Implement distance sampler

common/addons/config-noise-function/src/main/java/com/dfsek/terra/addons/noise/NoiseAddon.java

@@ -24,6 +24,7 @@ import com.dfsek.terra.addons.noise.config.templates.LinearHeightmapSamplerTempl
 import com.dfsek.terra.addons.noise.config.templates.TranslateSamplerTemplate;
 import com.dfsek.terra.addons.noise.config.templates.noise.CellularNoiseTemplate;
 import com.dfsek.terra.addons.noise.config.templates.noise.ConstantNoiseTemplate;
+import com.dfsek.terra.addons.noise.config.templates.noise.DistanceSamplerTemplate;
 import com.dfsek.terra.addons.noise.config.templates.noise.ExpressionFunctionTemplate;
 import com.dfsek.terra.addons.noise.config.templates.noise.GaborNoiseTemplate;
 import com.dfsek.terra.addons.noise.config.templates.noise.SimpleNoiseTemplate;
@@ -44,6 +45,7 @@ import com.dfsek.terra.addons.noise.samplers.arithmetic.MinSampler;
 import com.dfsek.terra.addons.noise.samplers.arithmetic.MultiplicationSampler;
 import com.dfsek.terra.addons.noise.samplers.arithmetic.SubtractionSampler;
 import com.dfsek.terra.addons.noise.samplers.noise.CellularSampler;
+import com.dfsek.terra.addons.noise.samplers.noise.DistanceSampler;
 import com.dfsek.terra.addons.noise.samplers.noise.random.GaussianNoiseSampler;
 import com.dfsek.terra.addons.noise.samplers.noise.random.PositiveWhiteNoiseSampler;
 import com.dfsek.terra.addons.noise.samplers.noise.random.WhiteNoiseSampler;
@@ -85,6 +87,8 @@ public class NoiseAddon implements AddonInitializer {
                                     (type, o, loader, depthTracker) -> CellularSampler.DistanceFunction.valueOf((String) o))
                        .applyLoader(CellularSampler.ReturnType.class,
                                     (type, o, loader, depthTracker) -> CellularSampler.ReturnType.valueOf((String) o))
+                       .applyLoader(DistanceSampler.DistanceFunction.class,
+                                    (type, o, loader, depthTracker) -> DistanceSampler.DistanceFunction.valueOf((String) o))
                        .applyLoader(DimensionApplicableNoiseSampler.class, DimensionApplicableNoiseSampler::new)
                        .applyLoader(FunctionTemplate.class, FunctionTemplate::new);
             

common/addons/config-noise-function/src/main/java/com/dfsek/terra/addons/noise/config/templates/noise/DistanceSamplerTemplate.java

@@ -0,0 +1,42 @@
+package com.dfsek.terra.addons.noise.config.templates.noise;
+
+import com.dfsek.tectonic.api.config.template.annotations.Default;
+import com.dfsek.tectonic.api.config.template.annotations.Value;
+
+import com.dfsek.terra.addons.noise.config.templates.SamplerTemplate;
+import com.dfsek.terra.addons.noise.samplers.noise.DistanceSampler;
+import com.dfsek.terra.addons.noise.samplers.noise.DistanceSampler.DistanceFunction;
+import com.dfsek.terra.api.config.meta.Meta;
+
+
+public class DistanceSamplerTemplate extends SamplerTemplate<DistanceSampler> {
+    
+    @Value("distance-function")
+    @Default
+    private DistanceSampler.@Meta DistanceFunction distanceFunction = DistanceFunction.Euclidean;
+    
+    @Value("point.x")
+    @Default
+    private @Meta double x = 0;
+    
+    @Value("point.y")
+    @Default
+    private @Meta double y = 0;
+    
+    @Value("point.z")
+    @Default
+    private @Meta double z = 0;
+    
+    @Value("normalize")
+    @Default
+    private @Meta boolean normalize = false;
+    
+    @Value("radius")
+    @Default
+    private @Meta double normalizeRadius = 100;
+    
+    @Override
+    public DistanceSampler get() {
+        return new DistanceSampler(distanceFunction, x, y, z, normalize, normalizeRadius);
+    }
+}

common/addons/config-noise-function/src/main/java/com/dfsek/terra/addons/noise/samplers/noise/DistanceSampler.java

@@ -0,0 +1,66 @@
+package com.dfsek.terra.addons.noise.samplers.noise;
+
+
+public class DistanceSampler extends NoiseFunction {
+    
+    private final DistanceFunction distanceFunction;
+    private final double ox, oy, oz;
+    private final boolean normalize;
+    private final double radius;
+    
+    private final double distanceAtRadius;
+    
+    public DistanceSampler(DistanceFunction distanceFunction, double ox, double oy, double oz, boolean normalize, double radius) {
+        frequency = 1;
+        this.distanceFunction = distanceFunction;
+        this.ox = ox;
+        this.oy = oy;
+        this.oz = oz;
+        this.normalize = normalize;
+        this.radius = radius;
+        this.distanceAtRadius = distance2d(distanceFunction, radius, 0); // distance2d and distance3d should return the same value
+    }
+   
+    @Override
+    public double getNoiseRaw(long seed, double x, double y) {
+        double dx = x - ox;
+        double dy = y - oz;
+        if (normalize && (fastAbs(dx) > radius || fastAbs(dy) > radius)) return 1;
+        double dist = distance2d(distanceFunction, dx, dy);
+        if (normalize) return fastMin(((2*dist)/distanceAtRadius)-1, 1);
+        return dist;
+    }
+    
+    @Override
+    public double getNoiseRaw(long seed, double x, double y, double z) {
+        double dx = x - ox;
+        double dy = y - oy;
+        double dz = z - oz;
+        if(normalize && (fastAbs(dx) > radius || fastAbs(dy) > radius || fastAbs(dz) > radius)) return 1;
+        double dist = distance3d(distanceFunction, dx, dy, dz);
+        if (normalize) return fastMin(((2*dist)/distanceAtRadius)-1, 1);
+        return dist;
+    }
+    
+    private static double distance2d(DistanceFunction distanceFunction, double x, double z) {
+        return switch(distanceFunction) {
+            case Euclidean -> fastSqrt(x*x + z*z);
+            case EuclideanSq -> x*x + z*z;
+            case Manhattan -> fastAbs(x) + fastAbs(z);
+        };
+    }
+    
+    private static double distance3d(DistanceFunction distanceFunction, double x, double y, double z) {
+        return switch(distanceFunction) {
+            case Euclidean -> fastSqrt(x*x + y*y + z*z);
+            case EuclideanSq -> x*x + y*y + z*z;
+            case Manhattan -> fastAbs(x) + fastAbs(y) + fastAbs(z);
+        };
+    }
+    
+    public enum DistanceFunction {
+        Euclidean,
+        EuclideanSq,
+        Manhattan
+    }
+}