Change Java whitespace handling in .editorconfig (#425)

* Change whitespace handling in .editorconfig

* Reformat code

* fix format error

* Reformat code

---------

Co-authored-by: Zoë Gidiere <duplexsys@protonmail.com>

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

@@ -73,90 +73,90 @@ public class NoiseAddon implements AddonInitializer {
     };
     @Inject
     private Platform plugin;
-    
+
     @Inject
     private BaseAddon addon;
-    
+
     @Override
     public void initialize() {
         plugin.getEventManager()
-              .getHandler(FunctionalEventHandler.class)
-              .register(addon, ConfigPackPreLoadEvent.class)
-              .then(event -> {
-                  CheckedRegistry<Supplier<ObjectTemplate<NoiseSampler>>> noiseRegistry = event.getPack().getOrCreateRegistry(
-                          NOISE_SAMPLER_TOKEN);
-                  event.getPack()
-                       .applyLoader(CellularSampler.DistanceFunction.class,
-                                    (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)
-                       .applyLoader(CubicSpline.Point.class, CubicSplinePointTemplate::new);
-                  
-                  noiseRegistry.register(addon.key("LINEAR"), LinearNormalizerTemplate::new);
-                  noiseRegistry.register(addon.key("NORMAL"), NormalNormalizerTemplate::new);
-                  noiseRegistry.register(addon.key("CLAMP"), ClampNormalizerTemplate::new);
-                  noiseRegistry.register(addon.key("PROBABILITY"), ProbabilityNormalizerTemplate::new);
-                  noiseRegistry.register(addon.key("SCALE"), ScaleNormalizerTemplate::new);
-                  noiseRegistry.register(addon.key("POSTERIZATION"), PosterizationNormalizerTemplate::new);
-                  noiseRegistry.register(addon.key("CUBIC_SPLINE"), CubicSplineNormalizerTemplate::new);
-                  
-                  noiseRegistry.register(addon.key("IMAGE"), ImageSamplerTemplate::new);
-                  
-                  noiseRegistry.register(addon.key("DOMAIN_WARP"), DomainWarpTemplate::new);
-                  
-                  noiseRegistry.register(addon.key("FBM"), BrownianMotionTemplate::new);
-                  noiseRegistry.register(addon.key("PING_PONG"), PingPongTemplate::new);
-                  noiseRegistry.register(addon.key("RIDGED"), RidgedFractalTemplate::new);
-                  
-                  noiseRegistry.register(addon.key("OPEN_SIMPLEX_2"), () -> new SimpleNoiseTemplate(OpenSimplex2Sampler::new));
-                  noiseRegistry.register(addon.key("OPEN_SIMPLEX_2S"), () -> new SimpleNoiseTemplate(OpenSimplex2SSampler::new));
-                  noiseRegistry.register(addon.key("PERLIN"), () -> new SimpleNoiseTemplate(PerlinSampler::new));
-                  noiseRegistry.register(addon.key("SIMPLEX"), () -> new SimpleNoiseTemplate(SimplexSampler::new));
-                  noiseRegistry.register(addon.key("GABOR"), GaborNoiseTemplate::new);
-                  
-                  
-                  noiseRegistry.register(addon.key("VALUE"), () -> new SimpleNoiseTemplate(ValueSampler::new));
-                  noiseRegistry.register(addon.key("VALUE_CUBIC"), () -> new SimpleNoiseTemplate(ValueCubicSampler::new));
-                  
-                  noiseRegistry.register(addon.key("CELLULAR"), CellularNoiseTemplate::new);
-                  
-                  noiseRegistry.register(addon.key("WHITE_NOISE"), () -> new SimpleNoiseTemplate(WhiteNoiseSampler::new));
-                  noiseRegistry.register(addon.key("POSITIVE_WHITE_NOISE"), () -> new SimpleNoiseTemplate(PositiveWhiteNoiseSampler::new));
-                  noiseRegistry.register(addon.key("GAUSSIAN"), () -> new SimpleNoiseTemplate(GaussianNoiseSampler::new));
-                  
-                  noiseRegistry.register(addon.key("DISTANCE"), DistanceSamplerTemplate::new);
-                  
-                  noiseRegistry.register(addon.key("CONSTANT"), ConstantNoiseTemplate::new);
-                  
-                  noiseRegistry.register(addon.key("KERNEL"), KernelTemplate::new);
-                  
-                  noiseRegistry.register(addon.key("LINEAR_HEIGHTMAP"), LinearHeightmapSamplerTemplate::new);
-                  noiseRegistry.register(addon.key("TRANSLATE"), TranslateSamplerTemplate::new);
-                  
-                  noiseRegistry.register(addon.key("ADD"), () -> new BinaryArithmeticTemplate<>(AdditionSampler::new));
-                  noiseRegistry.register(addon.key("SUB"), () -> new BinaryArithmeticTemplate<>(SubtractionSampler::new));
-                  noiseRegistry.register(addon.key("MUL"), () -> new BinaryArithmeticTemplate<>(MultiplicationSampler::new));
-                  noiseRegistry.register(addon.key("DIV"), () -> new BinaryArithmeticTemplate<>(DivisionSampler::new));
-                  noiseRegistry.register(addon.key("MAX"), () -> new BinaryArithmeticTemplate<>(MaxSampler::new));
-                  noiseRegistry.register(addon.key("MIN"), () -> new BinaryArithmeticTemplate<>(MinSampler::new));
-                  
-                  
-                  Map<String, DimensionApplicableNoiseSampler> packSamplers = new LinkedHashMap<>();
-                  Map<String, FunctionTemplate> packFunctions = new LinkedHashMap<>();
-                  noiseRegistry.register(addon.key("EXPRESSION"), () -> new ExpressionFunctionTemplate(packSamplers, packFunctions));
-                  noiseRegistry.register(addon.key("EXPRESSION_NORMALIZER"),
-                                         () -> new ExpressionNormalizerTemplate(packSamplers, packFunctions));
-                  
-                  NoiseConfigPackTemplate template = event.loadTemplate(new NoiseConfigPackTemplate());
-                  packSamplers.putAll(template.getSamplers());
-                  packFunctions.putAll(template.getFunctions());
-                  event.getPack().getContext().put(template);
-              })
-              .priority(50)
-              .failThrough();
+            .getHandler(FunctionalEventHandler.class)
+            .register(addon, ConfigPackPreLoadEvent.class)
+            .then(event -> {
+                CheckedRegistry<Supplier<ObjectTemplate<NoiseSampler>>> noiseRegistry = event.getPack().getOrCreateRegistry(
+                    NOISE_SAMPLER_TOKEN);
+                event.getPack()
+                    .applyLoader(CellularSampler.DistanceFunction.class,
+                        (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)
+                    .applyLoader(CubicSpline.Point.class, CubicSplinePointTemplate::new);
+
+                noiseRegistry.register(addon.key("LINEAR"), LinearNormalizerTemplate::new);
+                noiseRegistry.register(addon.key("NORMAL"), NormalNormalizerTemplate::new);
+                noiseRegistry.register(addon.key("CLAMP"), ClampNormalizerTemplate::new);
+                noiseRegistry.register(addon.key("PROBABILITY"), ProbabilityNormalizerTemplate::new);
+                noiseRegistry.register(addon.key("SCALE"), ScaleNormalizerTemplate::new);
+                noiseRegistry.register(addon.key("POSTERIZATION"), PosterizationNormalizerTemplate::new);
+                noiseRegistry.register(addon.key("CUBIC_SPLINE"), CubicSplineNormalizerTemplate::new);
+
+                noiseRegistry.register(addon.key("IMAGE"), ImageSamplerTemplate::new);
+
+                noiseRegistry.register(addon.key("DOMAIN_WARP"), DomainWarpTemplate::new);
+
+                noiseRegistry.register(addon.key("FBM"), BrownianMotionTemplate::new);
+                noiseRegistry.register(addon.key("PING_PONG"), PingPongTemplate::new);
+                noiseRegistry.register(addon.key("RIDGED"), RidgedFractalTemplate::new);
+
+                noiseRegistry.register(addon.key("OPEN_SIMPLEX_2"), () -> new SimpleNoiseTemplate(OpenSimplex2Sampler::new));
+                noiseRegistry.register(addon.key("OPEN_SIMPLEX_2S"), () -> new SimpleNoiseTemplate(OpenSimplex2SSampler::new));
+                noiseRegistry.register(addon.key("PERLIN"), () -> new SimpleNoiseTemplate(PerlinSampler::new));
+                noiseRegistry.register(addon.key("SIMPLEX"), () -> new SimpleNoiseTemplate(SimplexSampler::new));
+                noiseRegistry.register(addon.key("GABOR"), GaborNoiseTemplate::new);
+
+
+                noiseRegistry.register(addon.key("VALUE"), () -> new SimpleNoiseTemplate(ValueSampler::new));
+                noiseRegistry.register(addon.key("VALUE_CUBIC"), () -> new SimpleNoiseTemplate(ValueCubicSampler::new));
+
+                noiseRegistry.register(addon.key("CELLULAR"), CellularNoiseTemplate::new);
+
+                noiseRegistry.register(addon.key("WHITE_NOISE"), () -> new SimpleNoiseTemplate(WhiteNoiseSampler::new));
+                noiseRegistry.register(addon.key("POSITIVE_WHITE_NOISE"), () -> new SimpleNoiseTemplate(PositiveWhiteNoiseSampler::new));
+                noiseRegistry.register(addon.key("GAUSSIAN"), () -> new SimpleNoiseTemplate(GaussianNoiseSampler::new));
+
+                noiseRegistry.register(addon.key("DISTANCE"), DistanceSamplerTemplate::new);
+
+                noiseRegistry.register(addon.key("CONSTANT"), ConstantNoiseTemplate::new);
+
+                noiseRegistry.register(addon.key("KERNEL"), KernelTemplate::new);
+
+                noiseRegistry.register(addon.key("LINEAR_HEIGHTMAP"), LinearHeightmapSamplerTemplate::new);
+                noiseRegistry.register(addon.key("TRANSLATE"), TranslateSamplerTemplate::new);
+
+                noiseRegistry.register(addon.key("ADD"), () -> new BinaryArithmeticTemplate<>(AdditionSampler::new));
+                noiseRegistry.register(addon.key("SUB"), () -> new BinaryArithmeticTemplate<>(SubtractionSampler::new));
+                noiseRegistry.register(addon.key("MUL"), () -> new BinaryArithmeticTemplate<>(MultiplicationSampler::new));
+                noiseRegistry.register(addon.key("DIV"), () -> new BinaryArithmeticTemplate<>(DivisionSampler::new));
+                noiseRegistry.register(addon.key("MAX"), () -> new BinaryArithmeticTemplate<>(MaxSampler::new));
+                noiseRegistry.register(addon.key("MIN"), () -> new BinaryArithmeticTemplate<>(MinSampler::new));
+
+
+                Map<String, DimensionApplicableNoiseSampler> packSamplers = new LinkedHashMap<>();
+                Map<String, FunctionTemplate> packFunctions = new LinkedHashMap<>();
+                noiseRegistry.register(addon.key("EXPRESSION"), () -> new ExpressionFunctionTemplate(packSamplers, packFunctions));
+                noiseRegistry.register(addon.key("EXPRESSION_NORMALIZER"),
+                    () -> new ExpressionNormalizerTemplate(packSamplers, packFunctions));
+
+                NoiseConfigPackTemplate template = event.loadTemplate(new NoiseConfigPackTemplate());
+                packSamplers.putAll(template.getSamplers());
+                packFunctions.putAll(template.getFunctions());
+                event.getPack().getContext().put(template);
+            })
+            .priority(50)
+            .failThrough();
     }
 }

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

@@ -25,15 +25,15 @@ public class NoiseConfigPackTemplate implements ConfigTemplate, Properties {
     @Value("samplers")
     @Default
     private @Meta Map<String, @Meta DimensionApplicableNoiseSampler> noiseBuilderMap = new LinkedHashMap<>();
-    
+
     @Value("functions")
     @Default
     private @Meta LinkedHashMap<String, @Meta FunctionTemplate> expressions = new LinkedHashMap<>();
-    
+
     public Map<String, DimensionApplicableNoiseSampler> getSamplers() {
         return noiseBuilderMap;
     }
-    
+
     public LinkedHashMap<String, FunctionTemplate> getFunctions() {
         return expressions;
     }

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

@@ -8,16 +8,16 @@ import com.dfsek.terra.api.config.meta.Meta;
 
 
 public class CubicSplinePointTemplate implements ObjectTemplate<Point> {
-    
+
     @Value("from")
     private @Meta double from;
-    
+
     @Value("to")
     private @Meta double to;
-    
+
     @Value("gradient")
     private @Meta double gradient;
-    
+
     @Override
     public Point get() {
         return new Point(from, to, gradient);

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

@@ -17,19 +17,19 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public class DimensionApplicableNoiseSampler implements ObjectTemplate<DimensionApplicableNoiseSampler> {
     @Value("dimensions")
     private @Meta int dimensions;
-    
+
     @Value(".")
     private @Meta NoiseSampler sampler;
-    
+
     @Override
     public DimensionApplicableNoiseSampler get() {
         return this;
     }
-    
+
     public int getDimensions() {
         return dimensions;
     }
-    
+
     public NoiseSampler getSampler() {
         return sampler;
     }

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

@@ -15,11 +15,11 @@ public class BinaryArithmeticTemplate<T extends BinaryArithmeticSampler> extends
     private @Meta NoiseSampler left;
     @Value("right")
     private @Meta NoiseSampler right;
-    
+
     public BinaryArithmeticTemplate(BiFunction<NoiseSampler, NoiseSampler, T> function) {
         this.function = function;
     }
-    
+
     @Override
     public T get() {
         return function.apply(left, right);

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

@@ -19,14 +19,14 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public class DomainWarpTemplate extends SamplerTemplate<DomainWarpedSampler> {
     @Value("warp")
     private @Meta NoiseSampler warp;
-    
+
     @Value("sampler")
     private @Meta NoiseSampler function;
-    
+
     @Value("amplitude")
     @Default
     private @Meta double amplitude = 1;
-    
+
     @Override
     public NoiseSampler get() {
         return new DomainWarpedSampler(function, warp, amplitude);

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

@@ -22,31 +22,31 @@ import com.dfsek.terra.api.config.meta.Meta;
 public class FunctionTemplate implements ObjectTemplate<FunctionTemplate> {
     @Value("arguments")
     private List<String> args;
-    
+
     @Value("expression")
     private @Meta String function;
-    
+
     @Value("functions")
     @Default
     private @Meta LinkedHashMap<String, @Meta FunctionTemplate> functions = new LinkedHashMap<>();
-    
+
     @Override
     public FunctionTemplate get() {
         return this;
     }
-    
+
     public List<String> getArgs() {
         return args;
     }
-    
+
     public String getFunction() {
         return function;
     }
-    
+
     public LinkedHashMap<String, FunctionTemplate> getFunctions() {
         return functions;
     }
-    
+
     @Override
     public boolean equals(Object o) {
         if(this == o) return true;
@@ -54,7 +54,7 @@ public class FunctionTemplate implements ObjectTemplate<FunctionTemplate> {
         FunctionTemplate that = (FunctionTemplate) o;
         return args.equals(that.args) && function.equals(that.function) && functions.equals(that.functions);
     }
-    
+
     @Override
     public int hashCode() {
         return Objects.hash(args, function, functions);

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

@@ -20,20 +20,20 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 @SuppressWarnings({ "unused", "FieldMayBeFinal" })
 public class ImageSamplerTemplate extends SamplerTemplate<ImageSampler> {
-    
+
     private static final Logger logger = LoggerFactory.getLogger(ImageSamplerTemplate.class);
-    
+
     private static boolean used = false;
-    
+
     @Value("image")
     private @Meta BufferedImage image;
-    
+
     @Value("frequency")
     private @Meta double frequency;
-    
+
     @Value("channel")
     private ImageSampler.@Meta Channel channel;
-    
+
     @Override
     public NoiseSampler get() {
         if(!used) {

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

@@ -20,50 +20,50 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 @SuppressWarnings({ "unused", "FieldMayBeFinal" })
 public class KernelTemplate extends SamplerTemplate<KernelSampler> {
-    
+
     @Value("kernel")
     private @Meta List<@Meta List<@Meta Double>> kernel;
-    
+
     @Value("factor")
     @Default
     private @Meta double factor = 1;
-    
+
     @Value("sampler")
     private @Meta NoiseSampler function;
-    
+
     @Value("frequency")
     @Default
     private @Meta double frequency = 1;
-    
+
     @Override
     public NoiseSampler get() {
         double[][] k = new double[kernel.size()][kernel.get(0).size()];
-        
+
         for(int x = 0; x < kernel.size(); x++) {
             for(int y = 0; y < kernel.get(x).size(); y++) {
                 k[x][y] = kernel.get(x).get(y) * factor;
             }
         }
-        
+
         KernelSampler sampler = new KernelSampler(k, function);
         sampler.setFrequency(frequency);
         return sampler;
     }
-    
+
     @Override
     public boolean validate() throws ValidationException {
-        
+
         if(kernel.isEmpty()) throw new ValidationException("Kernel must not be empty.");
-        
+
         int len = kernel.get(0).size();
-        
+
         if(len == 0) throw new ValidationException("Kernel row must contain data.");
-        
+
         for(int i = 0; i < kernel.size(); i++) {
             if(kernel.get(i).size() != len)
                 throw new ValidationException("Kernel row " + i + " size mismatch. Expected " + len + ", found " + kernel.get(i).size());
         }
-        
+
         return super.validate();
     }
 }

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

@@ -13,14 +13,14 @@ public class LinearHeightmapSamplerTemplate extends SamplerTemplate<LinearHeight
     @Value("sampler")
     @Default
     private @Meta NoiseSampler sampler = NoiseSampler.zero();
-    
+
     @Value("base")
     private @Meta double base;
-    
+
     @Value("scale")
     @Default
     private @Meta double scale = 1;
-    
+
     @Override
     public NoiseSampler get() {
         return new LinearHeightmapSampler(sampler, scale, base);

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

@@ -22,13 +22,13 @@ public abstract class SamplerTemplate<T extends NoiseSampler> implements Validat
     @Value("dimensions")
     @Default
     private @Meta int dimensions = 2;
-    
+
     @Override
     public boolean validate() throws ValidationException {
         if(dimensions != 2 && dimensions != 3) throw new ValidationException("Illegal amount of dimensions: " + dimensions);
         return true;
     }
-    
+
     public int getDimensions() {
         return dimensions;
     }

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

@@ -9,22 +9,22 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 
 public class TranslateSamplerTemplate extends SamplerTemplate<TranslateSampler> {
-    
+
     @Value("sampler")
     private NoiseSampler sampler;
-    
+
     @Value("x")
     @Default
     private @Meta double x = 0;
-    
+
     @Value("y")
     @Default
     private @Meta double y = 0;
-    
+
     @Value("z")
     @Default
     private @Meta double z = 0;
-    
+
     @Override
     public NoiseSampler get() {
         return new TranslateSampler(sampler, x, y, z);

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

@@ -21,20 +21,20 @@ public class CellularNoiseTemplate extends NoiseTemplate<CellularSampler> {
     @Value("distance")
     @Default
     private CellularSampler.@Meta DistanceFunction cellularDistanceFunction = CellularSampler.DistanceFunction.EuclideanSq;
-    
+
     @Value("return")
     @Default
     private CellularSampler.@Meta ReturnType cellularReturnType = CellularSampler.ReturnType.Distance;
-    
+
     @Value("jitter")
     @Default
     private @Meta double cellularJitter = 1.0D;
-    
-    
+
+
     @Value("lookup")
     @Default
     private @Meta NoiseSampler lookup = new OpenSimplex2Sampler();
-    
+
     @Override
     public NoiseSampler get() {
         CellularSampler sampler = new CellularSampler();

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

@@ -21,7 +21,7 @@ public class ConstantNoiseTemplate extends SamplerTemplate<ConstantSampler> {
     @Value("value")
     @Default
     private @Meta double value = 0d;
-    
+
     @Override
     public NoiseSampler get() {
         return new ConstantSampler(value);

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

@@ -10,31 +10,31 @@ 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/config/templates/noise/ExpressionFunctionTemplate.java

@@ -40,13 +40,13 @@ public class ExpressionFunctionTemplate extends SamplerTemplate<ExpressionFuncti
     @Value("functions")
     @Default
     private @Meta LinkedHashMap<String, @Meta FunctionTemplate> functions = new LinkedHashMap<>();
-    
+
     public ExpressionFunctionTemplate(Map<String, DimensionApplicableNoiseSampler> globalSamplers,
                                       Map<String, FunctionTemplate> globalFunctions) {
         this.globalSamplers = globalSamplers;
         this.globalFunctions = globalFunctions;
     }
-    
+
     @Override
     public NoiseSampler get() {
         var mergedFunctions = new HashMap<>(globalFunctions);

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

@@ -20,23 +20,23 @@ public class GaborNoiseTemplate extends NoiseTemplate<GaborNoiseSampler> {
     @Value("rotation")
     @Default
     private @Meta double rotation = 0.25;
-    
+
     @Value("isotropic")
     @Default
     private @Meta boolean isotropic = true;
-    
+
     @Value("deviation")
     @Default
     private @Meta double deviation = 1.0;
-    
+
     @Value("impulses")
     @Default
     private @Meta double impulses = 64d;
-    
+
     @Value("frequency_0")
     @Default
     private @Meta double f0 = 0.625;
-    
+
     @Override
     public NoiseSampler get() {
         GaborNoiseSampler gaborNoiseSampler = new GaborNoiseSampler();

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

@@ -20,7 +20,7 @@ public abstract class NoiseTemplate<T extends NoiseFunction> extends SamplerTemp
     @Value("frequency")
     @Default
     protected @Meta double frequency = 0.02d;
-    
+
     @Value("salt")
     @Default
     protected @Meta long salt = 0;

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

@@ -15,11 +15,11 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 public class SimpleNoiseTemplate extends NoiseTemplate<NoiseFunction> {
     private final Supplier<NoiseFunction> samplerSupplier;
-    
+
     public SimpleNoiseTemplate(Supplier<NoiseFunction> samplerSupplier) {
         this.samplerSupplier = samplerSupplier;
     }
-    
+
     @Override
     public NoiseSampler get() {
         NoiseFunction sampler = samplerSupplier.get();

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

@@ -20,19 +20,19 @@ public abstract class FractalTemplate<T extends FractalNoiseFunction> extends Sa
     @Value("octaves")
     @Default
     protected @Meta int octaves = 3;
-    
+
     @Value("gain")
     @Default
     protected @Meta double fractalGain = 0.5D;
-    
+
     @Value("lacunarity")
     @Default
     protected @Meta double fractalLacunarity = 2.0D;
-    
+
     @Value("weighted-strength")
     @Default
     protected @Meta double weightedStrength = 0.0D;
-    
+
     @Value("sampler")
     protected @Meta NoiseSampler function;
 }

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

@@ -20,7 +20,7 @@ public class PingPongTemplate extends FractalTemplate<PingPongSampler> {
     @Value("ping-pong")
     @Default
     private @Meta double pingPong = 2.0D;
-    
+
     @Override
     public NoiseSampler get() {
         PingPongSampler sampler = new PingPongSampler(function);

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

@@ -18,10 +18,10 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public class ClampNormalizerTemplate extends NormalizerTemplate<ClampNormalizer> {
     @Value("max")
     private @Meta double max;
-    
+
     @Value("min")
     private @Meta double min;
-    
+
     @Override
     public NoiseSampler get() {
         return new ClampNormalizer(function, min, max);

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

@@ -12,10 +12,10 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 
 public class CubicSplineNormalizerTemplate extends NormalizerTemplate<CubicSplineNoiseSampler> {
-    
+
     @Value("points")
     private @Meta List<@Meta Point> points;
-    
+
     @Override
     public NoiseSampler get() {
         return new CubicSplineNoiseSampler(function, new CubicSpline(points));

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

@@ -26,31 +26,31 @@ import static com.dfsek.terra.addons.noise.paralithic.FunctionUtil.convertFuncti
 
 @SuppressWarnings({ "unused", "FieldMayBeFinal" })
 public class ExpressionNormalizerTemplate extends NormalizerTemplate<ExpressionNormalizer> {
-    
+
     private final Map<String, DimensionApplicableNoiseSampler> globalSamplers;
     private final Map<String, FunctionTemplate> globalFunctions;
-    
+
     @Value("expression")
     private @Meta String expression;
-    
+
     @Value("variables")
     @Default
     private @Meta Map<String, @Meta Double> vars = new HashMap<>();
-    
+
     @Value("samplers")
     @Default
     private @Meta LinkedHashMap<String, @Meta DimensionApplicableNoiseSampler> samplers = new LinkedHashMap<>();
-    
+
     @Value("functions")
     @Default
     private @Meta LinkedHashMap<String, @Meta FunctionTemplate> functions = new LinkedHashMap<>();
-    
+
     public ExpressionNormalizerTemplate(Map<String, DimensionApplicableNoiseSampler> globalSamplers,
                                         Map<String, FunctionTemplate> globalFunctions) {
         this.globalSamplers = globalSamplers;
         this.globalFunctions = globalFunctions;
     }
-    
+
     @Override
     public NoiseSampler get() {
         var mergedFunctions = new HashMap<>(globalFunctions);

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

@@ -18,10 +18,10 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public class LinearNormalizerTemplate extends NormalizerTemplate<LinearNormalizer> {
     @Value("max")
     private @Meta double max;
-    
+
     @Value("min")
     private @Meta double min;
-    
+
     @Override
     public NoiseSampler get() {
         return new LinearNormalizer(function, min, max);

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

@@ -19,14 +19,14 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public class NormalNormalizerTemplate extends NormalizerTemplate<NormalNormalizer> {
     @Value("mean")
     private @Meta double mean;
-    
+
     @Value("standard-deviation")
     private @Meta double stdDev;
-    
+
     @Value("groups")
     @Default
     private @Meta int groups = 16384;
-    
+
     @Override
     public NoiseSampler get() {
         return new NormalNormalizer(function, groups, mean, stdDev);

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

@@ -18,7 +18,7 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public class PosterizationNormalizerTemplate extends NormalizerTemplate<PosterizationNormalizer> {
     @Value("steps")
     private @Meta int steps;
-    
+
     @Override
     public NoiseSampler get() {
         return new PosterizationNormalizer(function, steps);

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

@@ -10,7 +10,7 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public class ScaleNormalizerTemplate extends NormalizerTemplate<ScaleNormalizer> {
     @Value("amplitude")
     private @Meta double amplitude;
-    
+
     @Override
     public NoiseSampler get() {
         return new ScaleNormalizer(function, amplitude);

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

@@ -9,30 +9,30 @@ import static com.dfsek.terra.api.util.MathUtil.lerp;
 
 
 public class CubicSpline {
-    
+
     private final double[] fromValues;
     private final double[] toValues;
     private final double[] gradients;
-    
+
     public CubicSpline(List<Point> points) {
         Collections.sort(points);
-        
+
         this.fromValues = new double[points.size()];
         this.toValues = new double[points.size()];
         this.gradients = new double[points.size()];
-        
+
         for(int i = 0; i < points.size(); i++) {
             fromValues[i] = points.get(i).from;
             toValues[i] = points.get(i).to;
             gradients[i] = points.get(i).gradient;
         }
     }
-    
+
     public static double calculate(double in, double[] fromValues, double[] toValues, double[] gradients) {
         int pointIdx = floorBinarySearch(in, fromValues) - 1;
-        
+
         int pointIdxLast = fromValues.length - 1;
-        
+
         if(pointIdx < 0) { // If to left of first point return linear function intersecting said point using point's gradient
             return gradients[0] * (in - fromValues[0]) + toValues[0];
         } else if(pointIdx == pointIdxLast) { // Do same if to right of last point
@@ -40,23 +40,23 @@ public class CubicSpline {
         } else {
             double fromLeft = fromValues[pointIdx];
             double fromRight = fromValues[pointIdx + 1];
-            
+
             double toLeft = toValues[pointIdx];
             double toRight = toValues[pointIdx + 1];
-            
+
             double gradientLeft = gradients[pointIdx];
             double gradientRight = gradients[pointIdx + 1];
-            
+
             double fromDelta = fromRight - fromLeft;
             double toDelta = toRight - toLeft;
-            
+
             double t = (in - fromLeft) / fromDelta;
-            
+
             return lerp(t, toLeft, toRight) + t * (1.0F - t) * lerp(t, gradientLeft * fromDelta - toDelta,
-                                                                    -gradientRight * fromDelta + toDelta);
+                -gradientRight * fromDelta + toDelta);
         }
     }
-    
+
     private static int floorBinarySearch(double targetValue, double[] values) {
         int left = 0;
         int right = values.length;
@@ -73,14 +73,14 @@ public class CubicSpline {
         }
         return left;
     }
-    
+
     public double apply(double in) {
         return calculate(in, fromValues, toValues, gradients);
     }
-    
-    
+
+
     public record Point(double from, double to, double gradient) implements Comparable<Point> {
-        
+
         @Override
         public int compareTo(@NotNull CubicSpline.Point o) {
             return Double.compare(from, o.from);

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

@@ -13,13 +13,13 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public class ClampNormalizer extends Normalizer {
     private final double min;
     private final double max;
-    
+
     public ClampNormalizer(NoiseSampler sampler, double min, double max) {
         super(sampler);
         this.min = min;
         this.max = max;
     }
-    
+
     @Override
     public double normalize(double in) {
         return Math.max(Math.min(in, max), min);

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

@@ -5,14 +5,14 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 
 public class CubicSplineNoiseSampler extends Normalizer {
-    
+
     private final CubicSpline spline;
-    
+
     public CubicSplineNoiseSampler(NoiseSampler sampler, CubicSpline spline) {
         super(sampler);
         this.spline = spline;
     }
-    
+
     @Override
     public double normalize(double in) {
         return spline.apply(in);

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

@@ -12,9 +12,9 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 
 public class ExpressionNormalizer extends Normalizer {
-    
+
     private final Expression expression;
-    
+
     public ExpressionNormalizer(NoiseSampler sampler, Map<String, Function> functions, String eq, Map<String, Double> vars)
     throws ParseException {
         super(sampler);
@@ -25,7 +25,7 @@ public class ExpressionNormalizer extends Normalizer {
         functions.forEach(p::registerFunction);
         expression = p.parse(eq, scope);
     }
-    
+
     @Override
     public double normalize(double in) {
         return expression.evaluate(in);

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

@@ -16,13 +16,13 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public class LinearNormalizer extends Normalizer {
     private final double min;
     private final double max;
-    
+
     public LinearNormalizer(NoiseSampler sampler, double min, double max) {
         super(sampler);
         this.min = min;
         this.max = max;
     }
-    
+
     @Override
     public double normalize(double in) {
         return (in - min) * (2 / (max - min)) - 1;

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

@@ -15,18 +15,18 @@ import com.dfsek.terra.api.util.MathUtil;
  * Normalizer to redistribute normally distributed data to a continuous distribution via an automatically generated lookup table.
  */
 public class NormalNormalizer extends Normalizer {
-    
+
     private final double[] lookup;
-    
+
     public NormalNormalizer(NoiseSampler sampler, int buckets, double mean, double standardDeviation) {
         super(sampler);
         this.lookup = new double[buckets];
-        
+
         for(int i = 0; i < buckets; i++) {
             lookup[i] = MathUtil.normalInverse((double) i / buckets, mean, standardDeviation);
         }
     }
-    
+
     @Override
     public double normalize(double in) {
         int start = 0;
@@ -41,12 +41,12 @@ public class NormalNormalizer extends Normalizer {
         }
         double left = Math.abs(lookup[start] - in);
         double right = Math.abs(lookup[end] - in);
-        
+
         double fin;
         if(left <= right) {
             fin = (double) start / (lookup.length);
         } else fin = (double) end / (lookup.length);
-        
+
         return (fin - 0.5) * 2;
     }
 }

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

@@ -12,18 +12,18 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 public abstract class Normalizer implements NoiseSampler {
     private final NoiseSampler sampler;
-    
+
     public Normalizer(NoiseSampler sampler) {
         this.sampler = sampler;
     }
-    
+
     public abstract double normalize(double in);
-    
+
     @Override
     public double noise(long seed, double x, double y) {
         return normalize(sampler.noise(seed, x, y));
     }
-    
+
     @Override
     public double noise(long seed, double x, double y, double z) {
         return normalize(sampler.noise(seed, x, y, z));

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

@@ -12,12 +12,12 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 public class PosterizationNormalizer extends Normalizer {
     private final double stepSize;
-    
+
     public PosterizationNormalizer(NoiseSampler sampler, int steps) {
         super(sampler);
         this.stepSize = 2.0 / (steps - 1);
     }
-    
+
     @Override
     public double normalize(double in) {
         return (int) Math.round((in + 1) / stepSize) * stepSize - 1;

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

@@ -7,7 +7,7 @@ public class ProbabilityNormalizer extends Normalizer {
     public ProbabilityNormalizer(NoiseSampler sampler) {
         super(sampler);
     }
-    
+
     @Override
     public double normalize(double in) {
         return (in + 1) / 2;

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

@@ -5,12 +5,12 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 public class ScaleNormalizer extends Normalizer {
     private final double scale;
-    
+
     public ScaleNormalizer(NoiseSampler sampler, double scale) {
         super(sampler);
         this.scale = scale;
     }
-    
+
     @Override
     public double normalize(double in) {
         return in * scale;

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

@@ -15,7 +15,7 @@ import com.dfsek.terra.addons.noise.paralithic.noise.NoiseFunction3;
 
 public class FunctionUtil {
     private FunctionUtil() { }
-    
+
     public static Map<String, Function> convertFunctionsAndSamplers(Map<String, FunctionTemplate> functions,
                                                                     Map<String, DimensionApplicableNoiseSampler> samplers)
     throws ParseException {
@@ -24,9 +24,9 @@ public class FunctionUtil {
             functionMap.put(entry.getKey(), UserDefinedFunction.newInstance(entry.getValue()));
         }
         samplers.forEach((id, sampler) -> functionMap.put(id,
-                                                          sampler.getDimensions() == 2 ?
-                                                          new NoiseFunction2(sampler.getSampler()) :
-                                                          new NoiseFunction3(sampler.getSampler())));
+            sampler.getDimensions() == 2 ?
+            new NoiseFunction2(sampler.getSampler()) :
+            new NoiseFunction3(sampler.getSampler())));
         return functionMap;
     }
 }

common/addons/config-noise-function/src/main/java/com/dfsek/terra/addons/noise/paralithic/defined/UserDefinedFunction.java

@@ -26,49 +26,49 @@ public class UserDefinedFunction implements DynamicFunction {
     private static final Map<FunctionTemplate, UserDefinedFunction> CACHE = new HashMap<>();
     private final Expression expression;
     private final int args;
-    
+
     protected UserDefinedFunction(Expression expression, int args) {
         this.expression = expression;
         this.args = args;
     }
-    
+
     public static UserDefinedFunction newInstance(FunctionTemplate template) throws ParseException {
         UserDefinedFunction function = CACHE.get(template);
         if(function == null) {
             Parser parser = new Parser();
             Scope parent = new Scope();
-            
+
             Scope functionScope = new Scope().withParent(parent);
-            
+
             template.getArgs().forEach(functionScope::addInvocationVariable);
-            
+
             for(Entry<String, FunctionTemplate> entry : template.getFunctions().entrySet()) {
                 String id = entry.getKey();
                 FunctionTemplate nest = entry.getValue();
                 parser.registerFunction(id, newInstance(nest));
             }
-            
+
             function = new UserDefinedFunction(parser.parse(template.getFunction(), functionScope), template.getArgs().size());
             CACHE.put(template, function);
         }
         return function;
     }
-    
+
     @Override
     public double eval(double... args) {
         return expression.evaluate(args);
     }
-    
+
     @Override
     public double eval(Context context, double... args) {
         return expression.evaluate(context, args);
     }
-    
+
     @Override
     public int getArgNumber() {
         return args;
     }
-    
+
     @Override
     public Statefulness statefulness() {
         return Statefulness.STATELESS;

common/addons/config-noise-function/src/main/java/com/dfsek/terra/addons/noise/paralithic/noise/NoiseFunction2.java

@@ -16,26 +16,26 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 public class NoiseFunction2 implements DynamicFunction {
     private final NoiseSampler gen;
-    
+
     public NoiseFunction2(NoiseSampler gen) {
         this.gen = gen;
     }
-    
+
     @Override
     public double eval(double... args) {
         throw new UnsupportedOperationException("Cannot evaluate seeded function without seed context.");
     }
-    
+
     @Override
     public double eval(Context context, double... args) {
         return gen.noise(((SeedContext) context).getSeed(), args[0], args[1]);
     }
-    
+
     @Override
     public int getArgNumber() {
         return 2;
     }
-    
+
     @Override
     public Statefulness statefulness() {
         return Statefulness.CONTEXTUAL;

common/addons/config-noise-function/src/main/java/com/dfsek/terra/addons/noise/paralithic/noise/NoiseFunction3.java

@@ -16,26 +16,26 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 public class NoiseFunction3 implements DynamicFunction {
     private final NoiseSampler gen;
-    
+
     public NoiseFunction3(NoiseSampler gen) {
         this.gen = gen;
     }
-    
+
     @Override
     public double eval(double... args) {
         throw new UnsupportedOperationException("Cannot evaluate seeded function without seed context.");
     }
-    
+
     @Override
     public double eval(Context context, double... args) {
         return gen.noise(((SeedContext) context).getSeed(), args[0], args[1], args[2]);
     }
-    
+
     @Override
     public int getArgNumber() {
         return 3;
     }
-    
+
     @Override
     public Statefulness statefulness() {
         return Statefulness.CONTEXTUAL;

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

@@ -12,11 +12,11 @@ import com.dfsek.paralithic.functions.dynamic.Context;
 
 public class SeedContext implements Context {
     private final long seed;
-    
+
     public SeedContext(long seed) {
         this.seed = seed;
     }
-    
+
     public long getSeed() {
         return seed;
     }

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

@@ -14,27 +14,27 @@ public class DomainWarpedSampler implements NoiseSampler {
     private final NoiseSampler function;
     private final NoiseSampler warp;
     private final double amplitude;
-    
+
     public DomainWarpedSampler(NoiseSampler function, NoiseSampler warp, double amplitude) {
         this.function = function;
         this.warp = warp;
         this.amplitude = amplitude;
     }
-    
+
     @Override
     public double noise(long seed, double x, double y) {
         return function.noise(seed++,
-                              x + warp.noise(seed++, x, y) * amplitude,
-                              y + warp.noise(seed, x, y) * amplitude
-                             );
+            x + warp.noise(seed++, x, y) * amplitude,
+            y + warp.noise(seed, x, y) * amplitude
+        );
     }
-    
+
     @Override
     public double noise(long seed, double x, double y, double z) {
         return function.noise(seed++,
-                              x + warp.noise(seed++, x, y, z) * amplitude,
-                              y + warp.noise(seed++, x, y, z) * amplitude,
-                              z + warp.noise(seed, x, y, z) * amplitude
-                             );
+            x + warp.noise(seed++, x, y, z) * amplitude,
+            y + warp.noise(seed++, x, y, z) * amplitude,
+            z + warp.noise(seed, x, y, z) * amplitude
+        );
     }
 }

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

@@ -15,27 +15,27 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public class ImageSampler implements NoiseSampler {
     private final BufferedImage image;
     private final Channel channel;
-    
+
     private final double frequency;
-    
+
     public ImageSampler(BufferedImage image, Channel channel, double frequency) {
         this.image = image;
         this.channel = channel;
         this.frequency = frequency;
     }
-    
+
     @Override
     public double noise(long seed, double x, double y) {
         return ((channel.getChannel(image.getRGB(Math.floorMod((int) Math.floor(x * frequency), image.getWidth()),
-                                                 Math.floorMod((int) Math.floor(y * frequency), image.getHeight()))) / 255D) - 0.5) *
+            Math.floorMod((int) Math.floor(y * frequency), image.getHeight()))) / 255D) - 0.5) *
                2;
     }
-    
+
     @Override
     public double noise(long seed, double x, double y, double z) {
         return noise(seed, x, y);
     }
-    
+
     public enum Channel {
         RED {
             @Override
@@ -67,7 +67,7 @@ public class ImageSampler implements NoiseSampler {
                 return (mashed >> 24) & 0xff;
             }
         };
-        
+
         public abstract int getChannel(int mashed);
     }
 }

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

@@ -14,22 +14,22 @@ public class KernelSampler implements NoiseSampler {
     private final double[][] kernel;
     private final NoiseSampler in;
     private double frequency = 1;
-    
+
     public KernelSampler(double[][] kernel, NoiseSampler in) {
         this.kernel = kernel;
         this.in = in;
     }
-    
+
     public void setFrequency(double frequency) {
         this.frequency = frequency;
     }
-    
+
     @Override
     public double noise(long seed, double x, double y) {
         x *= frequency;
         y *= frequency;
         double accumulator = 0;
-        
+
         for(int kx = 0; kx < kernel.length; kx++) {
             for(int ky = 0; ky < kernel[kx].length; ky++) {
                 double k = kernel[kx][ky];
@@ -38,17 +38,17 @@ public class KernelSampler implements NoiseSampler {
                 }
             }
         }
-        
+
         return accumulator;
     }
-    
+
     @Override
     public double noise(long seed, double x, double y, double z) {
         x *= frequency;
         y *= frequency;
         z *= frequency;
         double accumulator = 0;
-        
+
         for(int kx = 0; kx < kernel.length; kx++) {
             for(int ky = 0; ky < kernel[kx].length; ky++) {
                 double k = kernel[kx][ky];
@@ -57,7 +57,7 @@ public class KernelSampler implements NoiseSampler {
                 }
             }
         }
-        
+
         return accumulator;
     }
 }

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

@@ -7,19 +7,19 @@ public class LinearHeightmapSampler implements NoiseSampler {
     private final NoiseSampler sampler;
     private final double scale;
     private final double base;
-    
+
     public LinearHeightmapSampler(NoiseSampler sampler, double scale, double base) {
         this.sampler = sampler;
         this.scale = scale;
         this.base = base;
     }
-    
-    
+
+
     @Override
     public double noise(long seed, double x, double y) {
         return noise(seed, x, 0, y);
     }
-    
+
     @Override
     public double noise(long seed, double x, double y, double z) {
         return -y + base + sampler.noise(seed, x, y, z) * scale;

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

@@ -4,22 +4,22 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 
 
 public class TranslateSampler implements NoiseSampler {
-    
+
     private final NoiseSampler sampler;
     private final double dx, dy, dz;
-    
+
     public TranslateSampler(NoiseSampler sampler, double dx, double dy, double dz) {
         this.sampler = sampler;
         this.dx = dx;
         this.dy = dy;
         this.dz = dz;
     }
-    
+
     @Override
     public double noise(long seed, double x, double y) {
         return sampler.noise(seed, x - dx, y - dz);
     }
-    
+
     @Override
     public double noise(long seed, double x, double y, double z) {
         return sampler.noise(seed, x - dx, y - dy, z - dz);

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

@@ -7,7 +7,7 @@ public class AdditionSampler extends BinaryArithmeticSampler {
     public AdditionSampler(NoiseSampler left, NoiseSampler right) {
         super(left, right);
     }
-    
+
     @Override
     public double operate(double left, double right) {
         return left + right;

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

@@ -6,21 +6,21 @@ import com.dfsek.terra.api.noise.NoiseSampler;
 public abstract class BinaryArithmeticSampler implements NoiseSampler {
     private final NoiseSampler left;
     private final NoiseSampler right;
-    
+
     protected BinaryArithmeticSampler(NoiseSampler left, NoiseSampler right) {
         this.left = left;
         this.right = right;
     }
-    
+
     @Override
     public double noise(long seed, double x, double y) {
         return operate(left.noise(seed, x, y), right.noise(seed, x, y));
     }
-    
+
     @Override
     public double noise(long seed, double x, double y, double z) {
         return operate(left.noise(seed, x, y, z), right.noise(seed, x, y, z));
     }
-    
+
     public abstract double operate(double left, double right);
 }

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

@@ -7,7 +7,7 @@ public class DivisionSampler extends BinaryArithmeticSampler {
     public DivisionSampler(NoiseSampler left, NoiseSampler right) {
         super(left, right);
     }
-    
+
     @Override
     public double operate(double left, double right) {
         return left / right;

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

@@ -7,7 +7,7 @@ public class MaxSampler extends BinaryArithmeticSampler {
     public MaxSampler(NoiseSampler left, NoiseSampler right) {
         super(left, right);
     }
-    
+
     @Override
     public double operate(double left, double right) {
         return Math.max(left, right);

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

@@ -7,7 +7,7 @@ public class MinSampler extends BinaryArithmeticSampler {
     public MinSampler(NoiseSampler left, NoiseSampler right) {
         super(left, right);
     }
-    
+
     @Override
     public double operate(double left, double right) {
         return Math.min(left, right);

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

@@ -7,7 +7,7 @@ public class MultiplicationSampler extends BinaryArithmeticSampler {
     public MultiplicationSampler(NoiseSampler left, NoiseSampler right) {
         super(left, right);
     }
-    
+
     @Override
     public double operate(double left, double right) {
         return left * right;

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

@@ -7,7 +7,7 @@ public class SubtractionSampler extends BinaryArithmeticSampler {
     public SubtractionSampler(NoiseSampler left, NoiseSampler right) {
         super(left, right);
     }
-    
+
     @Override
     public double operate(double left, double right) {
         return left - right;

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

@@ -16,244 +16,244 @@ import com.dfsek.terra.api.noise.NoiseSampler;
  */
 public class CellularSampler extends NoiseFunction {
     private static final double[] RAND_VECS_3D = {
-            -0.7292736885d, -0.6618439697d, 0.1735581948d, 0, 0.790292081d, -0.5480887466d, -0.2739291014d, 0, 0.7217578935d, 0.6226212466d,
-            -0.3023380997d, 0, 0.565683137d, -0.8208298145d, -0.0790000257d, 0, 0.760049034d, -0.5555979497d, -0.3370999617d, 0,
-            0.3713945616d, 0.5011264475d, 0.7816254623d, 0, -0.1277062463d, -0.4254438999d, -0.8959289049d, 0, -0.2881560924d,
-            -0.5815838982d, 0.7607405838d, 0, 0.5849561111d, -0.662820239d, -0.4674352136d, 0, 0.3307171178d, 0.0391653737d, 0.94291689d, 0,
-            0.8712121778d, -0.4113374369d, -0.2679381538d, 0, 0.580981015d, 0.7021915846d, 0.4115677815d, 0, 0.503756873d, 0.6330056931d,
-            -0.5878203852d, 0, 0.4493712205d, 0.601390195d, 0.6606022552d, 0, -0.6878403724d, 0.09018890807d, -0.7202371714d, 0,
-            -0.5958956522d, -0.6469350577d, 0.475797649d, 0, -0.5127052122d, 0.1946921978d, -0.8361987284d, 0, -0.9911507142d,
-            -0.05410276466d, -0.1212153153d, 0, -0.2149721042d, 0.9720882117d, -0.09397607749d, 0, -0.7518650936d, -0.5428057603d,
-            0.3742469607d, 0, 0.5237068895d, 0.8516377189d, -0.02107817834d, 0, 0.6333504779d, 0.1926167129d, -0.7495104896d, 0,
-            -0.06788241606d, 0.3998305789d, 0.9140719259d, 0, -0.5538628599d, -0.4729896695d, -0.6852128902d, 0, -0.7261455366d,
-            -0.5911990757d, 0.3509933228d, 0, -0.9229274737d, -0.1782808786d, 0.3412049336d, 0, -0.6968815002d, 0.6511274338d,
-            0.3006480328d, 0, 0.9608044783d, -0.2098363234d, -0.1811724921d, 0, 0.06817146062d, -0.9743405129d, 0.2145069156d, 0,
-            -0.3577285196d, -0.6697087264d, -0.6507845481d, 0, -0.1868621131d, 0.7648617052d, -0.6164974636d, 0, -0.6541697588d,
-            0.3967914832d, 0.6439087246d, 0, 0.6993340405d, -0.6164538506d, 0.3618239211d, 0, -0.1546665739d, 0.6291283928d, 0.7617583057d,
-            0, -0.6841612949d, -0.2580482182d, -0.6821542638d, 0, 0.5383980957d, 0.4258654885d, 0.7271630328d, 0, -0.5026987823d,
-            -0.7939832935d, -0.3418836993d, 0, 0.3202971715d, 0.2834415347d, 0.9039195862d, 0, 0.8683227101d, -0.0003762656404d,
-            -0.4959995258d, 0, 0.791120031d, -0.08511045745d, 0.6057105799d, 0, -0.04011016052d, -0.4397248749d, 0.8972364289d, 0,
-            0.9145119872d, 0.3579346169d, -0.1885487608d, 0, -0.9612039066d, -0.2756484276d, 0.01024666929d, 0, 0.6510361721d,
-            -0.2877799159d, -0.7023778346d, 0, -0.2041786351d, 0.7365237271d, 0.644859585d, 0, -0.7718263711d, 0.3790626912d, 0.5104855816d,
-            0, -0.3060082741d, -0.7692987727d, 0.5608371729d, 0, 0.454007341d, -0.5024843065d, 0.7357899537d, 0, 0.4816795475d,
-            0.6021208291d, -0.6367380315d, 0, 0.6961980369d, -0.3222197429d, 0.641469197d, 0, -0.6532160499d, -0.6781148932d, 0.3368515753d,
-            0, 0.5089301236d, -0.6154662304d, -0.6018234363d, 0, -0.1635919754d, -0.9133604627d, -0.372840892d, 0, 0.52408019d,
-            -0.8437664109d, 0.1157505864d, 0, 0.5902587356d, 0.4983817807d, -0.6349883666d, 0, 0.5863227872d, 0.494764745d, 0.6414307729d,
-            0, 0.6779335087d, 0.2341345225d, 0.6968408593d, 0, 0.7177054546d, -0.6858979348d, 0.120178631d, 0, -0.5328819713d,
-            -0.5205125012d, 0.6671608058d, 0, -0.8654874251d, -0.0700727088d, -0.4960053754d, 0, -0.2861810166d, 0.7952089234d,
-            0.5345495242d, 0, -0.04849529634d, 0.9810836427d, -0.1874115585d, 0, -0.6358521667d, 0.6058348682d, 0.4781800233d, 0,
-            0.6254794696d, -0.2861619734d, 0.7258696564d, 0, -0.2585259868d, 0.5061949264d, -0.8227581726d, 0, 0.02136306781d,
-            0.5064016808d, -0.8620330371d, 0, 0.200111773d, 0.8599263484d, 0.4695550591d, 0, 0.4743561372d, 0.6014985084d, -0.6427953014d,
-            0, 0.6622993731d, -0.5202474575d, -0.5391679918d, 0, 0.08084972818d, -0.6532720452d, 0.7527940996d, 0, -0.6893687501d,
-            0.0592860349d, 0.7219805347d, 0, -0.1121887082d, -0.9673185067d, 0.2273952515d, 0, 0.7344116094d, 0.5979668656d, -0.3210532909d,
-            0, 0.5789393465d, -0.2488849713d, 0.7764570201d, 0, 0.6988182827d, 0.3557169806d, -0.6205791146d, 0, -0.8636845529d,
-            -0.2748771249d, -0.4224826141d, 0, -0.4247027957d, -0.4640880967d, 0.777335046d, 0, 0.5257722489d, -0.8427017621d,
-            0.1158329937d, 0, 0.9343830603d, 0.316302472d, -0.1639543925d, 0, -0.1016836419d, -0.8057303073d, -0.5834887393d, 0,
-            -0.6529238969d, 0.50602126d, -0.5635892736d, 0, -0.2465286165d, -0.9668205684d, -0.06694497494d, 0, -0.9776897119d,
-            -0.2099250524d, -0.007368825344d, 0, 0.7736893337d, 0.5734244712d, 0.2694238123d, 0, -0.6095087895d, 0.4995678998d,
-            0.6155736747d, 0, 0.5794535482d, 0.7434546771d, 0.3339292269d, 0, -0.8226211154d, 0.08142581855d, 0.5627293636d, 0,
-            -0.510385483d, 0.4703667658d, 0.7199039967d, 0, -0.5764971849d, -0.07231656274d, -0.8138926898d, 0, 0.7250628871d,
-            0.3949971505d, -0.5641463116d, 0, -0.1525424005d, 0.4860840828d, -0.8604958341d, 0, -0.5550976208d, -0.4957820792d,
-            0.667882296d, 0, -0.1883614327d, 0.9145869398d, 0.357841725d, 0, 0.7625556724d, -0.5414408243d, -0.3540489801d, 0,
-            -0.5870231946d, -0.3226498013d, -0.7424963803d, 0, 0.3051124198d, 0.2262544068d, -0.9250488391d, 0, 0.6379576059d, 0.577242424d,
-            -0.5097070502d, 0, -0.5966775796d, 0.1454852398d, -0.7891830656d, 0, -0.658330573d, 0.6555487542d, -0.3699414651d, 0,
-            0.7434892426d, 0.2351084581d, 0.6260573129d, 0, 0.5562114096d, 0.8264360377d, -0.0873632843d, 0, -0.3028940016d, -0.8251527185d,
-            0.4768419182d, 0, 0.1129343818d, -0.985888439d, -0.1235710781d, 0, 0.5937652891d, -0.5896813806d, 0.5474656618d, 0,
-            0.6757964092d, -0.5835758614d, -0.4502648413d, 0, 0.7242302609d, -0.1152719764d, 0.6798550586d, 0, -0.9511914166d,
-            0.0753623979d, -0.2992580792d, 0, 0.2539470961d, -0.1886339355d, 0.9486454084d, 0, 0.571433621d, -0.1679450851d, -0.8032795685d,
-            0, -0.06778234979d, 0.3978269256d, 0.9149531629d, 0, 0.6074972649d, 0.733060024d, -0.3058922593d, 0, -0.5435478392d,
-            0.1675822484d, 0.8224791405d, 0, -0.5876678086d, -0.3380045064d, -0.7351186982d, 0, -0.7967562402d, 0.04097822706d,
-            -0.6029098428d, 0, -0.1996350917d, 0.8706294745d, 0.4496111079d, 0, -0.02787660336d, -0.9106232682d, -0.4122962022d, 0,
-            -0.7797625996d, -0.6257634692d, 0.01975775581d, 0, -0.5211232846d, 0.7401644346d, -0.4249554471d, 0, 0.8575424857d,
-            0.4053272873d, -0.3167501783d, 0, 0.1045223322d, 0.8390195772d, -0.5339674439d, 0, 0.3501822831d, 0.9242524096d, -0.1520850155d,
-            0, 0.1987849858d, 0.07647613266d, 0.9770547224d, 0, 0.7845996363d, 0.6066256811d, -0.1280964233d, 0, 0.09006737436d,
-            -0.9750989929d, -0.2026569073d, 0, -0.8274343547d, -0.542299559d, 0.1458203587d, 0, -0.3485797732d, -0.415802277d, 0.840000362d,
-            0, -0.2471778936d, -0.7304819962d, -0.6366310879d, 0, -0.3700154943d, 0.8577948156d, 0.3567584454d, 0, 0.5913394901d,
-            -0.548311967d, -0.5913303597d, 0, 0.1204873514d, -0.7626472379d, -0.6354935001d, 0, 0.616959265d, 0.03079647928d, 0.7863922953d,
-            0, 0.1258156836d, -0.6640829889d, -0.7369967419d, 0, -0.6477565124d, -0.1740147258d, -0.7417077429d, 0, 0.6217889313d,
-            -0.7804430448d, -0.06547655076d, 0, 0.6589943422d, -0.6096987708d, 0.4404473475d, 0, -0.2689837504d, -0.6732403169d,
-            -0.6887635427d, 0, -0.3849775103d, 0.5676542638d, 0.7277093879d, 0, 0.5754444408d, 0.8110471154d, -0.1051963504d, 0,
-            0.9141593684d, 0.3832947817d, 0.131900567d, 0, -0.107925319d, 0.9245493968d, 0.3654593525d, 0, 0.377977089d, 0.3043148782d,
-            0.8743716458d, 0, -0.2142885215d, -0.8259286236d, 0.5214617324d, 0, 0.5802544474d, 0.4148098596d, -0.7008834116d, 0,
-            -0.1982660881d, 0.8567161266d, -0.4761596756d, 0, -0.03381553704d, 0.3773180787d, -0.9254661404d, 0, -0.6867922841d,
-            -0.6656597827d, 0.2919133642d, 0, 0.7731742607d, -0.2875793547d, -0.5652430251d, 0, -0.09655941928d, 0.9193708367d,
-            -0.3813575004d, 0, 0.2715702457d, -0.9577909544d, -0.09426605581d, 0, 0.2451015704d, -0.6917998565d, -0.6792188003d, 0,
-            0.977700782d, -0.1753855374d, 0.1155036542d, 0, -0.5224739938d, 0.8521606816d, 0.02903615945d, 0, -0.7734880599d,
-            -0.5261292347d, 0.3534179531d, 0, -0.7134492443d, -0.269547243d, 0.6467878011d, 0, 0.1644037271d, 0.5105846203d, -0.8439637196d,
-            0, 0.6494635788d, 0.05585611296d, 0.7583384168d, 0, -0.4711970882d, 0.5017280509d, -0.7254255765d, 0, -0.6335764307d,
-            -0.2381686273d, -0.7361091029d, 0, -0.9021533097d, -0.270947803d, -0.3357181763d, 0, -0.3793711033d, 0.872258117d,
-            0.3086152025d, 0, -0.6855598966d, -0.3250143309d, 0.6514394162d, 0, 0.2900942212d, -0.7799057743d, -0.5546100667d, 0,
-            -0.2098319339d, 0.85037073d, 0.4825351604d, 0, -0.4592603758d, 0.6598504336d, -0.5947077538d, 0, 0.8715945488d, 0.09616365406d,
-            -0.4807031248d, 0, -0.6776666319d, 0.7118504878d, -0.1844907016d, 0, 0.7044377633d, 0.312427597d, 0.637304036d, 0,
-            -0.7052318886d, -0.2401093292d, -0.6670798253d, 0, 0.081921007d, -0.7207336136d, -0.6883545647d, 0, -0.6993680906d,
-            -0.5875763221d, -0.4069869034d, 0, -0.1281454481d, 0.6419895885d, 0.7559286424d, 0, -0.6337388239d, -0.6785471501d,
-            -0.3714146849d, 0, 0.5565051903d, -0.2168887573d, -0.8020356851d, 0, -0.5791554484d, 0.7244372011d, -0.3738578718d, 0,
-            0.1175779076d, -0.7096451073d, 0.6946792478d, 0, -0.6134619607d, 0.1323631078d, 0.7785527795d, 0, 0.6984635305d,
-            -0.02980516237d, -0.715024719d, 0, 0.8318082963d, -0.3930171956d, 0.3919597455d, 0, 0.1469576422d, 0.05541651717d,
-            -0.9875892167d, 0, 0.708868575d, -0.2690503865d, 0.6520101478d, 0, 0.2726053183d, 0.67369766d, -0.68688995d, 0, -0.6591295371d,
-            0.3035458599d, -0.6880466294d, 0, 0.4815131379d, -0.7528270071d, 0.4487723203d, 0, 0.9430009463d, 0.1675647412d, -0.2875261255d,
-            0, 0.434802957d, 0.7695304522d, -0.4677277752d, 0, 0.3931996188d, 0.594473625d, 0.7014236729d, 0, 0.7254336655d, -0.603925654d,
-            0.3301814672d, 0, 0.7590235227d, -0.6506083235d, 0.02433313207d, 0, -0.8552768592d, -0.3430042733d, 0.3883935666d, 0,
-            -0.6139746835d, 0.6981725247d, 0.3682257648d, 0, -0.7465905486d, -0.5752009504d, 0.3342849376d, 0, 0.5730065677d, 0.810555537d,
-            -0.1210916791d, 0, -0.9225877367d, -0.3475211012d, -0.167514036d, 0, -0.7105816789d, -0.4719692027d, -0.5218416899d, 0,
-            -0.08564609717d, 0.3583001386d, 0.929669703d, 0, -0.8279697606d, -0.2043157126d, 0.5222271202d, 0, 0.427944023d, 0.278165994d,
-            0.8599346446d, 0, 0.5399079671d, -0.7857120652d, -0.3019204161d, 0, 0.5678404253d, -0.5495413974d, -0.6128307303d, 0,
-            -0.9896071041d, 0.1365639107d, -0.04503418428d, 0, -0.6154342638d, -0.6440875597d, 0.4543037336d, 0, 0.1074204368d,
-            -0.7946340692d, 0.5975094525d, 0, -0.3595449969d, -0.8885529948d, 0.28495784d, 0, -0.2180405296d, 0.1529888965d, 0.9638738118d,
-            0, -0.7277432317d, -0.6164050508d, -0.3007234646d, 0, 0.7249729114d, -0.00669719484d, 0.6887448187d, 0, -0.5553659455d,
-            -0.5336586252d, 0.6377908264d, 0, 0.5137558015d, 0.7976208196d, -0.3160000073d, 0, -0.3794024848d, 0.9245608561d,
-            -0.03522751494d, 0, 0.8229248658d, 0.2745365933d, -0.4974176556d, 0, -0.5404114394d, 0.6091141441d, 0.5804613989d, 0,
-            0.8036581901d, -0.2703029469d, 0.5301601931d, 0, 0.6044318879d, 0.6832968393d, 0.4095943388d, 0, 0.06389988817d, 0.9658208605d,
-            -0.2512108074d, 0, 0.1087113286d, 0.7402471173d, -0.6634877936d, 0, -0.713427712d, -0.6926784018d, 0.1059128479d, 0,
-            0.6458897819d, -0.5724548511d, -0.5050958653d, 0, -0.6553931414d, 0.7381471625d, 0.159995615d, 0, 0.3910961323d, 0.9188871375d,
-            -0.05186755998d, 0, -0.4879022471d, -0.5904376907d, 0.6429111375d, 0, 0.6014790094d, 0.7707441366d, -0.2101820095d, 0,
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+        -0.8684886582d, 0, 0.5131763485d, -0.7116334161d, 0.4798207128d, 0, -0.8737353606d, 0.18886992d, -0.4482350644d, 0,
+        0.8460043821d, -0.3725217914d, 0.3814499973d, 0, 0.8978727456d, -0.1780209141d, -0.4026575304d, 0, 0.2178065647d,
+        -0.9698322841d, -0.1094789531d, 0, -0.1518031304d, -0.7788918132d, -0.6085091231d, 0, -0.2600384876d, -0.4755398075d,
+        -0.8403819825d, 0, 0.572313509d, -0.7474340931d, -0.3373418503d, 0, -0.7174141009d, 0.1699017182d, -0.6756111411d, 0,
+        -0.684180784d, 0.02145707593d, -0.7289967412d, 0, -0.2007447902d, 0.06555605789d, -0.9774476623d, 0, -0.1148803697d,
+        -0.8044887315d, 0.5827524187d, 0, -0.7870349638d, 0.03447489231d, 0.6159443543d, 0, -0.2015596421d, 0.6859872284d,
+        0.6991389226d, 0, -0.08581082512d, -0.10920836d, -0.9903080513d, 0, 0.5532693395d, 0.7325250401d, -0.396610771d, 0,
+        -0.1842489331d, -0.9777375055d, -0.1004076743d, 0, 0.0775473789d, -0.9111505856d, 0.4047110257d, 0, 0.1399838409d,
+        0.7601631212d, -0.6344734459d, 0, 0.4484419361d, -0.845289248d, 0.2904925424d, 0
     };
-    
+
     private static final double[] RAND_VECS_2D = {
-            -0.2700222198d, -0.9628540911d, 0.3863092627d, -0.9223693152d, 0.04444859006d, -0.999011673d, -0.5992523158d, -0.8005602176d,
-            -0.7819280288d, 0.6233687174d, 0.9464672271d, 0.3227999196d, -0.6514146797d, -0.7587218957d, 0.9378472289d, 0.347048376d,
-            -0.8497875957d, -0.5271252623d, -0.879042592d, 0.4767432447d, -0.892300288d, -0.4514423508d, -0.379844434d, -0.9250503802d,
-            -0.9951650832d, 0.0982163789d, 0.7724397808d, -0.6350880136d, 0.7573283322d, -0.6530343002d, -0.9928004525d, -0.119780055d,
-            -0.0532665713d, 0.9985803285d, 0.9754253726d, -0.2203300762d, -0.7665018163d, 0.6422421394d, 0.991636706d, 0.1290606184d,
-            -0.994696838d, 0.1028503788d, -0.5379205513d, -0.84299554d, 0.5022815471d, -0.8647041387d, 0.4559821461d, -0.8899889226d,
-            -0.8659131224d, -0.5001944266d, 0.0879458407d, -0.9961252577d, -0.5051684983d, 0.8630207346d, 0.7753185226d, -0.6315704146d,
-            -0.6921944612d, 0.7217110418d, -0.5191659449d, -0.8546734591d, 0.8978622882d, -0.4402764035d, -0.1706774107d, 0.9853269617d,
-            -0.9353430106d, -0.3537420705d, -0.9992404798d, 0.03896746794d, -0.2882064021d, -0.9575683108d, -0.9663811329d, 0.2571137995d,
-            -0.8759714238d, -0.4823630009d, -0.8303123018d, -0.5572983775d, 0.05110133755d, -0.9986934731d, -0.8558373281d, -0.5172450752d,
-            0.09887025282d, 0.9951003332d, 0.9189016087d, 0.3944867976d, -0.2439375892d, -0.9697909324d, -0.8121409387d, -0.5834613061d,
-            -0.9910431363d, 0.1335421355d, 0.8492423985d, -0.5280031709d, -0.9717838994d, -0.2358729591d, 0.9949457207d, 0.1004142068d,
-            0.6241065508d, -0.7813392434d, 0.662910307d, 0.7486988212d, -0.7197418176d, 0.6942418282d, -0.8143370775d, -0.5803922158d,
-            0.104521054d, -0.9945226741d, -0.1065926113d, -0.9943027784d, 0.445799684d, -0.8951327509d, 0.105547406d, 0.9944142724d,
-            -0.992790267d, 0.1198644477d, -0.8334366408d, 0.552615025d, 0.9115561563d, -0.4111755999d, 0.8285544909d, -0.5599084351d,
-            0.7217097654d, -0.6921957921d, 0.4940492677d, -0.8694339084d, -0.3652321272d, -0.9309164803d, -0.9696606758d, 0.2444548501d,
-            0.08925509731d, -0.996008799d, 0.5354071276d, -0.8445941083d, -0.1053576186d, 0.9944343981d, -0.9890284586d, 0.1477251101d,
-            0.004856104961d, 0.9999882091d, 0.9885598478d, 0.1508291331d, 0.9286129562d, -0.3710498316d, -0.5832393863d, -0.8123003252d,
-            0.3015207509d, 0.9534596146d, -0.9575110528d, 0.2883965738d, 0.9715802154d, -0.2367105511d, 0.229981792d, 0.9731949318d,
-            0.955763816d, -0.2941352207d, 0.740956116d, 0.6715534485d, -0.9971513787d, -0.07542630764d, 0.6905710663d, -0.7232645452d,
-            -0.290713703d, -0.9568100872d, 0.5912777791d, -0.8064679708d, -0.9454592212d, -0.325740481d, 0.6664455681d, 0.74555369d,
-            0.6236134912d, 0.7817328275d, 0.9126993851d, -0.4086316587d, -0.8191762011d, 0.5735419353d, -0.8812745759d, -0.4726046147d,
-            0.9953313627d, 0.09651672651d, 0.9855650846d, -0.1692969699d, -0.8495980887d, 0.5274306472d, 0.6174853946d, -0.7865823463d,
-            0.8508156371d, 0.52546432d, 0.9985032451d, -0.05469249926d, 0.1971371563d, -0.9803759185d, 0.6607855748d, -0.7505747292d,
-            -0.03097494063d, 0.9995201614d, -0.6731660801d, 0.739491331d, -0.7195018362d, -0.6944905383d, 0.9727511689d, 0.2318515979d,
-            0.9997059088d, -0.0242506907d, 0.4421787429d, -0.8969269532d, 0.9981350961d, -0.061043673d, -0.9173660799d, -0.3980445648d,
-            -0.8150056635d, -0.5794529907d, -0.8789331304d, 0.4769450202d, 0.0158605829d, 0.999874213d, -0.8095464474d, 0.5870558317d,
-            -0.9165898907d, -0.3998286786d, -0.8023542565d, 0.5968480938d, -0.5176737917d, 0.8555780767d, -0.8154407307d, -0.5788405779d,
-            0.4022010347d, -0.9155513791d, -0.9052556868d, -0.4248672045d, 0.7317445619d, 0.6815789728d, -0.5647632201d, -0.8252529947d,
-            -0.8403276335d, -0.5420788397d, -0.9314281527d, 0.363925262d, 0.5238198472d, 0.8518290719d, 0.7432803869d, -0.6689800195d,
-            -0.985371561d, -0.1704197369d, 0.4601468731d, 0.88784281d, 0.825855404d, 0.5638819483d, 0.6182366099d, 0.7859920446d,
-            0.8331502863d, -0.553046653d, 0.1500307506d, 0.9886813308d, -0.662330369d, -0.7492119075d, -0.668598664d, 0.743623444d,
-            0.7025606278d, 0.7116238924d, -0.5419389763d, -0.8404178401d, -0.3388616456d, 0.9408362159d, 0.8331530315d, 0.5530425174d,
-            -0.2989720662d, -0.9542618632d, 0.2638522993d, 0.9645630949d, 0.124108739d, -0.9922686234d, -0.7282649308d, -0.6852956957d,
-            0.6962500149d, 0.7177993569d, -0.9183535368d, 0.3957610156d, -0.6326102274d, -0.7744703352d, -0.9331891859d, -0.359385508d,
-            -0.1153779357d, -0.9933216659d, 0.9514974788d, -0.3076565421d, -0.08987977445d, -0.9959526224d, 0.6678496916d, 0.7442961705d,
-            0.7952400393d, -0.6062947138d, -0.6462007402d, -0.7631674805d, -0.2733598753d, 0.9619118351d, 0.9669590226d, -0.254931851d,
-            -0.9792894595d, 0.2024651934d, -0.5369502995d, -0.8436138784d, -0.270036471d, -0.9628500944d, -0.6400277131d, 0.7683518247d,
-            -0.7854537493d, -0.6189203566d, 0.06005905383d, -0.9981948257d, -0.02455770378d, 0.9996984141d, -0.65983623d, 0.751409442d,
-            -0.6253894466d, -0.7803127835d, -0.6210408851d, -0.7837781695d, 0.8348888491d, 0.5504185768d, -0.1592275245d, 0.9872419133d,
-            0.8367622488d, 0.5475663786d, -0.8675753916d, -0.4973056806d, -0.2022662628d, -0.9793305667d, 0.9399189937d, 0.3413975472d,
-            0.9877404807d, -0.1561049093d, -0.9034455656d, 0.4287028224d, 0.1269804218d, -0.9919052235d, -0.3819600854d, 0.924178821d,
-            0.9754625894d, 0.2201652486d, -0.3204015856d, -0.9472818081d, -0.9874760884d, 0.1577687387d, 0.02535348474d, -0.9996785487d,
-            0.4835130794d, -0.8753371362d, -0.2850799925d, -0.9585037287d, -0.06805516006d, -0.99768156d, -0.7885244045d, -0.6150034663d,
-            0.3185392127d, -0.9479096845d, 0.8880043089d, 0.4598351306d, 0.6476921488d, -0.7619021462d, 0.9820241299d, 0.1887554194d,
-            0.9357275128d, -0.3527237187d, -0.8894895414d, 0.4569555293d, 0.7922791302d, 0.6101588153d, 0.7483818261d, 0.6632681526d,
-            -0.7288929755d, -0.6846276581d, 0.8729032783d, -0.4878932944d, 0.8288345784d, 0.5594937369d, 0.08074567077d, 0.9967347374d,
-            0.9799148216d, -0.1994165048d, -0.580730673d, -0.8140957471d, -0.4700049791d, -0.8826637636d, 0.2409492979d, 0.9705377045d,
-            0.9437816757d, -0.3305694308d, -0.8927998638d, -0.4504535528d, -0.8069622304d, 0.5906030467d, 0.06258973166d, 0.9980393407d,
-            -0.9312597469d, 0.3643559849d, 0.5777449785d, 0.8162173362d, -0.3360095855d, -0.941858566d, 0.697932075d, -0.7161639607d,
-            -0.002008157227d, -0.9999979837d, -0.1827294312d, -0.9831632392d, -0.6523911722d, 0.7578824173d, -0.4302626911d, -0.9027037258d,
-            -0.9985126289d, -0.05452091251d, -0.01028102172d, -0.9999471489d, -0.4946071129d, 0.8691166802d, -0.2999350194d, 0.9539596344d,
-            0.8165471961d, 0.5772786819d, 0.2697460475d, 0.962931498d, -0.7306287391d, -0.6827749597d, -0.7590952064d, -0.6509796216d,
-            -0.907053853d, 0.4210146171d, -0.5104861064d, -0.8598860013d, 0.8613350597d, 0.5080373165d, 0.5007881595d, -0.8655698812d,
-            -0.654158152d, 0.7563577938d, -0.8382755311d, -0.545246856d, 0.6940070834d, 0.7199681717d, 0.06950936031d, 0.9975812994d,
-            0.1702942185d, -0.9853932612d, 0.2695973274d, 0.9629731466d, 0.5519612192d, -0.8338697815d, 0.225657487d, -0.9742067022d,
-            0.4215262855d, -0.9068161835d, 0.4881873305d, -0.8727388672d, -0.3683854996d, -0.9296731273d, -0.9825390578d, 0.1860564427d,
-            0.81256471d, 0.5828709909d, 0.3196460933d, -0.9475370046d, 0.9570913859d, 0.2897862643d, -0.6876655497d, -0.7260276109d,
-            -0.9988770922d, -0.047376731d, -0.1250179027d, 0.992154486d, -0.8280133617d, 0.560708367d, 0.9324863769d, -0.3612051451d,
-            0.6394653183d, 0.7688199442d, -0.01623847064d, -0.9998681473d, -0.9955014666d, -0.09474613458d, -0.81453315d, 0.580117012d,
-            0.4037327978d, -0.9148769469d, 0.9944263371d, 0.1054336766d, -0.1624711654d, 0.9867132919d, -0.9949487814d, -0.100383875d,
-            -0.6995302564d, 0.7146029809d, 0.5263414922d, -0.85027327d, -0.5395221479d, 0.841971408d, 0.6579370318d, 0.7530729462d,
-            0.01426758847d, -0.9998982128d, -0.6734383991d, 0.7392433447d, 0.639412098d, -0.7688642071d, 0.9211571421d, 0.3891908523d,
-            -0.146637214d, -0.9891903394d, -0.782318098d, 0.6228791163d, -0.5039610839d, -0.8637263605d, -0.7743120191d, -0.6328039957d,
-            };
-    
-    
+        -0.2700222198d, -0.9628540911d, 0.3863092627d, -0.9223693152d, 0.04444859006d, -0.999011673d, -0.5992523158d, -0.8005602176d,
+        -0.7819280288d, 0.6233687174d, 0.9464672271d, 0.3227999196d, -0.6514146797d, -0.7587218957d, 0.9378472289d, 0.347048376d,
+        -0.8497875957d, -0.5271252623d, -0.879042592d, 0.4767432447d, -0.892300288d, -0.4514423508d, -0.379844434d, -0.9250503802d,
+        -0.9951650832d, 0.0982163789d, 0.7724397808d, -0.6350880136d, 0.7573283322d, -0.6530343002d, -0.9928004525d, -0.119780055d,
+        -0.0532665713d, 0.9985803285d, 0.9754253726d, -0.2203300762d, -0.7665018163d, 0.6422421394d, 0.991636706d, 0.1290606184d,
+        -0.994696838d, 0.1028503788d, -0.5379205513d, -0.84299554d, 0.5022815471d, -0.8647041387d, 0.4559821461d, -0.8899889226d,
+        -0.8659131224d, -0.5001944266d, 0.0879458407d, -0.9961252577d, -0.5051684983d, 0.8630207346d, 0.7753185226d, -0.6315704146d,
+        -0.6921944612d, 0.7217110418d, -0.5191659449d, -0.8546734591d, 0.8978622882d, -0.4402764035d, -0.1706774107d, 0.9853269617d,
+        -0.9353430106d, -0.3537420705d, -0.9992404798d, 0.03896746794d, -0.2882064021d, -0.9575683108d, -0.9663811329d, 0.2571137995d,
+        -0.8759714238d, -0.4823630009d, -0.8303123018d, -0.5572983775d, 0.05110133755d, -0.9986934731d, -0.8558373281d, -0.5172450752d,
+        0.09887025282d, 0.9951003332d, 0.9189016087d, 0.3944867976d, -0.2439375892d, -0.9697909324d, -0.8121409387d, -0.5834613061d,
+        -0.9910431363d, 0.1335421355d, 0.8492423985d, -0.5280031709d, -0.9717838994d, -0.2358729591d, 0.9949457207d, 0.1004142068d,
+        0.6241065508d, -0.7813392434d, 0.662910307d, 0.7486988212d, -0.7197418176d, 0.6942418282d, -0.8143370775d, -0.5803922158d,
+        0.104521054d, -0.9945226741d, -0.1065926113d, -0.9943027784d, 0.445799684d, -0.8951327509d, 0.105547406d, 0.9944142724d,
+        -0.992790267d, 0.1198644477d, -0.8334366408d, 0.552615025d, 0.9115561563d, -0.4111755999d, 0.8285544909d, -0.5599084351d,
+        0.7217097654d, -0.6921957921d, 0.4940492677d, -0.8694339084d, -0.3652321272d, -0.9309164803d, -0.9696606758d, 0.2444548501d,
+        0.08925509731d, -0.996008799d, 0.5354071276d, -0.8445941083d, -0.1053576186d, 0.9944343981d, -0.9890284586d, 0.1477251101d,
+        0.004856104961d, 0.9999882091d, 0.9885598478d, 0.1508291331d, 0.9286129562d, -0.3710498316d, -0.5832393863d, -0.8123003252d,
+        0.3015207509d, 0.9534596146d, -0.9575110528d, 0.2883965738d, 0.9715802154d, -0.2367105511d, 0.229981792d, 0.9731949318d,
+        0.955763816d, -0.2941352207d, 0.740956116d, 0.6715534485d, -0.9971513787d, -0.07542630764d, 0.6905710663d, -0.7232645452d,
+        -0.290713703d, -0.9568100872d, 0.5912777791d, -0.8064679708d, -0.9454592212d, -0.325740481d, 0.6664455681d, 0.74555369d,
+        0.6236134912d, 0.7817328275d, 0.9126993851d, -0.4086316587d, -0.8191762011d, 0.5735419353d, -0.8812745759d, -0.4726046147d,
+        0.9953313627d, 0.09651672651d, 0.9855650846d, -0.1692969699d, -0.8495980887d, 0.5274306472d, 0.6174853946d, -0.7865823463d,
+        0.8508156371d, 0.52546432d, 0.9985032451d, -0.05469249926d, 0.1971371563d, -0.9803759185d, 0.6607855748d, -0.7505747292d,
+        -0.03097494063d, 0.9995201614d, -0.6731660801d, 0.739491331d, -0.7195018362d, -0.6944905383d, 0.9727511689d, 0.2318515979d,
+        0.9997059088d, -0.0242506907d, 0.4421787429d, -0.8969269532d, 0.9981350961d, -0.061043673d, -0.9173660799d, -0.3980445648d,
+        -0.8150056635d, -0.5794529907d, -0.8789331304d, 0.4769450202d, 0.0158605829d, 0.999874213d, -0.8095464474d, 0.5870558317d,
+        -0.9165898907d, -0.3998286786d, -0.8023542565d, 0.5968480938d, -0.5176737917d, 0.8555780767d, -0.8154407307d, -0.5788405779d,
+        0.4022010347d, -0.9155513791d, -0.9052556868d, -0.4248672045d, 0.7317445619d, 0.6815789728d, -0.5647632201d, -0.8252529947d,
+        -0.8403276335d, -0.5420788397d, -0.9314281527d, 0.363925262d, 0.5238198472d, 0.8518290719d, 0.7432803869d, -0.6689800195d,
+        -0.985371561d, -0.1704197369d, 0.4601468731d, 0.88784281d, 0.825855404d, 0.5638819483d, 0.6182366099d, 0.7859920446d,
+        0.8331502863d, -0.553046653d, 0.1500307506d, 0.9886813308d, -0.662330369d, -0.7492119075d, -0.668598664d, 0.743623444d,
+        0.7025606278d, 0.7116238924d, -0.5419389763d, -0.8404178401d, -0.3388616456d, 0.9408362159d, 0.8331530315d, 0.5530425174d,
+        -0.2989720662d, -0.9542618632d, 0.2638522993d, 0.9645630949d, 0.124108739d, -0.9922686234d, -0.7282649308d, -0.6852956957d,
+        0.6962500149d, 0.7177993569d, -0.9183535368d, 0.3957610156d, -0.6326102274d, -0.7744703352d, -0.9331891859d, -0.359385508d,
+        -0.1153779357d, -0.9933216659d, 0.9514974788d, -0.3076565421d, -0.08987977445d, -0.9959526224d, 0.6678496916d, 0.7442961705d,
+        0.7952400393d, -0.6062947138d, -0.6462007402d, -0.7631674805d, -0.2733598753d, 0.9619118351d, 0.9669590226d, -0.254931851d,
+        -0.9792894595d, 0.2024651934d, -0.5369502995d, -0.8436138784d, -0.270036471d, -0.9628500944d, -0.6400277131d, 0.7683518247d,
+        -0.7854537493d, -0.6189203566d, 0.06005905383d, -0.9981948257d, -0.02455770378d, 0.9996984141d, -0.65983623d, 0.751409442d,
+        -0.6253894466d, -0.7803127835d, -0.6210408851d, -0.7837781695d, 0.8348888491d, 0.5504185768d, -0.1592275245d, 0.9872419133d,
+        0.8367622488d, 0.5475663786d, -0.8675753916d, -0.4973056806d, -0.2022662628d, -0.9793305667d, 0.9399189937d, 0.3413975472d,
+        0.9877404807d, -0.1561049093d, -0.9034455656d, 0.4287028224d, 0.1269804218d, -0.9919052235d, -0.3819600854d, 0.924178821d,
+        0.9754625894d, 0.2201652486d, -0.3204015856d, -0.9472818081d, -0.9874760884d, 0.1577687387d, 0.02535348474d, -0.9996785487d,
+        0.4835130794d, -0.8753371362d, -0.2850799925d, -0.9585037287d, -0.06805516006d, -0.99768156d, -0.7885244045d, -0.6150034663d,
+        0.3185392127d, -0.9479096845d, 0.8880043089d, 0.4598351306d, 0.6476921488d, -0.7619021462d, 0.9820241299d, 0.1887554194d,
+        0.9357275128d, -0.3527237187d, -0.8894895414d, 0.4569555293d, 0.7922791302d, 0.6101588153d, 0.7483818261d, 0.6632681526d,
+        -0.7288929755d, -0.6846276581d, 0.8729032783d, -0.4878932944d, 0.8288345784d, 0.5594937369d, 0.08074567077d, 0.9967347374d,
+        0.9799148216d, -0.1994165048d, -0.580730673d, -0.8140957471d, -0.4700049791d, -0.8826637636d, 0.2409492979d, 0.9705377045d,
+        0.9437816757d, -0.3305694308d, -0.8927998638d, -0.4504535528d, -0.8069622304d, 0.5906030467d, 0.06258973166d, 0.9980393407d,
+        -0.9312597469d, 0.3643559849d, 0.5777449785d, 0.8162173362d, -0.3360095855d, -0.941858566d, 0.697932075d, -0.7161639607d,
+        -0.002008157227d, -0.9999979837d, -0.1827294312d, -0.9831632392d, -0.6523911722d, 0.7578824173d, -0.4302626911d, -0.9027037258d,
+        -0.9985126289d, -0.05452091251d, -0.01028102172d, -0.9999471489d, -0.4946071129d, 0.8691166802d, -0.2999350194d, 0.9539596344d,
+        0.8165471961d, 0.5772786819d, 0.2697460475d, 0.962931498d, -0.7306287391d, -0.6827749597d, -0.7590952064d, -0.6509796216d,
+        -0.907053853d, 0.4210146171d, -0.5104861064d, -0.8598860013d, 0.8613350597d, 0.5080373165d, 0.5007881595d, -0.8655698812d,
+        -0.654158152d, 0.7563577938d, -0.8382755311d, -0.545246856d, 0.6940070834d, 0.7199681717d, 0.06950936031d, 0.9975812994d,
+        0.1702942185d, -0.9853932612d, 0.2695973274d, 0.9629731466d, 0.5519612192d, -0.8338697815d, 0.225657487d, -0.9742067022d,
+        0.4215262855d, -0.9068161835d, 0.4881873305d, -0.8727388672d, -0.3683854996d, -0.9296731273d, -0.9825390578d, 0.1860564427d,
+        0.81256471d, 0.5828709909d, 0.3196460933d, -0.9475370046d, 0.9570913859d, 0.2897862643d, -0.6876655497d, -0.7260276109d,
+        -0.9988770922d, -0.047376731d, -0.1250179027d, 0.992154486d, -0.8280133617d, 0.560708367d, 0.9324863769d, -0.3612051451d,
+        0.6394653183d, 0.7688199442d, -0.01623847064d, -0.9998681473d, -0.9955014666d, -0.09474613458d, -0.81453315d, 0.580117012d,
+        0.4037327978d, -0.9148769469d, 0.9944263371d, 0.1054336766d, -0.1624711654d, 0.9867132919d, -0.9949487814d, -0.100383875d,
+        -0.6995302564d, 0.7146029809d, 0.5263414922d, -0.85027327d, -0.5395221479d, 0.841971408d, 0.6579370318d, 0.7530729462d,
+        0.01426758847d, -0.9998982128d, -0.6734383991d, 0.7392433447d, 0.639412098d, -0.7688642071d, 0.9211571421d, 0.3891908523d,
+        -0.146637214d, -0.9891903394d, -0.782318098d, 0.6228791163d, -0.5039610839d, -0.8637263605d, -0.7743120191d, -0.6328039957d,
+        };
+
+
     private DistanceFunction distanceFunction = DistanceFunction.EuclideanSq;
     private ReturnType returnType = ReturnType.Distance;
     private double jitterModifier = 1.0;
-    
+
     private NoiseSampler noiseLookup;
-    
+
     public CellularSampler() {
         noiseLookup = new OpenSimplex2Sampler();
     }
-    
+
     public void setDistanceFunction(DistanceFunction distanceFunction) {
         this.distanceFunction = distanceFunction;
     }
-    
+
     public void setJitterModifier(double jitterModifier) {
         this.jitterModifier = jitterModifier;
     }
-    
+
     public void setNoiseLookup(NoiseSampler noiseLookup) {
         this.noiseLookup = noiseLookup;
     }
-    
+
     public void setReturnType(ReturnType returnType) {
         this.returnType = returnType;
     }
-    
+
     @Override
     public double getNoiseRaw(long sl, double x, double y) {
         int seed = (int) sl;
         int xr = (int) Math.round(x);
         int yr = (int) Math.round(y);
-        
+
         double distance0 = Double.MAX_VALUE;
         double distance1 = Double.MAX_VALUE;
         double distance2 = Double.MAX_VALUE;
-        
+
         int closestHash = 0;
-        
+
         double cellularJitter = 0.43701595 * jitterModifier;
-        
+
         int xPrimed = (xr - 1) * PRIME_X;
         int yPrimedBase = (yr - 1) * PRIME_Y;
-        
+
         double centerX = x;
         double centerY = y;
-        
+
         for(int xi = xr - 1; xi <= xr + 1; xi++) {
             int yPrimed = yPrimedBase;
-            
+
             for(int yi = yr - 1; yi <= yr + 1; yi++) {
                 int hash = hash(seed, xPrimed, yPrimed);
                 int idx = hash & (255 << 1);
-                
+
                 double vecX = (xi - x) + RAND_VECS_2D[idx] * cellularJitter;
                 double vecY = (yi - y) + RAND_VECS_2D[idx | 1] * cellularJitter;
-                
+
                 double newDistance = switch(distanceFunction) {
                     case Manhattan -> Math.abs(vecX) + Math.abs(vecY);
                     case Hybrid -> (Math.abs(vecX) + Math.abs(vecY)) + (vecX * vecX + vecY * vecY);
                     default -> vecX * vecX + vecY * vecY;
                 };
-                
+
                 distance1 = Math.max(Math.min(distance1, newDistance), distance0);
                 if(newDistance < distance0) {
                     distance0 = newDistance;
@@ -270,14 +270,14 @@ public class CellularSampler extends NoiseFunction {
             }
             xPrimed += PRIME_X;
         }
-        
+
         if(distanceFunction == DistanceFunction.Euclidean && returnType != ReturnType.CellValue) {
             distance0 = Math.sqrt(distance0);
             if(returnType != ReturnType.CellValue) {
                 distance1 = Math.sqrt(distance1);
             }
         }
-        
+
         return switch(returnType) {
             case CellValue -> closestHash * (1 / 2147483648.0);
             case Distance -> distance0 - 1;
@@ -296,43 +296,43 @@ public class CellularSampler extends NoiseFunction {
             case Angle -> Math.atan2(y / frequency - centerY, x / frequency - centerX);
         };
     }
-    
+
     @Override
     public double getNoiseRaw(long sl, double x, double y, double z) {
         int seed = (int) sl;
         int xr = (int) Math.round(x);
         int yr = (int) Math.round(y);
         int zr = (int) Math.round(z);
-        
+
         double distance0 = Double.MAX_VALUE;
         double distance1 = Double.MAX_VALUE;
         double distance2 = Double.MAX_VALUE;
         int closestHash = 0;
-        
+
         double cellularJitter = 0.39614353 * jitterModifier;
-        
+
         int xPrimed = (xr - 1) * PRIME_X;
         int yPrimedBase = (yr - 1) * PRIME_Y;
         int zPrimedBase = (zr - 1) * PRIME_Z;
-        
+
         double centerX = x;
         double centerY = y;
         double centerZ = z;
-        
+
         for(int xi = xr - 1; xi <= xr + 1; xi++) {
             int yPrimed = yPrimedBase;
-            
+
             for(int yi = yr - 1; yi <= yr + 1; yi++) {
                 int zPrimed = zPrimedBase;
-                
+
                 for(int zi = zr - 1; zi <= zr + 1; zi++) {
                     int hash = hash(seed, xPrimed, yPrimed, zPrimed);
                     int idx = hash & (255 << 2);
-                    
+
                     double vecX = (xi - x) + RAND_VECS_3D[idx] * cellularJitter;
                     double vecY = (yi - y) + RAND_VECS_3D[idx | 1] * cellularJitter;
                     double vecZ = (zi - z) + RAND_VECS_3D[idx | 2] * cellularJitter;
-                    
+
                     double newDistance = 0;
                     switch(distanceFunction) {
                         case Euclidean, EuclideanSq -> newDistance = vecX * vecX + vecY * vecY + vecZ * vecZ;
@@ -342,7 +342,7 @@ public class CellularSampler extends NoiseFunction {
                             distance1 = Math.max(Math.min(distance1, newDistance), distance0);
                         }
                     }
-                    
+
                     if(newDistance < distance0) {
                         distance0 = newDistance;
                         closestHash = hash;
@@ -361,14 +361,14 @@ public class CellularSampler extends NoiseFunction {
             }
             xPrimed += PRIME_X;
         }
-        
+
         if(distanceFunction == DistanceFunction.Euclidean && returnType != ReturnType.CellValue) {
             distance0 = Math.sqrt(distance0);
             if(returnType != ReturnType.CellValue) {
                 distance1 = Math.sqrt(distance1);
             }
         }
-        
+
         return switch(returnType) {
             case CellValue -> closestHash * (1 / 2147483648.0);
             case Distance -> distance0 - 1;
@@ -387,15 +387,15 @@ public class CellularSampler extends NoiseFunction {
             case Angle -> Math.atan2(y / frequency - centerY, x / frequency - centerX);
         };
     }
-    
+
     public enum DistanceFunction {
         Euclidean,
         EuclideanSq,
         Manhattan,
         Hybrid
     }
-    
-    
+
+
     public enum ReturnType {
         CellValue,
         Distance,

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

@@ -12,16 +12,16 @@ package com.dfsek.terra.addons.noise.samplers.noise;
  */
 public class ConstantSampler extends NoiseFunction {
     private final double constant;
-    
+
     public ConstantSampler(double constant) {
         this.constant = constant;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y) {
         return constant;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y, double z) {
         return constant;

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

@@ -2,14 +2,14 @@ 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;
@@ -20,7 +20,7 @@ public class DistanceSampler extends NoiseFunction {
         this.radius = radius;
         this.distanceAtRadius = distance2d(distanceFunction, radius, 0); // distance2d and distance3d should return the same value
     }
-    
+
     private static double distance2d(DistanceFunction distanceFunction, double x, double z) {
         return switch(distanceFunction) {
             case Euclidean -> Math.sqrt(x * x + z * z);
@@ -28,7 +28,7 @@ public class DistanceSampler extends NoiseFunction {
             case Manhattan -> Math.abs(x) + Math.abs(z);
         };
     }
-    
+
     private static double distance3d(DistanceFunction distanceFunction, double x, double y, double z) {
         return switch(distanceFunction) {
             case Euclidean -> Math.sqrt(x * x + y * y + z * z);
@@ -36,7 +36,7 @@ public class DistanceSampler extends NoiseFunction {
             case Manhattan -> Math.abs(x) + Math.abs(y) + Math.abs(z);
         };
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y) {
         double dx = x - ox;
@@ -46,7 +46,7 @@ public class DistanceSampler extends NoiseFunction {
         if(normalize) return Math.min(((2 * dist) / distanceAtRadius) - 1, 1);
         return dist;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y, double z) {
         double dx = x - ox;
@@ -57,7 +57,7 @@ public class DistanceSampler extends NoiseFunction {
         if(normalize) return Math.min(((2 * dist) / distanceAtRadius) - 1, 1);
         return dist;
     }
-    
+
     public enum DistanceFunction {
         Euclidean,
         EuclideanSq,

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

@@ -13,38 +13,38 @@ import com.dfsek.paralithic.eval.parser.Scope;
 import com.dfsek.paralithic.eval.tokenizer.ParseException;
 import com.dfsek.paralithic.functions.Function;
 
-import com.dfsek.terra.addons.noise.paralithic.noise.SeedContext;
-
 import java.util.Map;
 
+import com.dfsek.terra.addons.noise.paralithic.noise.SeedContext;
+
 
 /**
  * NoiseSampler implementation using a Paralithic expression.
  */
 public class ExpressionFunction extends NoiseFunction {
     private final Expression expression;
-    
+
     public ExpressionFunction(Map<String, Function> functions, String eq, Map<String, Double> vars) throws ParseException {
         Parser p = new Parser();
         Scope scope = new Scope();
-        
+
         scope.addInvocationVariable("x");
         scope.addInvocationVariable("y");
         scope.addInvocationVariable("z");
-        
+
         vars.forEach(scope::create);
-        
+
         functions.forEach(p::registerFunction);
-        
+
         expression = p.parse(eq, scope);
         frequency = 1;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y) {
         return expression.evaluate(new SeedContext(seed), x, 0, y);
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y, double z) {
         return expression.evaluate(new SeedContext(seed), x, y, z);

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

@@ -23,19 +23,19 @@ public class GaborNoiseSampler extends NoiseFunction {
     private double g = Math.exp(-impulsesPerCell);
     private double omega0 = Math.PI * 0.25;
     private boolean isotropic = true;
-    
-    
+
+
     public GaborNoiseSampler() {
         rand = new WhiteNoiseSampler();
     }
-    
+
     private void recalculateRadiusAndDensity() {
         kernelRadius = (Math.sqrt(-Math.log(0.05) / Math.PI) / this.a);
         impulseDensity = (impulsesPerKernel / (Math.PI * kernelRadius * kernelRadius));
         impulsesPerCell = impulseDensity * kernelRadius * kernelRadius;
         g = Math.exp(-impulsesPerCell);
     }
-    
+
     private double gaborNoise(long seed, double x, double y) {
         x /= kernelRadius;
         y /= kernelRadius;
@@ -51,62 +51,62 @@ public class GaborNoiseSampler extends NoiseFunction {
         }
         return noise;
     }
-    
+
     private double calculateCell(long seed, int xi, int yi, double x, double y) {
         long mashedSeed = MathUtil.murmur64(31L * xi + yi) + seed;
-        
+
         double gaussianSource = (rand.getNoiseRaw(mashedSeed++) + 1) / 2;
         int impulses = 0;
         while(gaussianSource > g) {
             impulses++;
             gaussianSource *= (rand.getNoiseRaw(mashedSeed++) + 1) / 2;
         }
-        
+
         double noise = 0;
         for(int i = 0; i < impulses; i++) {
             noise += rand.getNoiseRaw(mashedSeed++) * gabor(isotropic ? (rand.getNoiseRaw(mashedSeed++) + 1) * Math.PI : omega0,
-                                                            x * kernelRadius, y * kernelRadius);
+                x * kernelRadius, y * kernelRadius);
         }
         return noise;
     }
-    
+
     private double gabor(double omega_0, double x, double y) {
         return k * (Math.exp(-Math.PI * (a * a) * (x * x + y * y)) * MathUtil.cos(2 * Math.PI * f0 * (x * MathUtil.cos(omega_0) +
                                                                                                       y * MathUtil.sin(
-                                                                                                              omega_0))));
+                                                                                                          omega_0))));
     }
-    
+
     public void setA(double a) {
         this.a = a;
         recalculateRadiusAndDensity();
     }
-    
+
     public void setDeviation(double k) {
         this.k = k;
     }
-    
+
     public void setFrequency0(double f0) {
         this.f0 = f0;
     }
-    
+
     public void setImpulsesPerKernel(double impulsesPerKernel) {
         this.impulsesPerKernel = impulsesPerKernel;
         recalculateRadiusAndDensity();
     }
-    
+
     public void setIsotropic(boolean isotropic) {
         this.isotropic = isotropic;
     }
-    
+
     public void setRotation(double omega0) {
         this.omega0 = Math.PI * omega0;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double z) {
         return gaborNoise(seed, x, z);
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y, double z) {
         return gaborNoise(seed, x, z);

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

@@ -15,51 +15,51 @@ public abstract class NoiseFunction implements NoiseSampler {
     protected static final int PRIME_X = 501125321;
     protected static final int PRIME_Y = 1136930381;
     protected static final int PRIME_Z = 1720413743;
-    
+
     protected double frequency = 0.02d;
     protected long salt;
-    
+
     public NoiseFunction() {
         this.salt = 0;
     }
-    
+
     protected static int hash(int seed, int xPrimed, int yPrimed, int zPrimed) {
         int hash = seed ^ xPrimed ^ yPrimed ^ zPrimed;
-        
+
         hash *= 0x27d4eb2d;
         return hash;
     }
-    
+
     protected static int hash(int seed, int xPrimed, int yPrimed) {
         int hash = seed ^ xPrimed ^ yPrimed;
-        
+
         hash *= 0x27d4eb2d;
         return hash;
     }
-    
+
     public void setSalt(long salt) {
         this.salt = salt;
     }
-    
+
     public double getFrequency() {
         return frequency;
     }
-    
+
     public void setFrequency(double frequency) {
         this.frequency = frequency;
     }
-    
+
     @Override
     public double noise(long seed, double x, double y) {
         return getNoiseRaw(seed + salt, x * frequency, y * frequency);
     }
-    
+
     @Override
     public double noise(long seed, double x, double y, double z) {
         return getNoiseRaw(seed + salt, x * frequency, y * frequency, z * frequency);
     }
-    
+
     public abstract double getNoiseRaw(long seed, double x, double y);
-    
+
     public abstract double getNoiseRaw(long seed, double x, double y, double z);
 }

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

@@ -15,41 +15,41 @@ public class BrownianMotionSampler extends FractalNoiseFunction {
     public BrownianMotionSampler(NoiseSampler input) {
         super(input);
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y) {
         double sum = 0;
         double amp = fractalBounding;
-        
+
         for(int i = 0; i < octaves; i++) {
             double noise = input.noise(seed++, x, y);
             sum += noise * amp;
             amp *= MathUtil.lerp(1.0, Math.min(noise + 1, 2) * 0.5, weightedStrength);
-            
+
             x *= lacunarity;
             y *= lacunarity;
             amp *= gain;
         }
-        
+
         return sum;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y, double z) {
         double sum = 0;
         double amp = fractalBounding;
-        
+
         for(int i = 0; i < octaves; i++) {
             double noise = input.noise(seed++, x, y, z);
             sum += noise * amp;
             amp *= MathUtil.lerp(1.0, (noise + 1) * 0.5, weightedStrength);
-            
+
             x *= lacunarity;
             y *= lacunarity;
             z *= lacunarity;
             amp *= gain;
         }
-        
+
         return sum;
     }
 }

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

@@ -18,12 +18,12 @@ public abstract class FractalNoiseFunction extends NoiseFunction {
     protected double gain = 0.5;
     protected double lacunarity = 2.0d;
     protected double weightedStrength = 0.0d;
-    
+
     public FractalNoiseFunction(NoiseSampler input) {
         this.input = input;
         frequency = 1;
     }
-    
+
     protected void calculateFractalBounding() {
         double gain = Math.abs(this.gain);
         double amp = gain;
@@ -34,21 +34,21 @@ public abstract class FractalNoiseFunction extends NoiseFunction {
         }
         fractalBounding = 1 / ampFractal;
     }
-    
+
     public void setGain(double gain) {
         this.gain = gain;
         calculateFractalBounding();
     }
-    
+
     public void setLacunarity(double lacunarity) {
         this.lacunarity = lacunarity;
     }
-    
+
     public void setOctaves(int octaves) {
         this.octaves = octaves;
         calculateFractalBounding();
     }
-    
+
     public void setWeightedStrength(double weightedStrength) {
         this.weightedStrength = weightedStrength;
     }

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

@@ -13,55 +13,55 @@ import com.dfsek.terra.api.util.MathUtil;
 
 public class PingPongSampler extends FractalNoiseFunction {
     private double pingPongStrength = 2.0;
-    
+
     public PingPongSampler(NoiseSampler input) {
         super(input);
     }
-    
-    
+
+
     private static double pingPong(double t) {
         t -= (int) (t * 0.5f) << 1;
         return t < 1 ? t : 2 - t;
     }
-    
+
     public void setPingPongStrength(double strength) {
         this.pingPongStrength = strength;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y) {
         double sum = 0;
         double amp = fractalBounding;
-        
+
         for(int i = 0; i < octaves; i++) {
             double noise = pingPong((input.noise(seed++, x, y) + 1) * pingPongStrength);
             sum += (noise - 0.5) * 2 * amp;
             amp *= MathUtil.lerp(1.0, noise, weightedStrength);
-            
+
             x *= lacunarity;
             y *= lacunarity;
             amp *= gain;
         }
-        
+
         return sum;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y, double z) {
         double sum = 0;
         double amp = fractalBounding;
-        
+
         for(int i = 0; i < octaves; i++) {
             double noise = pingPong((input.noise(seed++, x, y, z) + 1) * pingPongStrength);
             sum += (noise - 0.5) * 2 * amp;
             amp *= MathUtil.lerp(1.0, noise, weightedStrength);
-            
+
             x *= lacunarity;
             y *= lacunarity;
             z *= lacunarity;
             amp *= gain;
         }
-        
+
         return sum;
     }
 }

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

@@ -12,45 +12,45 @@ import com.dfsek.terra.api.util.MathUtil;
 
 
 public class RidgedFractalSampler extends FractalNoiseFunction {
-    
+
     public RidgedFractalSampler(NoiseSampler input) {
         super(input);
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y) {
         double sum = 0;
         double amp = fractalBounding;
-        
+
         for(int i = 0; i < octaves; i++) {
             double noise = Math.abs(input.noise(seed++, x, y));
             sum += (noise * -2 + 1) * amp;
             amp *= MathUtil.lerp(1.0, 1 - noise, weightedStrength);
-            
+
             x *= lacunarity;
             y *= lacunarity;
             amp *= gain;
         }
-        
+
         return sum;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y, double z) {
         double sum = 0;
         double amp = fractalBounding;
-        
+
         for(int i = 0; i < octaves; i++) {
             double noise = Math.abs(input.noise(seed++, x, y, z));
             sum += (noise * -2 + 1) * amp;
             amp *= MathUtil.lerp(1.0, 1 - noise, weightedStrength);
-            
+
             x *= lacunarity;
             y *= lacunarity;
             z *= lacunarity;
             amp *= gain;
         }
-        
+
         return sum;
     }
 }

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

@@ -15,11 +15,11 @@ import com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction;
  */
 public class GaussianNoiseSampler extends NoiseFunction {
     private final WhiteNoiseSampler whiteNoiseSampler; // Back with a white noise sampler.
-    
+
     public GaussianNoiseSampler() {
         whiteNoiseSampler = new WhiteNoiseSampler();
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y) {
         double v1, v2, s;
@@ -31,7 +31,7 @@ public class GaussianNoiseSampler extends NoiseFunction {
         double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s) / s);
         return v1 * multiplier;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y, double z) {
         double v1, v2, s;

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

@@ -16,16 +16,16 @@ import com.dfsek.terra.api.util.MathUtil;
 public class PositiveWhiteNoiseSampler extends WhiteNoiseSampler {
     private static final long POSITIVE_POW1 = 0b01111111111L << 52;
     // Bits that when applied to the exponent/sign section of a double, produce a positive number with a power of 1.
-    
+
     public double getNoiseRaw(long seed) {
         return (Double.longBitsToDouble((MathUtil.murmur64(seed) & 0x000fffffffffffffL) | POSITIVE_POW1) - 1.5) * 2;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y) {
         return (getNoiseUnmapped(seed, x, y) - 1);
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y, double z) {
         return (getNoiseUnmapped(seed, x, y, z) - 1);

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

@@ -17,43 +17,43 @@ import com.dfsek.terra.api.util.MathUtil;
 public class WhiteNoiseSampler extends NoiseFunction {
     private static final long POSITIVE_POW1 = 0b01111111111L << 52;
     // Bits that when applied to the exponent/sign section of a double, produce a positive number with a power of 1.
-    
+
     public WhiteNoiseSampler() {
     }
-    
+
     public long randomBits(long seed, double x, double y, double z) {
         long hashX = Double.doubleToRawLongBits(x) ^ seed;
         long hashZ = Double.doubleToRawLongBits(y) ^ seed;
         long hash = (((hashX ^ (hashX >>> 32)) + ((hashZ ^ (hashZ >>> 32)) << 32)) ^ seed) + Double.doubleToRawLongBits(z);
         return MathUtil.murmur64(hash);
     }
-    
+
     public long randomBits(long seed, double x, double y) {
         long hashX = Double.doubleToRawLongBits(x) ^ seed;
         long hashZ = Double.doubleToRawLongBits(y) ^ seed;
         long hash = ((hashX ^ (hashX >>> 32)) + ((hashZ ^ (hashZ >>> 32)) << 32)) ^ seed;
         return MathUtil.murmur64(hash);
     }
-    
+
     public double getNoiseRaw(long seed) {
         return (Double.longBitsToDouble((MathUtil.murmur64(seed) & 0x000fffffffffffffL) | POSITIVE_POW1) - 1.5) * 2;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y) {
         return (getNoiseUnmapped(seed, x, y) - 1.5) * 2;
     }
-    
+
     @Override
     public double getNoiseRaw(long seed, double x, double y, double z) {
         return (getNoiseUnmapped(seed, x, y, z) - 1.5) * 2;
     }
-    
+
     public double getNoiseUnmapped(long seed, double x, double y, double z) {
         long base = ((randomBits(seed, x, y, z)) & 0x000fffffffffffffL) | POSITIVE_POW1; // Sign and exponent
         return Double.longBitsToDouble(base);
     }
-    
+
     public double getNoiseUnmapped(long seed, double x, double y) {
         long base = (randomBits(seed, x, y) & 0x000fffffffffffffL) | POSITIVE_POW1; // Sign and exponent
         return Double.longBitsToDouble(base);

common/addons/config-noise-function/src/main/java/com/dfsek/terra/addons/noise/samplers/noise/simplex/OpenSimplex2SSampler.java

@@ -16,38 +16,38 @@ public class OpenSimplex2SSampler extends SimplexStyleSampler {
     public double getNoiseRaw(long sl, double x, double y) {
         int seed = (int) sl;
         // 2D OpenSimplex2S case is a modified 2D simplex noise.
-        
+
         final double SQRT3 = 1.7320508075688772935274463415059;
         final double G2 = (3 - SQRT3) / 6;
-        
+
         final double F2 = 0.5f * (SQRT3 - 1);
         double s = (x + y) * F2;
         x += s;
         y += s;
-        
-        
+
+
         int i = (int) Math.floor(x);
         int j = (int) Math.floor(y);
         double xi = x - i;
         double yi = y - j;
-        
+
         i *= PRIME_X;
         j *= PRIME_Y;
         int i1 = i + PRIME_X;
         int j1 = j + PRIME_Y;
-        
+
         double t = (xi + yi) * G2;
         double x0 = xi - t;
         double y0 = yi - t;
-        
+
         double a0 = (2.0 / 3.0) - x0 * x0 - y0 * y0;
         double value = (a0 * a0) * (a0 * a0) * gradCoord(seed, i, j, x0, y0);
-        
+
         double a1 = 2 * (1 - 2 * G2) * (1 / G2 - 2) * t + ((-2 * (1 - 2 * G2) * (1 - 2 * G2)) + a0);
         double x1 = x0 - (1 - 2 * G2);
         double y1 = y0 - (1 - 2 * G2);
         value += (a1 * a1) * (a1 * a1) * gradCoord(seed, i1, j1, x1, y1);
-        
+
         // Nested conditionals were faster than compact bit logic/arithmetic.
         double xmyi = xi - yi;
         if(t > G2) {
@@ -66,7 +66,7 @@ public class OpenSimplex2SSampler extends SimplexStyleSampler {
                     value += (a2 * a2) * (a2 * a2) * gradCoord(seed, i, j + PRIME_Y, x2, y2);
                 }
             }
-            
+
             if(yi - xmyi > 1) {
                 double x3 = x0 + (3 * G2 - 1);
                 double y3 = y0 + (3 * G2 - 2);
@@ -98,7 +98,7 @@ public class OpenSimplex2SSampler extends SimplexStyleSampler {
                     value += (a2 * a2) * (a2 * a2) * gradCoord(seed, i + PRIME_X, j, x2, y2);
                 }
             }
-            
+
             if(yi < xmyi) {
                 double x2 = x0 - G2;
                 double y2 = y0 - (G2 - 1);
@@ -115,10 +115,10 @@ public class OpenSimplex2SSampler extends SimplexStyleSampler {
                 }
             }
         }
-        
+
         return value * 18.24196194486065;
     }
-    
+
     @Override
     @SuppressWarnings("NumericOverflow")
     public double getNoiseRaw(long sl, double x, double y, double z) {
@@ -129,62 +129,62 @@ public class OpenSimplex2SSampler extends SimplexStyleSampler {
         x = r - x;
         y = r - y;
         z = r - z;
-        
-        
+
+
         int i = (int) Math.floor(x);
         int j = (int) Math.floor(y);
         int k = (int) Math.floor(z);
         double xi = x - i;
         double yi = y - j;
         double zi = z - k;
-        
+
         i *= PRIME_X;
         j *= PRIME_Y;
         k *= PRIME_Z;
         int seed2 = seed + 1293373;
-        
+
         int xNMask = (int) (-0.5 - xi);
         int yNMask = (int) (-0.5 - yi);
         int zNMask = (int) (-0.5 - zi);
-        
+
         double x0 = xi + xNMask;
         double y0 = yi + yNMask;
         double z0 = zi + zNMask;
         double a0 = 0.75 - x0 * x0 - y0 * y0 - z0 * z0;
         double value = (a0 * a0) * (a0 * a0) * gradCoord(seed, i + (xNMask & PRIME_X), j + (yNMask & PRIME_Y), k + (zNMask & PRIME_Z), x0,
-                                                         y0,
-                                                         z0);
-        
+            y0,
+            z0);
+
         double x1 = xi - 0.5;
         double y1 = yi - 0.5;
         double z1 = zi - 0.5;
         double a1 = 0.75 - x1 * x1 - y1 * y1 - z1 * z1;
         value += (a1 * a1) * (a1 * a1) * gradCoord(seed2, i + PRIME_X, j + PRIME_Y, k + PRIME_Z, x1, y1, z1);
-        
+
         double xAFlipMask0 = ((xNMask | 1) << 1) * x1;
         double yAFlipMask0 = ((yNMask | 1) << 1) * y1;
         double zAFlipMask0 = ((zNMask | 1) << 1) * z1;
         double xAFlipMask1 = (-2 - (xNMask << 2)) * x1 - 1.0;
         double yAFlipMask1 = (-2 - (yNMask << 2)) * y1 - 1.0;
         double zAFlipMask1 = (-2 - (zNMask << 2)) * z1 - 1.0;
-        
+
         boolean skip5 = false;
         double a2 = xAFlipMask0 + a0;
         if(a2 > 0) {
             double x2 = x0 - (xNMask | 1);
             value += (a2 * a2) * (a2 * a2) * gradCoord(seed, i + (~xNMask & PRIME_X), j + (yNMask & PRIME_Y), k + (zNMask & PRIME_Z), x2,
-                                                       y0,
-                                                       z0);
+                y0,
+                z0);
         } else {
             double a3 = yAFlipMask0 + zAFlipMask0 + a0;
             if(a3 > 0) {
                 double y3 = y0 - (yNMask | 1);
                 double z3 = z0 - (zNMask | 1);
                 value += (a3 * a3) * (a3 * a3) * gradCoord(seed, i + (xNMask & PRIME_X), j + (~yNMask & PRIME_Y), k + (~zNMask & PRIME_Z),
-                                                           x0,
-                                                           y3, z3);
+                    x0,
+                    y3, z3);
             }
-            
+
             double a4 = xAFlipMask1 + a1;
             if(a4 > 0) {
                 double x4 = (xNMask | 1) + x1;
@@ -192,24 +192,24 @@ public class OpenSimplex2SSampler extends SimplexStyleSampler {
                 skip5 = true;
             }
         }
-        
+
         boolean skip9 = false;
         double a6 = yAFlipMask0 + a0;
         if(a6 > 0) {
             double y6 = y0 - (yNMask | 1);
             value += (a6 * a6) * (a6 * a6) * gradCoord(seed, i + (xNMask & PRIME_X), j + (~yNMask & PRIME_Y), k + (zNMask & PRIME_Z), x0,
-                                                       y6,
-                                                       z0);
+                y6,
+                z0);
         } else {
             double a7 = xAFlipMask0 + zAFlipMask0 + a0;
             if(a7 > 0) {
                 double x7 = x0 - (xNMask | 1);
                 double z7 = z0 - (zNMask | 1);
                 value += (a7 * a7) * (a7 * a7) * gradCoord(seed, i + (~xNMask & PRIME_X), j + (yNMask & PRIME_Y), k + (~zNMask & PRIME_Z),
-                                                           x7,
-                                                           y0, z7);
+                    x7,
+                    y0, z7);
             }
-            
+
             double a8 = yAFlipMask1 + a1;
             if(a8 > 0) {
                 double y8 = (yNMask | 1) + y1;
@@ -217,24 +217,24 @@ public class OpenSimplex2SSampler extends SimplexStyleSampler {
                 skip9 = true;
             }
         }
-        
+
         boolean skipD = false;
         double aA = zAFlipMask0 + a0;
         if(aA > 0) {
             double zA = z0 - (zNMask | 1);
             value += (aA * aA) * (aA * aA) * gradCoord(seed, i + (xNMask & PRIME_X), j + (yNMask & PRIME_Y), k + (~zNMask & PRIME_Z), x0,
-                                                       y0,
-                                                       zA);
+                y0,
+                zA);
         } else {
             double aB = xAFlipMask0 + yAFlipMask0 + a0;
             if(aB > 0) {
                 double xB = x0 - (xNMask | 1);
                 double yB = y0 - (yNMask | 1);
                 value += (aB * aB) * (aB * aB) * gradCoord(seed, i + (~xNMask & PRIME_X), j + (~yNMask & PRIME_Y), k + (zNMask & PRIME_Z),
-                                                           xB,
-                                                           yB, z0);
+                    xB,
+                    yB, z0);
             }
-            
+
             double aC = zAFlipMask1 + a1;
             if(aC > 0) {
                 double zC = (zNMask | 1) + z1;
@@ -242,38 +242,38 @@ public class OpenSimplex2SSampler extends SimplexStyleSampler {
                 skipD = true;
             }
         }
-        
+
         if(!skip5) {
             double a5 = yAFlipMask1 + zAFlipMask1 + a1;
             if(a5 > 0) {
                 double y5 = (yNMask | 1) + y1;
                 double z5 = (zNMask | 1) + z1;
                 value += (a5 * a5) * (a5 * a5) * gradCoord(seed2, i + PRIME_X, j + (yNMask & (PRIME_Y << 1)), k + (zNMask & (PRIME_Z << 1)),
-                                                           x1, y5, z5);
+                    x1, y5, z5);
             }
         }
-        
+
         if(!skip9) {
             double a9 = xAFlipMask1 + zAFlipMask1 + a1;
             if(a9 > 0) {
                 double x9 = (xNMask | 1) + x1;
                 double z9 = (zNMask | 1) + z1;
                 value += (a9 * a9) * (a9 * a9) * gradCoord(seed2, i + (xNMask & (PRIME_X << 1)), j + PRIME_Y, k + (zNMask & (PRIME_Z << 1)),
-                                                           x9,
-                                                           y1, z9);
+                    x9,
+                    y1, z9);
             }
         }
-        
+
         if(!skipD) {
             double aD = xAFlipMask1 + yAFlipMask1 + a1;
             if(aD > 0) {
                 double xD = (xNMask | 1) + x1;
                 double yD = (yNMask | 1) + y1;
                 value += (aD * aD) * (aD * aD) * gradCoord(seed2, i + (xNMask & (PRIME_X << 1)), j + (yNMask & (PRIME_Y << 1)), k + PRIME_Z,
-                                                           xD, yD, z1);
+                    xD, yD, z1);
             }
         }
-        
+
         return value * 9.046026385208288;
     }
 }

common/addons/config-noise-function/src/main/java/com/dfsek/terra/addons/noise/samplers/noise/simplex/OpenSimplex2Sampler.java

@@ -12,39 +12,39 @@ package com.dfsek.terra.addons.noise.samplers.noise.simplex;
  */
 public class OpenSimplex2Sampler extends SimplexStyleSampler {
     private static final double SQRT3 = 1.7320508075688772935274463415059;
-    
+
     @Override
     public double getNoiseRaw(long sl, double x, double y) {
         int seed = (int) sl;
         // 2D OpenSimplex2 case uses the same algorithm as ordinary Simplex.
         final double G2 = (3 - SQRT3) / 6;
-        
+
         final double F2 = 0.5f * (SQRT3 - 1);
         double s = (x + y) * F2;
         x += s;
         y += s;
-        
-        
+
+
         int i = (int) Math.floor(x);
         int j = (int) Math.floor(y);
         double xi = x - i;
         double yi = y - j;
-        
+
         double t = (xi + yi) * G2;
         double x0 = xi - t;
         double y0 = yi - t;
-        
+
         i *= PRIME_X;
         j *= PRIME_Y;
-        
+
         double n0, n1, n2;
-        
+
         double a = 0.5 - x0 * x0 - y0 * y0;
         if(a <= 0) n0 = 0;
         else {
             n0 = (a * a) * (a * a) * gradCoord(seed, i, j, x0, y0);
         }
-        
+
         double c = 2 * (1 - 2 * G2) * (1 / G2 - 2) * t + ((-2 * (1 - 2 * G2) * (1 - 2 * G2)) + a);
         if(c <= 0) n2 = 0;
         else {
@@ -52,7 +52,7 @@ public class OpenSimplex2Sampler extends SimplexStyleSampler {
             double y2 = y0 + (2 * G2 - 1);
             n2 = (c * c) * (c * c) * gradCoord(seed, i + PRIME_X, j + PRIME_Y, x2, y2);
         }
-        
+
         if(y0 > x0) {
             double x1 = x0 + G2;
             double y1 = y0 + (G2 - 1);
@@ -70,10 +70,10 @@ public class OpenSimplex2Sampler extends SimplexStyleSampler {
                 n1 = (b * b) * (b * b) * gradCoord(seed, i + PRIME_X, j, x1, y1);
             }
         }
-        
+
         return (n0 + n1 + n2) * 99.83685446303647f;
     }
-    
+
     @Override
     public double getNoiseRaw(long sl, double x, double y, double z) {
         int seed = (int) sl;
@@ -83,35 +83,35 @@ public class OpenSimplex2Sampler extends SimplexStyleSampler {
         x = r - x;
         y = r - y;
         z = r - z;
-        
-        
+
+
         int i = (int) Math.round(x);
         int j = (int) Math.round(y);
         int k = (int) Math.round(z);
         double x0 = x - i;
         double y0 = y - j;
         double z0 = z - k;
-        
+
         int xNSign = (int) (-1.0 - x0) | 1;
         int yNSign = (int) (-1.0 - y0) | 1;
         int zNSign = (int) (-1.0 - z0) | 1;
-        
+
         double ax0 = xNSign * -x0;
         double ay0 = yNSign * -y0;
         double az0 = zNSign * -z0;
-        
+
         i *= PRIME_X;
         j *= PRIME_Y;
         k *= PRIME_Z;
-        
+
         double value = 0;
         double a = (0.6f - x0 * x0) - (y0 * y0 + z0 * z0);
-        
+
         for(int l = 0; ; l++) {
             if(a > 0) {
                 value += (a * a) * (a * a) * gradCoord(seed, i, j, k, x0, y0, z0);
             }
-            
+
             if(ax0 >= ay0 && ax0 >= az0) {
                 double b = a + ax0 + ax0;
                 if(b > 1) {
@@ -131,30 +131,30 @@ public class OpenSimplex2Sampler extends SimplexStyleSampler {
                     value += (b * b) * (b * b) * gradCoord(seed, i, j, k - zNSign * PRIME_Z, x0, y0, z0 + zNSign);
                 }
             }
-            
+
             if(l == 1) break;
-            
+
             ax0 = 0.5 - ax0;
             ay0 = 0.5 - ay0;
             az0 = 0.5 - az0;
-            
+
             x0 = xNSign * ax0;
             y0 = yNSign * ay0;
             z0 = zNSign * az0;
-            
+
             a += (0.75 - ax0) - (ay0 + az0);
-            
+
             i += (xNSign >> 1) & PRIME_X;
             j += (yNSign >> 1) & PRIME_Y;
             k += (zNSign >> 1) & PRIME_Z;
-            
+
             xNSign = -xNSign;
             yNSign = -yNSign;
             zNSign = -zNSign;
-            
+
             seed = ~seed;
         }
-        
+
         return value * 32.69428253173828125;
     }
 }

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

@@ -19,59 +19,59 @@ public class PerlinSampler extends SimplexStyleSampler {
         int seed = (int) sl;
         int x0 = (int) Math.floor(x);
         int y0 = (int) Math.floor(y);
-        
+
         double xd0 = x - x0;
         double yd0 = y - y0;
         double xd1 = xd0 - 1;
         double yd1 = yd0 - 1;
-        
+
         double xs = MathUtil.interpQuintic(xd0);
         double ys = MathUtil.interpQuintic(yd0);
-        
+
         x0 *= PRIME_X;
         y0 *= PRIME_Y;
         int x1 = x0 + PRIME_X;
         int y1 = y0 + PRIME_Y;
-        
+
         double xf0 = MathUtil.lerp(gradCoord(seed, x0, y0, xd0, yd0), gradCoord(seed, x1, y0, xd1, yd0), xs);
         double xf1 = MathUtil.lerp(gradCoord(seed, x0, y1, xd0, yd1), gradCoord(seed, x1, y1, xd1, yd1), xs);
-        
+
         return MathUtil.lerp(xf0, xf1, ys) * 1.4247691104677813;
     }
-    
+
     @Override
     public double getNoiseRaw(long sl, double x, double y, double z) {
         int seed = (int) sl;
         int x0 = (int) Math.floor(x);
         int y0 = (int) Math.floor(y);
         int z0 = (int) Math.floor(z);
-        
+
         double xd0 = x - x0;
         double yd0 = y - y0;
         double zd0 = z - z0;
         double xd1 = xd0 - 1;
         double yd1 = yd0 - 1;
         double zd1 = zd0 - 1;
-        
+
         double xs = MathUtil.interpQuintic(xd0);
         double ys = MathUtil.interpQuintic(yd0);
         double zs = MathUtil.interpQuintic(zd0);
-        
+
         x0 *= PRIME_X;
         y0 *= PRIME_Y;
         z0 *= PRIME_Z;
         int x1 = x0 + PRIME_X;
         int y1 = y0 + PRIME_Y;
         int z1 = z0 + PRIME_Z;
-        
+
         double xf00 = MathUtil.lerp(gradCoord(seed, x0, y0, z0, xd0, yd0, zd0), gradCoord(seed, x1, y0, z0, xd1, yd0, zd0), xs);
         double xf10 = MathUtil.lerp(gradCoord(seed, x0, y1, z0, xd0, yd1, zd0), gradCoord(seed, x1, y1, z0, xd1, yd1, zd0), xs);
         double xf01 = MathUtil.lerp(gradCoord(seed, x0, y0, z1, xd0, yd0, zd1), gradCoord(seed, x1, y0, z1, xd1, yd0, zd1), xs);
         double xf11 = MathUtil.lerp(gradCoord(seed, x0, y1, z1, xd0, yd1, zd1), gradCoord(seed, x1, y1, z1, xd1, yd1, zd1), xs);
-        
+
         double yf0 = MathUtil.lerp(xf00, xf10, ys);
         double yf1 = MathUtil.lerp(xf01, xf11, ys);
-        
+
         return MathUtil.lerp(yf0, yf1, zs) * 0.964921414852142333984375;
     }
 }

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

@@ -9,68 +9,68 @@ package com.dfsek.terra.addons.noise.samplers.noise.simplex;
 
 public class SimplexSampler extends SimplexStyleSampler {
     private static final Double2[] GRAD_2D = {
-            new Double2(-1, -1), new Double2(1, -1), new Double2(-1, 1), new Double2(1, 1),
-            new Double2(0, -1), new Double2(-1, 0), new Double2(0, 1), new Double2(1, 0),
-            };
+        new Double2(-1, -1), new Double2(1, -1), new Double2(-1, 1), new Double2(1, 1),
+        new Double2(0, -1), new Double2(-1, 0), new Double2(0, 1), new Double2(1, 0),
+        };
     private static final Double3[] GRAD_3D = {
-            new Double3(1, 1, 0), new Double3(-1, 1, 0), new Double3(1, -1, 0), new Double3(-1, -1, 0),
-            new Double3(1, 0, 1), new Double3(-1, 0, 1), new Double3(1, 0, -1), new Double3(-1, 0, -1),
-            new Double3(0, 1, 1), new Double3(0, -1, 1), new Double3(0, 1, -1), new Double3(0, -1, -1),
-            new Double3(1, 1, 0), new Double3(0, -1, 1), new Double3(-1, 1, 0), new Double3(0, -1, -1),
-            };
-    
+        new Double3(1, 1, 0), new Double3(-1, 1, 0), new Double3(1, -1, 0), new Double3(-1, -1, 0),
+        new Double3(1, 0, 1), new Double3(-1, 0, 1), new Double3(1, 0, -1), new Double3(-1, 0, -1),
+        new Double3(0, 1, 1), new Double3(0, -1, 1), new Double3(0, 1, -1), new Double3(0, -1, -1),
+        new Double3(1, 1, 0), new Double3(0, -1, 1), new Double3(-1, 1, 0), new Double3(0, -1, -1),
+        };
+
     private static final double F2 = 1.0 / 2.0;
     private static final double F3 = (1.0 / 3.0);
     private static final double G2 = 1.0 / 4.0;
     private static final double G3 = (1.0 / 6.0);
     private static final double G33 = G3 * 3 - 1;
-    
+
     private static final int X_PRIME = 1619;
     private static final int Y_PRIME = 31337;
     private static final int Z_PRIME = 6971;
-    
-    
+
+
     private static double gradCoord3D(int seed, int x, int y, int z, double xd, double yd, double zd) {
         int hash = seed;
         hash ^= X_PRIME * x;
         hash ^= Y_PRIME * y;
         hash ^= Z_PRIME * z;
-        
+
         hash = hash * hash * hash * 60493;
         hash = (hash >> 13) ^ hash;
-        
+
         Double3 g = GRAD_3D[hash & 15];
-        
+
         return xd * g.x + yd * g.y + zd * g.z;
     }
-    
+
     private static double gradCoord2D(int seed, int x, int y, double xd, double yd) {
         int hash = seed;
         hash ^= X_PRIME * x;
         hash ^= Y_PRIME * y;
-        
+
         hash = hash * hash * hash * 60493;
         hash = (hash >> 13) ^ hash;
-        
+
         Double2 g = GRAD_2D[hash & 7];
-        
+
         return xd * g.x + yd * g.y;
     }
-    
+
     @Override
     public double getNoiseRaw(long sl, double x, double y) {
         int seed = (int) sl;
         double t = (x + y) * F2;
         int i = (int) Math.floor(x + t);
         int j = (int) Math.floor(y + t);
-        
+
         t = (i + j) * G2;
         double X0 = i - t;
         double Y0 = j - t;
-        
+
         double x0 = x - X0;
         double y0 = y - Y0;
-        
+
         int i1, j1;
         if(x0 > y0) {
             i1 = 1;
@@ -79,14 +79,14 @@ public class SimplexSampler extends SimplexStyleSampler {
             i1 = 0;
             j1 = 1;
         }
-        
+
         double x1 = x0 - i1 + G2;
         double y1 = y0 - j1 + G2;
         double x2 = x0 - 1 + F2;
         double y2 = y0 - 1 + F2;
-        
+
         double n0, n1, n2;
-        
+
         t = 0.5 - x0 * x0 - y0 * y0;
         if(t < 0) {
             n0 = 0;
@@ -94,7 +94,7 @@ public class SimplexSampler extends SimplexStyleSampler {
             t *= t;
             n0 = t * t * gradCoord2D(seed, i, j, x0, y0);
         }
-        
+
         t = 0.5 - x1 * x1 - y1 * y1;
         if(t < 0) {
             n1 = 0;
@@ -102,7 +102,7 @@ public class SimplexSampler extends SimplexStyleSampler {
             t *= t;
             n1 = t * t * gradCoord2D(seed, i + i1, j + j1, x1, y1);
         }
-        
+
         t = 0.5 - x2 * x2 - y2 * y2;
         if(t < 0) {
             n2 = 0;
@@ -110,10 +110,10 @@ public class SimplexSampler extends SimplexStyleSampler {
             t *= t;
             n2 = t * t * gradCoord2D(seed, i + 1, j + 1, x2, y2);
         }
-        
+
         return 50 * (n0 + n1 + n2);
     }
-    
+
     @Override
     public double getNoiseRaw(long sl, double x, double y, double z) {
         int seed = (int) sl;
@@ -121,15 +121,15 @@ public class SimplexSampler extends SimplexStyleSampler {
         int i = (int) Math.floor(x + t);
         int j = (int) Math.floor(y + t);
         int k = (int) Math.floor(z + t);
-        
+
         t = (i + j + k) * G3;
         double x0 = x - (i - t);
         double y0 = y - (j - t);
         double z0 = z - (k - t);
-        
+
         int i1, j1, k1;
         int i2, j2, k2;
-        
+
         if(x0 >= y0) {
             if(y0 >= z0) {
                 i1 = 1;
@@ -180,7 +180,7 @@ public class SimplexSampler extends SimplexStyleSampler {
                 k2 = 0;
             }
         }
-        
+
         double x1 = x0 - i1 + G3;
         double y1 = y0 - j1 + G3;
         double z1 = z0 - k1 + G3;
@@ -190,16 +190,16 @@ public class SimplexSampler extends SimplexStyleSampler {
         double x3 = x0 + G33;
         double y3 = y0 + G33;
         double z3 = z0 + G33;
-        
+
         double n0, n1, n2, n3;
-        
+
         t = 0.6 - x0 * x0 - y0 * y0 - z0 * z0;
         if(t < 0) n0 = 0;
         else {
             t *= t;
             n0 = t * t * gradCoord3D(seed, i, j, k, x0, y0, z0);
         }
-        
+
         t = 0.6 - x1 * x1 - y1 * y1 - z1 * z1;
         if(t < 0) {
             n1 = 0;
@@ -207,7 +207,7 @@ public class SimplexSampler extends SimplexStyleSampler {
             t *= t;
             n1 = t * t * gradCoord3D(seed, i + i1, j + j1, k + k1, x1, y1, z1);
         }
-        
+
         t = 0.6 - x2 * x2 - y2 * y2 - z2 * z2;
         if(t < 0) {
             n2 = 0;
@@ -215,7 +215,7 @@ public class SimplexSampler extends SimplexStyleSampler {
             t *= t;
             n2 = t * t * gradCoord3D(seed, i + i2, j + j2, k + k2, x2, y2, z2);
         }
-        
+
         t = 0.6 - x3 * x3 - y3 * y3 - z3 * z3;
         if(t < 0) {
             n3 = 0;
@@ -223,23 +223,23 @@ public class SimplexSampler extends SimplexStyleSampler {
             t *= t;
             n3 = t * t * gradCoord3D(seed, i + 1, j + 1, k + 1, x3, y3, z3);
         }
-        
+
         return 32 * (n0 + n1 + n2 + n3);
     }
-    
+
     private static class Double2 {
         public final double x, y;
-        
+
         public Double2(double x, double y) {
             this.x = x;
             this.y = y;
         }
     }
-    
-    
+
+
     private static class Double3 {
         public final double x, y, z;
-        
+
         public Double3(double x, double y, double z) {
             this.x = x;
             this.y = y;

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

@@ -15,90 +15,90 @@ import com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction;
  */
 public abstract class SimplexStyleSampler extends NoiseFunction {
     protected static final double[] GRADIENTS_2_D = {
-            0.130526192220052d, 0.99144486137381d, 0.38268343236509d, 0.923879532511287d, 0.608761429008721d, 0.793353340291235d,
-            0.793353340291235d, 0.608761429008721d, 0.923879532511287d, 0.38268343236509d, 0.99144486137381d, 0.130526192220051d,
-            0.99144486137381d, -0.130526192220051d, 0.923879532511287d, -0.38268343236509d, 0.793353340291235d, -0.60876142900872d,
-            0.608761429008721d, -0.793353340291235d, 0.38268343236509d, -0.923879532511287d, 0.130526192220052d, -0.99144486137381d,
-            -0.130526192220052d, -0.99144486137381d, -0.38268343236509d, -0.923879532511287d, -0.608761429008721d, -0.793353340291235d,
-            -0.793353340291235d, -0.608761429008721d, -0.923879532511287d, -0.38268343236509d, -0.99144486137381d, -0.130526192220052d,
-            -0.99144486137381d, 0.130526192220051d, -0.923879532511287d, 0.38268343236509d, -0.793353340291235d, 0.608761429008721d,
-            -0.608761429008721d, 0.793353340291235d, -0.38268343236509d, 0.923879532511287d, -0.130526192220052d, 0.99144486137381d,
-            0.130526192220052d, 0.99144486137381d, 0.38268343236509d, 0.923879532511287d, 0.608761429008721d, 0.793353340291235d,
-            0.793353340291235d, 0.608761429008721d, 0.923879532511287d, 0.38268343236509d, 0.99144486137381d, 0.130526192220051d,
-            0.99144486137381d, -0.130526192220051d, 0.923879532511287d, -0.38268343236509d, 0.793353340291235d, -0.60876142900872d,
-            0.608761429008721d, -0.793353340291235d, 0.38268343236509d, -0.923879532511287d, 0.130526192220052d, -0.99144486137381d,
-            -0.130526192220052d, -0.99144486137381d, -0.38268343236509d, -0.923879532511287d, -0.608761429008721d, -0.793353340291235d,
-            -0.793353340291235d, -0.608761429008721d, -0.923879532511287d, -0.38268343236509d, -0.99144486137381d, -0.130526192220052d,
-            -0.99144486137381d, 0.130526192220051d, -0.923879532511287d, 0.38268343236509d, -0.793353340291235d, 0.608761429008721d,
-            -0.608761429008721d, 0.793353340291235d, -0.38268343236509d, 0.923879532511287d, -0.130526192220052d, 0.99144486137381d,
-            0.130526192220052d, 0.99144486137381d, 0.38268343236509d, 0.923879532511287d, 0.608761429008721d, 0.793353340291235d,
-            0.793353340291235d, 0.608761429008721d, 0.923879532511287d, 0.38268343236509d, 0.99144486137381d, 0.130526192220051d,
-            0.99144486137381d, -0.130526192220051d, 0.923879532511287d, -0.38268343236509d, 0.793353340291235d, -0.60876142900872d,
-            0.608761429008721d, -0.793353340291235d, 0.38268343236509d, -0.923879532511287d, 0.130526192220052d, -0.99144486137381d,
-            -0.130526192220052d, -0.99144486137381d, -0.38268343236509d, -0.923879532511287d, -0.608761429008721d, -0.793353340291235d,
-            -0.793353340291235d, -0.608761429008721d, -0.923879532511287d, -0.38268343236509d, -0.99144486137381d, -0.130526192220052d,
-            -0.99144486137381d, 0.130526192220051d, -0.923879532511287d, 0.38268343236509d, -0.793353340291235d, 0.608761429008721d,
-            -0.608761429008721d, 0.793353340291235d, -0.38268343236509d, 0.923879532511287d, -0.130526192220052d, 0.99144486137381d,
-            0.130526192220052d, 0.99144486137381d, 0.38268343236509d, 0.923879532511287d, 0.608761429008721d, 0.793353340291235d,
-            0.793353340291235d, 0.608761429008721d, 0.923879532511287d, 0.38268343236509d, 0.99144486137381d, 0.130526192220051d,
-            0.99144486137381d, -0.130526192220051d, 0.923879532511287d, -0.38268343236509d, 0.793353340291235d, -0.60876142900872d,
-            0.608761429008721d, -0.793353340291235d, 0.38268343236509d, -0.923879532511287d, 0.130526192220052d, -0.99144486137381d,
-            -0.130526192220052d, -0.99144486137381d, -0.38268343236509d, -0.923879532511287d, -0.608761429008721d, -0.793353340291235d,
-            -0.793353340291235d, -0.608761429008721d, -0.923879532511287d, -0.38268343236509d, -0.99144486137381d, -0.130526192220052d,
-            -0.99144486137381d, 0.130526192220051d, -0.923879532511287d, 0.38268343236509d, -0.793353340291235d, 0.608761429008721d,
-            -0.608761429008721d, 0.793353340291235d, -0.38268343236509d, 0.923879532511287d, -0.130526192220052d, 0.99144486137381d,
-            0.130526192220052d, 0.99144486137381d, 0.38268343236509d, 0.923879532511287d, 0.608761429008721d, 0.793353340291235d,
-            0.793353340291235d, 0.608761429008721d, 0.923879532511287d, 0.38268343236509d, 0.99144486137381d, 0.130526192220051d,
-            0.99144486137381d, -0.130526192220051d, 0.923879532511287d, -0.38268343236509d, 0.793353340291235d, -0.60876142900872d,
-            0.608761429008721d, -0.793353340291235d, 0.38268343236509d, -0.923879532511287d, 0.130526192220052d, -0.99144486137381d,
-            -0.130526192220052d, -0.99144486137381d, -0.38268343236509d, -0.923879532511287d, -0.608761429008721d, -0.793353340291235d,
-            -0.793353340291235d, -0.608761429008721d, -0.923879532511287d, -0.38268343236509d, -0.99144486137381d, -0.130526192220052d,
-            -0.99144486137381d, 0.130526192220051d, -0.923879532511287d, 0.38268343236509d, -0.793353340291235d, 0.608761429008721d,
-            -0.608761429008721d, 0.793353340291235d, -0.38268343236509d, 0.923879532511287d, -0.130526192220052d, 0.99144486137381d,
-            0.38268343236509d, 0.923879532511287d, 0.923879532511287d, 0.38268343236509d, 0.923879532511287d, -0.38268343236509d,
-            0.38268343236509d, -0.923879532511287d, -0.38268343236509d, -0.923879532511287d, -0.923879532511287d, -0.38268343236509d,
-            -0.923879532511287d, 0.38268343236509d, -0.38268343236509d, 0.923879532511287d,
-            };
-    
+        0.130526192220052d, 0.99144486137381d, 0.38268343236509d, 0.923879532511287d, 0.608761429008721d, 0.793353340291235d,
+        0.793353340291235d, 0.608761429008721d, 0.923879532511287d, 0.38268343236509d, 0.99144486137381d, 0.130526192220051d,
+        0.99144486137381d, -0.130526192220051d, 0.923879532511287d, -0.38268343236509d, 0.793353340291235d, -0.60876142900872d,
+        0.608761429008721d, -0.793353340291235d, 0.38268343236509d, -0.923879532511287d, 0.130526192220052d, -0.99144486137381d,
+        -0.130526192220052d, -0.99144486137381d, -0.38268343236509d, -0.923879532511287d, -0.608761429008721d, -0.793353340291235d,
+        -0.793353340291235d, -0.608761429008721d, -0.923879532511287d, -0.38268343236509d, -0.99144486137381d, -0.130526192220052d,
+        -0.99144486137381d, 0.130526192220051d, -0.923879532511287d, 0.38268343236509d, -0.793353340291235d, 0.608761429008721d,
+        -0.608761429008721d, 0.793353340291235d, -0.38268343236509d, 0.923879532511287d, -0.130526192220052d, 0.99144486137381d,
+        0.130526192220052d, 0.99144486137381d, 0.38268343236509d, 0.923879532511287d, 0.608761429008721d, 0.793353340291235d,
+        0.793353340291235d, 0.608761429008721d, 0.923879532511287d, 0.38268343236509d, 0.99144486137381d, 0.130526192220051d,
+        0.99144486137381d, -0.130526192220051d, 0.923879532511287d, -0.38268343236509d, 0.793353340291235d, -0.60876142900872d,
+        0.608761429008721d, -0.793353340291235d, 0.38268343236509d, -0.923879532511287d, 0.130526192220052d, -0.99144486137381d,
+        -0.130526192220052d, -0.99144486137381d, -0.38268343236509d, -0.923879532511287d, -0.608761429008721d, -0.793353340291235d,
+        -0.793353340291235d, -0.608761429008721d, -0.923879532511287d, -0.38268343236509d, -0.99144486137381d, -0.130526192220052d,
+        -0.99144486137381d, 0.130526192220051d, -0.923879532511287d, 0.38268343236509d, -0.793353340291235d, 0.608761429008721d,
+        -0.608761429008721d, 0.793353340291235d, -0.38268343236509d, 0.923879532511287d, -0.130526192220052d, 0.99144486137381d,
+        0.130526192220052d, 0.99144486137381d, 0.38268343236509d, 0.923879532511287d, 0.608761429008721d, 0.793353340291235d,
+        0.793353340291235d, 0.608761429008721d, 0.923879532511287d, 0.38268343236509d, 0.99144486137381d, 0.130526192220051d,
+        0.99144486137381d, -0.130526192220051d, 0.923879532511287d, -0.38268343236509d, 0.793353340291235d, -0.60876142900872d,
+        0.608761429008721d, -0.793353340291235d, 0.38268343236509d, -0.923879532511287d, 0.130526192220052d, -0.99144486137381d,
+        -0.130526192220052d, -0.99144486137381d, -0.38268343236509d, -0.923879532511287d, -0.608761429008721d, -0.793353340291235d,
+        -0.793353340291235d, -0.608761429008721d, -0.923879532511287d, -0.38268343236509d, -0.99144486137381d, -0.130526192220052d,
+        -0.99144486137381d, 0.130526192220051d, -0.923879532511287d, 0.38268343236509d, -0.793353340291235d, 0.608761429008721d,
+        -0.608761429008721d, 0.793353340291235d, -0.38268343236509d, 0.923879532511287d, -0.130526192220052d, 0.99144486137381d,
+        0.130526192220052d, 0.99144486137381d, 0.38268343236509d, 0.923879532511287d, 0.608761429008721d, 0.793353340291235d,
+        0.793353340291235d, 0.608761429008721d, 0.923879532511287d, 0.38268343236509d, 0.99144486137381d, 0.130526192220051d,
+        0.99144486137381d, -0.130526192220051d, 0.923879532511287d, -0.38268343236509d, 0.793353340291235d, -0.60876142900872d,
+        0.608761429008721d, -0.793353340291235d, 0.38268343236509d, -0.923879532511287d, 0.130526192220052d, -0.99144486137381d,
+        -0.130526192220052d, -0.99144486137381d, -0.38268343236509d, -0.923879532511287d, -0.608761429008721d, -0.793353340291235d,
+        -0.793353340291235d, -0.608761429008721d, -0.923879532511287d, -0.38268343236509d, -0.99144486137381d, -0.130526192220052d,
+        -0.99144486137381d, 0.130526192220051d, -0.923879532511287d, 0.38268343236509d, -0.793353340291235d, 0.608761429008721d,
+        -0.608761429008721d, 0.793353340291235d, -0.38268343236509d, 0.923879532511287d, -0.130526192220052d, 0.99144486137381d,
+        0.130526192220052d, 0.99144486137381d, 0.38268343236509d, 0.923879532511287d, 0.608761429008721d, 0.793353340291235d,
+        0.793353340291235d, 0.608761429008721d, 0.923879532511287d, 0.38268343236509d, 0.99144486137381d, 0.130526192220051d,
+        0.99144486137381d, -0.130526192220051d, 0.923879532511287d, -0.38268343236509d, 0.793353340291235d, -0.60876142900872d,
+        0.608761429008721d, -0.793353340291235d, 0.38268343236509d, -0.923879532511287d, 0.130526192220052d, -0.99144486137381d,
+        -0.130526192220052d, -0.99144486137381d, -0.38268343236509d, -0.923879532511287d, -0.608761429008721d, -0.793353340291235d,
+        -0.793353340291235d, -0.608761429008721d, -0.923879532511287d, -0.38268343236509d, -0.99144486137381d, -0.130526192220052d,
+        -0.99144486137381d, 0.130526192220051d, -0.923879532511287d, 0.38268343236509d, -0.793353340291235d, 0.608761429008721d,
+        -0.608761429008721d, 0.793353340291235d, -0.38268343236509d, 0.923879532511287d, -0.130526192220052d, 0.99144486137381d,
+        0.38268343236509d, 0.923879532511287d, 0.923879532511287d, 0.38268343236509d, 0.923879532511287d, -0.38268343236509d,
+        0.38268343236509d, -0.923879532511287d, -0.38268343236509d, -0.923879532511287d, -0.923879532511287d, -0.38268343236509d,
+        -0.923879532511287d, 0.38268343236509d, -0.38268343236509d, 0.923879532511287d,
+        };
+
     protected static final double[] GRADIENTS_3D = {
-            0, 1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0,
-            1, 0, 1, 0, -1, 0, 1, 0, 1, 0, -1, 0, -1, 0, -1, 0,
-            1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0,
-            0, 1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0,
-            1, 0, 1, 0, -1, 0, 1, 0, 1, 0, -1, 0, -1, 0, -1, 0,
-            1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0,
-            0, 1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0,
-            1, 0, 1, 0, -1, 0, 1, 0, 1, 0, -1, 0, -1, 0, -1, 0,
-            1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0,
-            0, 1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0,
-            1, 0, 1, 0, -1, 0, 1, 0, 1, 0, -1, 0, -1, 0, -1, 0,
-            1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0,
-            0, 1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0,
-            1, 0, 1, 0, -1, 0, 1, 0, 1, 0, -1, 0, -1, 0, -1, 0,
-            1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0,
-            1, 1, 0, 0, 0, -1, 1, 0, -1, 1, 0, 0, 0, -1, -1, 0
+        0, 1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0,
+        1, 0, 1, 0, -1, 0, 1, 0, 1, 0, -1, 0, -1, 0, -1, 0,
+        1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0,
+        0, 1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0,
+        1, 0, 1, 0, -1, 0, 1, 0, 1, 0, -1, 0, -1, 0, -1, 0,
+        1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0,
+        0, 1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0,
+        1, 0, 1, 0, -1, 0, 1, 0, 1, 0, -1, 0, -1, 0, -1, 0,
+        1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0,
+        0, 1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0,
+        1, 0, 1, 0, -1, 0, 1, 0, 1, 0, -1, 0, -1, 0, -1, 0,
+        1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0,
+        0, 1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0,
+        1, 0, 1, 0, -1, 0, 1, 0, 1, 0, -1, 0, -1, 0, -1, 0,
+        1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 0, 0, -1, -1, 0, 0,
+        1, 1, 0, 0, 0, -1, 1, 0, -1, 1, 0, 0, 0, -1, -1, 0
     };
-    
+
     protected static double gradCoord(int seed, int xPrimed, int yPrimed, double xd, double yd) {
         int hash = hash(seed, xPrimed, yPrimed);
         hash ^= hash >> 15;
         hash &= 127 << 1;
-        
+
         double xg = GRADIENTS_2_D[hash];
         double yg = GRADIENTS_2_D[hash | 1];
-        
+
         return xd * xg + yd * yg;
     }
-    
+
     protected static double gradCoord(int seed, int xPrimed, int yPrimed, int zPrimed, double xd, double yd, double zd) {
         int hash = hash(seed, xPrimed, yPrimed, zPrimed);
         hash ^= hash >> 15;
         hash &= 63 << 2;
-        
+
         double xg = GRADIENTS_3D[hash];
         double yg = GRADIENTS_3D[hash | 1];
         double zg = GRADIENTS_3D[hash | 2];
-        
+
         return xd * xg + yd * yg + zd * zg;
     }
 }

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

@@ -16,10 +16,10 @@ public class ValueCubicSampler extends ValueStyleNoise {
         int seed = (int) sl;
         int x1 = (int) Math.floor(x);
         int y1 = (int) Math.floor(y);
-        
+
         double xs = x - x1;
         double ys = y - y1;
-        
+
         x1 *= PRIME_X;
         y1 *= PRIME_Y;
         int x0 = x1 - PRIME_X;
@@ -28,34 +28,34 @@ public class ValueCubicSampler extends ValueStyleNoise {
         int y2 = y1 + PRIME_Y;
         int x3 = x1 + (PRIME_X << 1);
         int y3 = y1 + (PRIME_Y << 1);
-        
+
         return MathUtil.cubicLerp(
-                MathUtil.cubicLerp(valCoord(seed, x0, y0), valCoord(seed, x1, y0), valCoord(seed, x2, y0), valCoord(seed, x3, y0),
-                                   xs),
-                MathUtil.cubicLerp(valCoord(seed, x0, y1), valCoord(seed, x1, y1), valCoord(seed, x2, y1), valCoord(seed, x3, y1),
-                                   xs),
-                MathUtil.cubicLerp(valCoord(seed, x0, y2), valCoord(seed, x1, y2), valCoord(seed, x2, y2), valCoord(seed, x3, y2),
-                                   xs),
-                MathUtil.cubicLerp(valCoord(seed, x0, y3), valCoord(seed, x1, y3), valCoord(seed, x2, y3), valCoord(seed, x3, y3),
-                                   xs),
-                ys) * (1 / (1.5 * 1.5));
+            MathUtil.cubicLerp(valCoord(seed, x0, y0), valCoord(seed, x1, y0), valCoord(seed, x2, y0), valCoord(seed, x3, y0),
+                xs),
+            MathUtil.cubicLerp(valCoord(seed, x0, y1), valCoord(seed, x1, y1), valCoord(seed, x2, y1), valCoord(seed, x3, y1),
+                xs),
+            MathUtil.cubicLerp(valCoord(seed, x0, y2), valCoord(seed, x1, y2), valCoord(seed, x2, y2), valCoord(seed, x3, y2),
+                xs),
+            MathUtil.cubicLerp(valCoord(seed, x0, y3), valCoord(seed, x1, y3), valCoord(seed, x2, y3), valCoord(seed, x3, y3),
+                xs),
+            ys) * (1 / (1.5 * 1.5));
     }
-    
+
     @Override
     public double getNoiseRaw(long sl, double x, double y, double z) {
         int seed = (int) sl;
         int x1 = (int) Math.floor(x);
         int y1 = (int) Math.floor(y);
         int z1 = (int) Math.floor(z);
-        
+
         double xs = x - x1;
         double ys = y - y1;
         double zs = z - z1;
-        
+
         x1 *= PRIME_X;
         y1 *= PRIME_Y;
         z1 *= PRIME_Z;
-        
+
         int x0 = x1 - PRIME_X;
         int y0 = y1 - PRIME_Y;
         int z0 = z1 - PRIME_Z;
@@ -65,48 +65,48 @@ public class ValueCubicSampler extends ValueStyleNoise {
         int x3 = x1 + (PRIME_X << 1);
         int y3 = y1 + (PRIME_Y << 1);
         int z3 = z1 + (PRIME_Z << 1);
-        
+
         return MathUtil.cubicLerp(
-                MathUtil.cubicLerp(
-                        MathUtil.cubicLerp(valCoord(seed, x0, y0, z0), valCoord(seed, x1, y0, z0), valCoord(seed, x2, y0, z0),
-                                           valCoord(seed, x3, y0, z0), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y1, z0), valCoord(seed, x1, y1, z0), valCoord(seed, x2, y1, z0),
-                                           valCoord(seed, x3, y1, z0), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y2, z0), valCoord(seed, x1, y2, z0), valCoord(seed, x2, y2, z0),
-                                           valCoord(seed, x3, y2, z0), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y3, z0), valCoord(seed, x1, y3, z0), valCoord(seed, x2, y3, z0),
-                                           valCoord(seed, x3, y3, z0), xs),
-                        ys),
-                MathUtil.cubicLerp(
-                        MathUtil.cubicLerp(valCoord(seed, x0, y0, z1), valCoord(seed, x1, y0, z1), valCoord(seed, x2, y0, z1),
-                                           valCoord(seed, x3, y0, z1), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y1, z1), valCoord(seed, x1, y1, z1), valCoord(seed, x2, y1, z1),
-                                           valCoord(seed, x3, y1, z1), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y2, z1), valCoord(seed, x1, y2, z1), valCoord(seed, x2, y2, z1),
-                                           valCoord(seed, x3, y2, z1), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y3, z1), valCoord(seed, x1, y3, z1), valCoord(seed, x2, y3, z1),
-                                           valCoord(seed, x3, y3, z1), xs),
-                        ys),
-                MathUtil.cubicLerp(
-                        MathUtil.cubicLerp(valCoord(seed, x0, y0, z2), valCoord(seed, x1, y0, z2), valCoord(seed, x2, y0, z2),
-                                           valCoord(seed, x3, y0, z2), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y1, z2), valCoord(seed, x1, y1, z2), valCoord(seed, x2, y1, z2),
-                                           valCoord(seed, x3, y1, z2), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y2, z2), valCoord(seed, x1, y2, z2), valCoord(seed, x2, y2, z2),
-                                           valCoord(seed, x3, y2, z2), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y3, z2), valCoord(seed, x1, y3, z2), valCoord(seed, x2, y3, z2),
-                                           valCoord(seed, x3, y3, z2), xs),
-                        ys),
-                MathUtil.cubicLerp(
-                        MathUtil.cubicLerp(valCoord(seed, x0, y0, z3), valCoord(seed, x1, y0, z3), valCoord(seed, x2, y0, z3),
-                                           valCoord(seed, x3, y0, z3), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y1, z3), valCoord(seed, x1, y1, z3), valCoord(seed, x2, y1, z3),
-                                           valCoord(seed, x3, y1, z3), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y2, z3), valCoord(seed, x1, y2, z3), valCoord(seed, x2, y2, z3),
-                                           valCoord(seed, x3, y2, z3), xs),
-                        MathUtil.cubicLerp(valCoord(seed, x0, y3, z3), valCoord(seed, x1, y3, z3), valCoord(seed, x2, y3, z3),
-                                           valCoord(seed, x3, y3, z3), xs),
-                        ys),
-                zs) * (1 / (1.5 * 1.5 * 1.5));
+            MathUtil.cubicLerp(
+                MathUtil.cubicLerp(valCoord(seed, x0, y0, z0), valCoord(seed, x1, y0, z0), valCoord(seed, x2, y0, z0),
+                    valCoord(seed, x3, y0, z0), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y1, z0), valCoord(seed, x1, y1, z0), valCoord(seed, x2, y1, z0),
+                    valCoord(seed, x3, y1, z0), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y2, z0), valCoord(seed, x1, y2, z0), valCoord(seed, x2, y2, z0),
+                    valCoord(seed, x3, y2, z0), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y3, z0), valCoord(seed, x1, y3, z0), valCoord(seed, x2, y3, z0),
+                    valCoord(seed, x3, y3, z0), xs),
+                ys),
+            MathUtil.cubicLerp(
+                MathUtil.cubicLerp(valCoord(seed, x0, y0, z1), valCoord(seed, x1, y0, z1), valCoord(seed, x2, y0, z1),
+                    valCoord(seed, x3, y0, z1), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y1, z1), valCoord(seed, x1, y1, z1), valCoord(seed, x2, y1, z1),
+                    valCoord(seed, x3, y1, z1), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y2, z1), valCoord(seed, x1, y2, z1), valCoord(seed, x2, y2, z1),
+                    valCoord(seed, x3, y2, z1), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y3, z1), valCoord(seed, x1, y3, z1), valCoord(seed, x2, y3, z1),
+                    valCoord(seed, x3, y3, z1), xs),
+                ys),
+            MathUtil.cubicLerp(
+                MathUtil.cubicLerp(valCoord(seed, x0, y0, z2), valCoord(seed, x1, y0, z2), valCoord(seed, x2, y0, z2),
+                    valCoord(seed, x3, y0, z2), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y1, z2), valCoord(seed, x1, y1, z2), valCoord(seed, x2, y1, z2),
+                    valCoord(seed, x3, y1, z2), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y2, z2), valCoord(seed, x1, y2, z2), valCoord(seed, x2, y2, z2),
+                    valCoord(seed, x3, y2, z2), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y3, z2), valCoord(seed, x1, y3, z2), valCoord(seed, x2, y3, z2),
+                    valCoord(seed, x3, y3, z2), xs),
+                ys),
+            MathUtil.cubicLerp(
+                MathUtil.cubicLerp(valCoord(seed, x0, y0, z3), valCoord(seed, x1, y0, z3), valCoord(seed, x2, y0, z3),
+                    valCoord(seed, x3, y0, z3), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y1, z3), valCoord(seed, x1, y1, z3), valCoord(seed, x2, y1, z3),
+                    valCoord(seed, x3, y1, z3), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y2, z3), valCoord(seed, x1, y2, z3), valCoord(seed, x2, y2, z3),
+                    valCoord(seed, x3, y2, z3), xs),
+                MathUtil.cubicLerp(valCoord(seed, x0, y3, z3), valCoord(seed, x1, y3, z3), valCoord(seed, x2, y3, z3),
+                    valCoord(seed, x3, y3, z3), xs),
+                ys),
+            zs) * (1 / (1.5 * 1.5 * 1.5));
     }
 }

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

@@ -16,47 +16,47 @@ public class ValueSampler extends ValueStyleNoise {
         int seed = (int) sl;
         int x0 = (int) Math.floor(x);
         int y0 = (int) Math.floor(y);
-        
+
         double xs = MathUtil.interpHermite(x - x0);
         double ys = MathUtil.interpHermite(y - y0);
-        
+
         x0 *= PRIME_X;
         y0 *= PRIME_Y;
         int x1 = x0 + PRIME_X;
         int y1 = y0 + PRIME_Y;
-        
+
         double xf0 = MathUtil.lerp(valCoord(seed, x0, y0), valCoord(seed, x1, y0), xs);
         double xf1 = MathUtil.lerp(valCoord(seed, x0, y1), valCoord(seed, x1, y1), xs);
-        
+
         return MathUtil.lerp(xf0, xf1, ys);
     }
-    
+
     @Override
     public double getNoiseRaw(long sl, double x, double y, double z) {
         int seed = (int) sl;
         int x0 = (int) Math.floor(x);
         int y0 = (int) Math.floor(y);
         int z0 = (int) Math.floor(z);
-        
+
         double xs = MathUtil.interpHermite(x - x0);
         double ys = MathUtil.interpHermite(y - y0);
         double zs = MathUtil.interpHermite(z - z0);
-        
+
         x0 *= PRIME_X;
         y0 *= PRIME_Y;
         z0 *= PRIME_Z;
         int x1 = x0 + PRIME_X;
         int y1 = y0 + PRIME_Y;
         int z1 = z0 + PRIME_Z;
-        
+
         double xf00 = MathUtil.lerp(valCoord(seed, x0, y0, z0), valCoord(seed, x1, y0, z0), xs);
         double xf10 = MathUtil.lerp(valCoord(seed, x0, y1, z0), valCoord(seed, x1, y1, z0), xs);
         double xf01 = MathUtil.lerp(valCoord(seed, x0, y0, z1), valCoord(seed, x1, y0, z1), xs);
         double xf11 = MathUtil.lerp(valCoord(seed, x0, y1, z1), valCoord(seed, x1, y1, z1), xs);
-        
+
         double yf0 = MathUtil.lerp(xf00, xf10, ys);
         double yf1 = MathUtil.lerp(xf01, xf11, ys);
-        
+
         return MathUtil.lerp(yf0, yf1, zs);
     }
 }

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

@@ -11,18 +11,18 @@ import com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction;
 
 
 public abstract class ValueStyleNoise extends NoiseFunction {
-    
+
     protected static double valCoord(int seed, int xPrimed, int yPrimed) {
         int hash = hash(seed, xPrimed, yPrimed);
-        
+
         hash *= hash;
         hash ^= hash << 19;
         return hash * (1 / 2147483648.0);
     }
-    
+
     protected static double valCoord(int seed, int xPrimed, int yPrimed, int zPrimed) {
         int hash = hash(seed, xPrimed, yPrimed, zPrimed);
-        
+
         hash *= hash;
         hash ^= hash << 19;
         return hash * (1 / 2147483648.0);