // Fade function for smooth interpolation
     float fade(float t) {
         return t * t * t * (t * (t * 6 - 15) + 10);
@@ -257,44 +694,6 @@ private:
         if (x > upperlimit) return upperlimit;
         return x;
     }
-
-    // float grad(const int& hash, const float& b, const float& c, const float& d) {
-    //     TIME_FUNCTION;
-    //     int h = hash & 15;
-    //     float u = (h < 8) ? c : b;
-    //     float v = (h < 4) ? b : ((h == 12 || h == 14) ? c : d);
-    //     return (((h & 1) == 0) ? u : -u) + (((h & 2) == 0) ? v : -v);
-    // }
-    float gradNoise(int xi, int yi, float xf, float yf) {
-        // Generate deterministic "random" unit vector using hash
-        int hash = (xi * 1619 + yi * 31337);
-        
-        // Use hash to generate angle in fixed steps (faster than trig)
-        float angle = (hash & 255) * (2.0f * 3.14159265f / 256.0f);
-        
-        // Or even faster: use gradient table with 8 or 16 precomputed directions
-        int gradIndex = hash & 7; // 8 directions
-        static constexpr std::array<Grad, 8> grads = {
-            {1,0}, {0.707f,0.707f}, {0,1}, {-0.707f,0.707f},
-            {-1,0}, {-0.707f,-0.707f}, {0,-1}, {0.707f,-0.707f}
-        };
-        
-        return xf * grads[gradIndex].x + yf * grads[gradIndex].y;
-    }
-
-    // Gradient noise function
-    float slowGradNoise(int xi, int yi, float xf, float yf) {
-        // Generate consistent random gradient from integer coordinates
-        rng.seed(xi * 1619 + yi * 31337);
-        float angle = dist(rng) * 2.0f * 3.14159265f;
-        
-        // Gradient vector
-        float gx = std::cos(angle);
-        float gy = std::sin(angle);
-        
-        // Dot product
-        return xf * gx + yf * gy;
-    }
 };
 
 #endif