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3
common/addons/config-noise-function/README.md
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3
common/addons/config-noise-function/README.md
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# config-noise-function
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Registers the default noise functions.
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2
common/addons/config-noise-function/build.gradle.kts
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2
common/addons/config-noise-function/build.gradle.kts
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dependencies {
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}
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package com.dfsek.terra.addons.noise;
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import com.dfsek.tectonic.loading.object.ObjectTemplate;
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import java.util.HashMap;
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import java.util.LinkedHashMap;
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import java.util.Map;
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import java.util.function.Supplier;
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import com.dfsek.terra.addons.noise.config.DimensionApplicableNoiseSampler;
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import com.dfsek.terra.addons.noise.config.templates.DomainWarpTemplate;
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import com.dfsek.terra.addons.noise.config.templates.FunctionTemplate;
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import com.dfsek.terra.addons.noise.config.templates.ImageSamplerTemplate;
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import com.dfsek.terra.addons.noise.config.templates.KernelTemplate;
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import com.dfsek.terra.addons.noise.config.templates.noise.CellularNoiseTemplate;
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import com.dfsek.terra.addons.noise.config.templates.noise.ConstantNoiseTemplate;
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import com.dfsek.terra.addons.noise.config.templates.noise.ExpressionFunctionTemplate;
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import com.dfsek.terra.addons.noise.config.templates.noise.GaborNoiseTemplate;
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import com.dfsek.terra.addons.noise.config.templates.noise.SimpleNoiseTemplate;
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import com.dfsek.terra.addons.noise.config.templates.noise.fractal.BrownianMotionTemplate;
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import com.dfsek.terra.addons.noise.config.templates.noise.fractal.PingPongTemplate;
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import com.dfsek.terra.addons.noise.config.templates.noise.fractal.RidgedFractalTemplate;
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import com.dfsek.terra.addons.noise.config.templates.normalizer.ClampNormalizerTemplate;
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import com.dfsek.terra.addons.noise.config.templates.normalizer.LinearNormalizerTemplate;
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import com.dfsek.terra.addons.noise.config.templates.normalizer.NormalNormalizerTemplate;
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import com.dfsek.terra.addons.noise.samplers.noise.CellularSampler;
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import com.dfsek.terra.addons.noise.samplers.noise.random.GaussianNoiseSampler;
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import com.dfsek.terra.addons.noise.samplers.noise.random.WhiteNoiseSampler;
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import com.dfsek.terra.addons.noise.samplers.noise.simplex.OpenSimplex2SSampler;
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import com.dfsek.terra.addons.noise.samplers.noise.simplex.OpenSimplex2Sampler;
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import com.dfsek.terra.addons.noise.samplers.noise.simplex.PerlinSampler;
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import com.dfsek.terra.addons.noise.samplers.noise.simplex.SimplexSampler;
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import com.dfsek.terra.addons.noise.samplers.noise.value.ValueCubicSampler;
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import com.dfsek.terra.addons.noise.samplers.noise.value.ValueSampler;
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import com.dfsek.terra.api.Platform;
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import com.dfsek.terra.api.addon.TerraAddon;
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import com.dfsek.terra.api.addon.annotations.Addon;
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import com.dfsek.terra.api.addon.annotations.Author;
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import com.dfsek.terra.api.addon.annotations.Version;
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import com.dfsek.terra.api.event.events.config.pack.ConfigPackPreLoadEvent;
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import com.dfsek.terra.api.event.functional.FunctionalEventHandler;
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import com.dfsek.terra.api.inject.annotations.Inject;
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import com.dfsek.terra.api.noise.NoiseSampler;
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import com.dfsek.terra.api.registry.CheckedRegistry;
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import com.dfsek.terra.api.util.reflection.TypeKey;
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@Addon("config-noise-function")
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@Author("Terra")
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@Version("1.0.0")
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public class NoiseAddon extends TerraAddon {
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public static final TypeKey<Supplier<ObjectTemplate<NoiseSampler>>> NOISE_SAMPLER_TOKEN = new TypeKey<>() {
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};
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@Inject
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private Platform plugin;
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@Override
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public void initialize() {
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plugin.getEventManager()
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.getHandler(FunctionalEventHandler.class)
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.register(this, ConfigPackPreLoadEvent.class)
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.then(event -> {
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CheckedRegistry<Supplier<ObjectTemplate<NoiseSampler>>> noiseRegistry = event.getPack().getOrCreateRegistry(
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NOISE_SAMPLER_TOKEN);
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event.getPack()
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.applyLoader(CellularSampler.DistanceFunction.class,
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(t, o, l) -> CellularSampler.DistanceFunction.valueOf((String) o))
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.applyLoader(CellularSampler.ReturnType.class, (t, o, l) -> CellularSampler.ReturnType.valueOf((String) o))
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.applyLoader(DimensionApplicableNoiseSampler.class, DimensionApplicableNoiseSampler::new)
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.applyLoader(FunctionTemplate.class, FunctionTemplate::new);
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noiseRegistry.register("LINEAR", LinearNormalizerTemplate::new);
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noiseRegistry.register("NORMAL", NormalNormalizerTemplate::new);
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noiseRegistry.register("CLAMP", ClampNormalizerTemplate::new);
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noiseRegistry.register("IMAGE", ImageSamplerTemplate::new);
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noiseRegistry.register("DOMAIN_WARP", DomainWarpTemplate::new);
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noiseRegistry.register("FBM", BrownianMotionTemplate::new);
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noiseRegistry.register("PING_PONG", PingPongTemplate::new);
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noiseRegistry.register("RIDGED", RidgedFractalTemplate::new);
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noiseRegistry.register("OPEN_SIMPLEX_2", () -> new SimpleNoiseTemplate(OpenSimplex2Sampler::new));
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noiseRegistry.register("OPEN_SIMPLEX_2S", () -> new SimpleNoiseTemplate(OpenSimplex2SSampler::new));
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noiseRegistry.register("PERLIN", () -> new SimpleNoiseTemplate(PerlinSampler::new));
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noiseRegistry.register("SIMPLEX", () -> new SimpleNoiseTemplate(SimplexSampler::new));
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noiseRegistry.register("GABOR", GaborNoiseTemplate::new);
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noiseRegistry.register("VALUE", () -> new SimpleNoiseTemplate(ValueSampler::new));
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noiseRegistry.register("VALUE_CUBIC", () -> new SimpleNoiseTemplate(ValueCubicSampler::new));
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noiseRegistry.register("CELLULAR", CellularNoiseTemplate::new);
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noiseRegistry.register("WHITE_NOISE", () -> new SimpleNoiseTemplate(WhiteNoiseSampler::new));
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noiseRegistry.register("GAUSSIAN", () -> new SimpleNoiseTemplate(GaussianNoiseSampler::new));
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noiseRegistry.register("CONSTANT", ConstantNoiseTemplate::new);
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noiseRegistry.register("KERNEL", KernelTemplate::new);
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Map<String, DimensionApplicableNoiseSampler> packSamplers = new LinkedHashMap<>();
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Map<String, FunctionTemplate> packFunctions = new LinkedHashMap<>();
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noiseRegistry.register("EXPRESSION", () -> new ExpressionFunctionTemplate(packSamplers, packFunctions));
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NoiseConfigPackTemplate template = new NoiseConfigPackTemplate();
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event.loadTemplate(template);
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packSamplers.putAll(template.getSamplers());
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packFunctions.putAll(template.getFunctions());
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})
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.failThrough();
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}
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}
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package com.dfsek.terra.addons.noise;
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import com.dfsek.tectonic.annotations.Default;
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import com.dfsek.tectonic.annotations.Value;
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import com.dfsek.tectonic.config.ConfigTemplate;
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import java.util.LinkedHashMap;
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import java.util.Map;
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import com.dfsek.terra.addons.noise.config.DimensionApplicableNoiseSampler;
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import com.dfsek.terra.addons.noise.config.templates.FunctionTemplate;
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import com.dfsek.terra.api.config.meta.Meta;
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public class NoiseConfigPackTemplate implements ConfigTemplate {
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@Value("noise")
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private @Meta Map<String, @Meta DimensionApplicableNoiseSampler> noiseBuilderMap;
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@Value("functions")
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@Default
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private @Meta LinkedHashMap<String, @Meta FunctionTemplate> expressions = new LinkedHashMap<>();
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public Map<String, DimensionApplicableNoiseSampler> getSamplers() {
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return noiseBuilderMap;
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}
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public LinkedHashMap<String, FunctionTemplate> getFunctions() {
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return expressions;
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}
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}
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package com.dfsek.terra.addons.noise.config;
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import com.dfsek.tectonic.annotations.Value;
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import com.dfsek.tectonic.loading.object.ObjectTemplate;
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import com.dfsek.terra.api.config.meta.Meta;
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import com.dfsek.terra.api.noise.NoiseSampler;
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public class DimensionApplicableNoiseSampler implements ObjectTemplate<DimensionApplicableNoiseSampler> {
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@Value("dimensions")
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private @Meta int dimensions;
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@Value(".")
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private @Meta NoiseSampler sampler;
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@Override
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public DimensionApplicableNoiseSampler get() {
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return this;
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}
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public int getDimensions() {
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return dimensions;
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}
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public NoiseSampler getSampler() {
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return sampler;
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}
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}
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package com.dfsek.terra.addons.noise.config.templates;
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import com.dfsek.tectonic.annotations.Default;
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import com.dfsek.tectonic.annotations.Value;
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import com.dfsek.terra.addons.noise.samplers.DomainWarpedSampler;
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import com.dfsek.terra.api.config.meta.Meta;
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import com.dfsek.terra.api.noise.NoiseSampler;
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@SuppressWarnings({ "unused", "FieldMayBeFinal" })
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public class DomainWarpTemplate extends SamplerTemplate<DomainWarpedSampler> {
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@Value("warp")
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private @Meta NoiseSampler warp;
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@Value("function")
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private @Meta NoiseSampler function;
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@Value("amplitude")
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@Default
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private @Meta double amplitude = 1;
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@Override
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public NoiseSampler get() {
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return new DomainWarpedSampler(function, warp, amplitude);
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}
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}
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package com.dfsek.terra.addons.noise.config.templates;
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import com.dfsek.tectonic.annotations.Value;
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import com.dfsek.tectonic.loading.object.ObjectTemplate;
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import java.util.List;
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import com.dfsek.terra.api.config.meta.Meta;
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@SuppressWarnings("unused")
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public class FunctionTemplate implements ObjectTemplate<FunctionTemplate> {
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@Value("arguments")
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private List<String> args;
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@Value("expression")
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private @Meta String function;
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@Override
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public FunctionTemplate get() {
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return this;
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}
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public List<String> getArgs() {
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return args;
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}
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public String getFunction() {
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return function;
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}
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}
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package com.dfsek.terra.addons.noise.config.templates;
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import com.dfsek.tectonic.annotations.Value;
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import java.awt.image.BufferedImage;
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import com.dfsek.terra.addons.noise.samplers.ImageSampler;
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import com.dfsek.terra.api.config.meta.Meta;
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import com.dfsek.terra.api.noise.NoiseSampler;
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@SuppressWarnings({ "unused", "FieldMayBeFinal" })
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public class ImageSamplerTemplate extends SamplerTemplate<ImageSampler> {
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@Value("image")
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private @Meta BufferedImage image;
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@Value("frequency")
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private @Meta double frequency;
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@Value("channel")
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private ImageSampler.@Meta Channel channel;
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@Override
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public NoiseSampler get() {
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return new ImageSampler(image, channel, frequency);
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}
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}
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package com.dfsek.terra.addons.noise.config.templates;
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import com.dfsek.tectonic.annotations.Default;
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import com.dfsek.tectonic.annotations.Value;
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import com.dfsek.tectonic.exception.ValidationException;
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import java.util.List;
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import com.dfsek.terra.addons.noise.samplers.KernelSampler;
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import com.dfsek.terra.api.config.meta.Meta;
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import com.dfsek.terra.api.noise.NoiseSampler;
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@SuppressWarnings({ "unused", "FieldMayBeFinal" })
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public class KernelTemplate extends SamplerTemplate<KernelSampler> {
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@Value("kernel")
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private @Meta List<@Meta List<@Meta Double>> kernel;
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@Value("factor")
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@Default
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private @Meta double factor = 1;
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@Value("function")
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private @Meta NoiseSampler function;
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@Value("frequency")
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@Default
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private @Meta double frequency = 1;
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@Override
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public NoiseSampler get() {
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double[][] k = new double[kernel.size()][kernel.get(0).size()];
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for(int x = 0; x < kernel.size(); x++) {
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for(int y = 0; y < kernel.get(x).size(); y++) {
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k[x][y] = kernel.get(x).get(y) * factor;
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}
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}
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KernelSampler sampler = new KernelSampler(k, function);
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sampler.setFrequency(frequency);
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return sampler;
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}
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|
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@Override
|
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public boolean validate() throws ValidationException {
|
||||
|
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if(kernel.isEmpty()) throw new ValidationException("Kernel must not be empty.");
|
||||
|
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int len = kernel.get(0).size();
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if(len == 0) throw new ValidationException("Kernel row must contain data.");
|
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|
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for(int i = 0; i < kernel.size(); i++) {
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if(kernel.get(i).size() != len)
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throw new ValidationException("Kernel row " + i + " size mismatch. Expected " + len + ", found " + kernel.get(i).size());
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}
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|
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return super.validate();
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}
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}
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@ -0,0 +1,28 @@
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package com.dfsek.terra.addons.noise.config.templates;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Default;
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
import com.dfsek.tectonic.config.ValidatedConfigTemplate;
|
||||
import com.dfsek.tectonic.exception.ValidationException;
|
||||
import com.dfsek.tectonic.loading.object.ObjectTemplate;
|
||||
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
@SuppressWarnings("FieldMayBeFinal")
|
||||
public abstract class SamplerTemplate<T extends NoiseSampler> implements ValidatedConfigTemplate, ObjectTemplate<NoiseSampler> {
|
||||
@Value("dimensions")
|
||||
@Default
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||||
private @Meta int dimensions = 2;
|
||||
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||||
@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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,42 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.noise;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Default;
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.CellularSampler;
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.simplex.OpenSimplex2Sampler;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
@SuppressWarnings("FieldMayBeFinal")
|
||||
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();
|
||||
sampler.setNoiseLookup(lookup);
|
||||
sampler.setFrequency(frequency);
|
||||
sampler.setJitterModifier(cellularJitter);
|
||||
sampler.setReturnType(cellularReturnType);
|
||||
sampler.setDistanceFunction(cellularDistanceFunction);
|
||||
sampler.setSalt(salt);
|
||||
return sampler;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,22 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.noise;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Default;
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
|
||||
import com.dfsek.terra.addons.noise.config.templates.SamplerTemplate;
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.ConstantSampler;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
@SuppressWarnings("FieldMayBeFinal")
|
||||
public class ConstantNoiseTemplate extends SamplerTemplate<ConstantSampler> {
|
||||
@Value("value")
|
||||
@Default
|
||||
private @Meta double value = 0d;
|
||||
|
||||
@Override
|
||||
public NoiseSampler get() {
|
||||
return new ConstantSampler(value);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,97 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.noise;
|
||||
|
||||
import com.dfsek.paralithic.eval.parser.Parser;
|
||||
import com.dfsek.paralithic.eval.parser.Scope;
|
||||
import com.dfsek.paralithic.eval.tokenizer.ParseException;
|
||||
import com.dfsek.paralithic.functions.Function;
|
||||
import com.dfsek.tectonic.annotations.Default;
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
import com.dfsek.tectonic.exception.ValidationException;
|
||||
|
||||
import java.util.HashMap;
|
||||
import java.util.LinkedHashMap;
|
||||
import java.util.Map;
|
||||
|
||||
import com.dfsek.terra.addons.noise.config.DimensionApplicableNoiseSampler;
|
||||
import com.dfsek.terra.addons.noise.config.templates.FunctionTemplate;
|
||||
import com.dfsek.terra.addons.noise.config.templates.SamplerTemplate;
|
||||
import com.dfsek.terra.addons.noise.paralithic.defined.UserDefinedFunction;
|
||||
import com.dfsek.terra.addons.noise.paralithic.noise.NoiseFunction2;
|
||||
import com.dfsek.terra.addons.noise.paralithic.noise.NoiseFunction3;
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.ExpressionFunction;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
@SuppressWarnings({ "FieldMayBeFinal", "unused" })
|
||||
public class ExpressionFunctionTemplate extends SamplerTemplate<ExpressionFunction> {
|
||||
private final Map<String, DimensionApplicableNoiseSampler> otherFunctions;
|
||||
@Value("variables")
|
||||
@Default
|
||||
private @Meta Map<String, @Meta Double> vars = new HashMap<>();
|
||||
|
||||
@Value("expression")
|
||||
private @Meta String equation;
|
||||
|
||||
@Value("samplers")
|
||||
@Default
|
||||
private @Meta LinkedHashMap<String, @Meta DimensionApplicableNoiseSampler> samplers = new LinkedHashMap<>();
|
||||
|
||||
@Value("functions")
|
||||
@Default
|
||||
private @Meta LinkedHashMap<String, @Meta FunctionTemplate> functions = new LinkedHashMap<>();
|
||||
|
||||
private final Map<String, FunctionTemplate> globalFunctions;
|
||||
|
||||
public ExpressionFunctionTemplate(Map<String, DimensionApplicableNoiseSampler> otherFunctions, Map<String, FunctionTemplate> samplers) {
|
||||
this.otherFunctions = otherFunctions;
|
||||
this.globalFunctions = samplers;
|
||||
}
|
||||
|
||||
@Override
|
||||
public NoiseSampler get() {
|
||||
try {
|
||||
Map<String, Function> noiseFunctionMap = generateFunctions();
|
||||
return new ExpressionFunction(noiseFunctionMap, equation, vars);
|
||||
} catch(ParseException e) {
|
||||
throw new IllegalStateException(e);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean validate() throws ValidationException {
|
||||
try {
|
||||
Map<String, Function> noiseFunctionMap = generateFunctions();
|
||||
new ExpressionFunction(noiseFunctionMap, equation, vars);
|
||||
} catch(ParseException e) {
|
||||
throw new ValidationException("Errors occurred while parsing noise equation: ", e);
|
||||
}
|
||||
return super.validate();
|
||||
}
|
||||
|
||||
private Map<String, Function> generateFunctions() throws ParseException {
|
||||
Map<String, Function> noiseFunctionMap = new HashMap<>();
|
||||
|
||||
for(Map.Entry<String, FunctionTemplate> entry : globalFunctions.entrySet()) {
|
||||
noiseFunctionMap.put(entry.getKey(), UserDefinedFunction.newInstance(entry.getValue(), new Parser(), new Scope()));
|
||||
}
|
||||
|
||||
for(Map.Entry<String, FunctionTemplate> entry : functions.entrySet()) {
|
||||
noiseFunctionMap.put(entry.getKey(), UserDefinedFunction.newInstance(entry.getValue(), new Parser(), new Scope()));
|
||||
}
|
||||
|
||||
otherFunctions.forEach((id, function) -> {
|
||||
if(function.getDimensions() == 2) {
|
||||
noiseFunctionMap.put(id, new NoiseFunction2(function.getSampler()));
|
||||
} else noiseFunctionMap.put(id, new NoiseFunction3(function.getSampler()));
|
||||
});
|
||||
|
||||
samplers.forEach((id, function) -> {
|
||||
if(function.getDimensions() == 2) {
|
||||
noiseFunctionMap.put(id, new NoiseFunction2(function.getSampler()));
|
||||
} else noiseFunctionMap.put(id, new NoiseFunction3(function.getSampler()));
|
||||
});
|
||||
|
||||
return noiseFunctionMap;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,44 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.noise;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Default;
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.GaborNoiseSampler;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
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();
|
||||
gaborNoiseSampler.setFrequency(frequency);
|
||||
gaborNoiseSampler.setRotation(rotation);
|
||||
gaborNoiseSampler.setIsotropic(isotropic);
|
||||
gaborNoiseSampler.setDeviation(deviation);
|
||||
gaborNoiseSampler.setImpulsesPerKernel(impulses);
|
||||
gaborNoiseSampler.setFrequency0(f0);
|
||||
gaborNoiseSampler.setSalt(salt);
|
||||
return gaborNoiseSampler;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,20 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.noise;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Default;
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
|
||||
import com.dfsek.terra.addons.noise.config.templates.SamplerTemplate;
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
|
||||
|
||||
@SuppressWarnings({ "unused", "FieldMayBeFinal" })
|
||||
public abstract class NoiseTemplate<T extends NoiseFunction> extends SamplerTemplate<T> {
|
||||
@Value("frequency")
|
||||
@Default
|
||||
protected @Meta double frequency = 0.02d;
|
||||
|
||||
@Value("salt")
|
||||
@Default
|
||||
protected @Meta long salt = 0;
|
||||
}
|
||||
@ -0,0 +1,23 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.noise;
|
||||
|
||||
import java.util.function.Supplier;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction;
|
||||
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();
|
||||
sampler.setFrequency(frequency);
|
||||
sampler.setSalt(salt);
|
||||
return sampler;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,17 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.noise.fractal;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.fractal.BrownianMotionSampler;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
public class BrownianMotionTemplate extends FractalTemplate<BrownianMotionSampler> {
|
||||
@Override
|
||||
public NoiseSampler get() {
|
||||
BrownianMotionSampler sampler = new BrownianMotionSampler(function);
|
||||
sampler.setGain(fractalGain);
|
||||
sampler.setLacunarity(fractalLacunarity);
|
||||
sampler.setOctaves(octaves);
|
||||
sampler.setWeightedStrength(weightedStrength);
|
||||
return sampler;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,31 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.noise.fractal;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Default;
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
|
||||
import com.dfsek.terra.addons.noise.config.templates.SamplerTemplate;
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.fractal.FractalNoiseFunction;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
public abstract class FractalTemplate<T extends FractalNoiseFunction> extends SamplerTemplate<T> {
|
||||
@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("function")
|
||||
protected @Meta NoiseSampler function;
|
||||
}
|
||||
@ -0,0 +1,27 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.noise.fractal;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Default;
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.fractal.PingPongSampler;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
@SuppressWarnings({ "unused", "FieldMayBeFinal" })
|
||||
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);
|
||||
sampler.setGain(fractalGain);
|
||||
sampler.setLacunarity(fractalLacunarity);
|
||||
sampler.setOctaves(octaves);
|
||||
sampler.setWeightedStrength(weightedStrength);
|
||||
sampler.setPingPongStrength(pingPong);
|
||||
return sampler;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,17 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.noise.fractal;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.fractal.RidgedFractalSampler;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
public class RidgedFractalTemplate extends FractalTemplate<RidgedFractalSampler> {
|
||||
@Override
|
||||
public NoiseSampler get() {
|
||||
RidgedFractalSampler sampler = new RidgedFractalSampler(function);
|
||||
sampler.setGain(fractalGain);
|
||||
sampler.setLacunarity(fractalLacunarity);
|
||||
sampler.setOctaves(octaves);
|
||||
sampler.setWeightedStrength(weightedStrength);
|
||||
return sampler;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,22 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.normalizer;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
|
||||
import com.dfsek.terra.addons.noise.normalizer.ClampNormalizer;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
@SuppressWarnings({ "unused", "FieldMayBeFinal" })
|
||||
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);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,22 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.normalizer;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
|
||||
import com.dfsek.terra.addons.noise.normalizer.LinearNormalizer;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
@SuppressWarnings({ "unused", "FieldMayBeFinal" })
|
||||
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);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,27 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.normalizer;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Default;
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
|
||||
import com.dfsek.terra.addons.noise.normalizer.NormalNormalizer;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
@SuppressWarnings({ "unused", "FieldMayBeFinal" })
|
||||
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);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,14 @@
|
||||
package com.dfsek.terra.addons.noise.config.templates.normalizer;
|
||||
|
||||
import com.dfsek.tectonic.annotations.Value;
|
||||
|
||||
import com.dfsek.terra.addons.noise.config.templates.SamplerTemplate;
|
||||
import com.dfsek.terra.addons.noise.normalizer.Normalizer;
|
||||
import com.dfsek.terra.api.config.meta.Meta;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
public abstract class NormalizerTemplate<T extends Normalizer> extends SamplerTemplate<T> {
|
||||
@Value("function")
|
||||
protected @Meta NoiseSampler function;
|
||||
}
|
||||
@ -0,0 +1,22 @@
|
||||
package com.dfsek.terra.addons.noise.normalizer;
|
||||
|
||||
import net.jafama.FastMath;
|
||||
|
||||
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 FastMath.max(FastMath.min(in, max), min);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,23 @@
|
||||
package com.dfsek.terra.addons.noise.normalizer;
|
||||
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
/**
|
||||
* Normalizer to linearly scale data's range.
|
||||
*/
|
||||
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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,47 @@
|
||||
package com.dfsek.terra.addons.noise.normalizer;
|
||||
|
||||
import net.jafama.FastMath;
|
||||
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
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;
|
||||
int end = lookup.length - 1;
|
||||
while(start + 1 < end) {
|
||||
int mid = start + (end - start) / 2;
|
||||
if(lookup[mid] <= in) {
|
||||
start = mid;
|
||||
} else {
|
||||
end = mid;
|
||||
}
|
||||
}
|
||||
double left = FastMath.abs(lookup[start] - in);
|
||||
double right = FastMath.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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,24 @@
|
||||
package com.dfsek.terra.addons.noise.normalizer;
|
||||
|
||||
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 getNoiseSeeded(long seed, double x, double y) {
|
||||
return normalize(sampler.getNoiseSeeded(seed, x, y));
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getNoiseSeeded(long seed, double x, double y, double z) {
|
||||
return normalize(sampler.getNoiseSeeded(seed, x, y, z));
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,52 @@
|
||||
package com.dfsek.terra.addons.noise.paralithic.defined;
|
||||
|
||||
import com.dfsek.paralithic.Expression;
|
||||
import com.dfsek.paralithic.eval.parser.Parser;
|
||||
import com.dfsek.paralithic.eval.parser.Scope;
|
||||
import com.dfsek.paralithic.eval.tokenizer.ParseException;
|
||||
import com.dfsek.paralithic.functions.dynamic.Context;
|
||||
import com.dfsek.paralithic.functions.dynamic.DynamicFunction;
|
||||
|
||||
import com.dfsek.paralithic.node.Statefulness;
|
||||
|
||||
import com.dfsek.terra.addons.noise.config.templates.FunctionTemplate;
|
||||
|
||||
|
||||
public class UserDefinedFunction implements DynamicFunction {
|
||||
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, Parser parser, Scope parent) throws ParseException {
|
||||
|
||||
Scope functionScope = new Scope().withParent(parent);
|
||||
|
||||
template.getArgs().forEach(functionScope::addInvocationVariable);
|
||||
|
||||
return new UserDefinedFunction(parser.parse(template.getFunction(), functionScope), template.getArgs().size());
|
||||
}
|
||||
|
||||
@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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,37 @@
|
||||
package com.dfsek.terra.addons.noise.paralithic.noise;
|
||||
|
||||
import com.dfsek.paralithic.functions.dynamic.Context;
|
||||
import com.dfsek.paralithic.functions.dynamic.DynamicFunction;
|
||||
|
||||
import com.dfsek.paralithic.node.Statefulness;
|
||||
|
||||
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.getNoiseSeeded(((SeedContext) context).getSeed(), args[0], args[1]);
|
||||
}
|
||||
|
||||
@Override
|
||||
public int getArgNumber() {
|
||||
return 2;
|
||||
}
|
||||
|
||||
@Override
|
||||
public Statefulness statefulness() {
|
||||
return Statefulness.CONTEXTUAL;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,37 @@
|
||||
package com.dfsek.terra.addons.noise.paralithic.noise;
|
||||
|
||||
import com.dfsek.paralithic.functions.dynamic.Context;
|
||||
import com.dfsek.paralithic.functions.dynamic.DynamicFunction;
|
||||
|
||||
import com.dfsek.paralithic.node.Statefulness;
|
||||
|
||||
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.getNoiseSeeded(((SeedContext) context).getSeed(), args[0], args[1], args[2]);
|
||||
}
|
||||
|
||||
@Override
|
||||
public int getArgNumber() {
|
||||
return 3;
|
||||
}
|
||||
|
||||
@Override
|
||||
public Statefulness statefulness() {
|
||||
return Statefulness.CONTEXTUAL;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,16 @@
|
||||
package com.dfsek.terra.addons.noise.paralithic.noise;
|
||||
|
||||
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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,33 @@
|
||||
package com.dfsek.terra.addons.noise.samplers;
|
||||
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
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 getNoiseSeeded(long seed, double x, double y) {
|
||||
return function.getNoiseSeeded(seed++,
|
||||
x + warp.getNoiseSeeded(seed++, x, y) * amplitude,
|
||||
y + warp.getNoiseSeeded(seed, x, y) * amplitude
|
||||
);
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getNoiseSeeded(long seed, double x, double y, double z) {
|
||||
return function.getNoiseSeeded(seed++,
|
||||
x + warp.getNoiseSeeded(seed++, x, y, z) * amplitude,
|
||||
y + warp.getNoiseSeeded(seed++, x, y, z) * amplitude,
|
||||
z + warp.getNoiseSeeded(seed, x, y, z) * amplitude
|
||||
);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,68 @@
|
||||
package com.dfsek.terra.addons.noise.samplers;
|
||||
|
||||
import net.jafama.FastMath;
|
||||
|
||||
import java.awt.image.BufferedImage;
|
||||
|
||||
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 getNoiseSeeded(long seed, double x, double y) {
|
||||
return ((channel.getChannel(image.getRGB(FastMath.floorMod(FastMath.floorToInt(x * frequency), image.getWidth()),
|
||||
FastMath.floorMod(FastMath.floorToInt(y * frequency), image.getHeight()))) / 255D) - 0.5) *
|
||||
2;
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getNoiseSeeded(long seed, double x, double y, double z) {
|
||||
return getNoiseSeeded(seed, x, y);
|
||||
}
|
||||
|
||||
public enum Channel {
|
||||
RED {
|
||||
@Override
|
||||
public int getChannel(int mashed) {
|
||||
return (mashed >> 16) & 0xff;
|
||||
}
|
||||
},
|
||||
GREEN {
|
||||
@Override
|
||||
public int getChannel(int mashed) {
|
||||
return (mashed >> 8) & 0xff;
|
||||
}
|
||||
},
|
||||
BLUE {
|
||||
@Override
|
||||
public int getChannel(int mashed) {
|
||||
return mashed & 0xff;
|
||||
}
|
||||
},
|
||||
GRAYSCALE {
|
||||
@Override
|
||||
public int getChannel(int mashed) {
|
||||
return (RED.getChannel(mashed) + GREEN.getChannel(mashed) + BLUE.getChannel(mashed)) / 3;
|
||||
}
|
||||
},
|
||||
ALPHA {
|
||||
@Override
|
||||
public int getChannel(int mashed) {
|
||||
return (mashed >> 24) & 0xff;
|
||||
}
|
||||
};
|
||||
|
||||
public abstract int getChannel(int mashed);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,50 @@
|
||||
package com.dfsek.terra.addons.noise.samplers;
|
||||
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
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 getNoiseSeeded(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++) {
|
||||
accumulator += in.getNoiseSeeded(seed, x + kx, y + ky) * kernel[kx][ky];
|
||||
}
|
||||
}
|
||||
|
||||
return accumulator;
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getNoiseSeeded(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++) {
|
||||
accumulator += in.getNoiseSeeded(seed, x + kx, y, z + ky) * kernel[kx][ky];
|
||||
}
|
||||
}
|
||||
|
||||
return accumulator;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,537 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.simplex.OpenSimplex2Sampler;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
import com.dfsek.terra.api.util.vector.Vector2;
|
||||
import com.dfsek.terra.api.util.vector.Vector3;
|
||||
|
||||
|
||||
/**
|
||||
* NoiseSampler implementation for Cellular (Voronoi/Worley) Noise.
|
||||
*/
|
||||
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,
|
||||
-0.5677173047d, 0.7511360995d, 0.3368851762d, 0, 0.7858573506d, 0.226674665d, 0.5753666838d, 0, -0.4520345543d, -0.604222686d,
|
||||
-0.6561857263d, 0, 0.002272116345d, 0.4132844051d, -0.9105991643d, 0, -0.5815751419d, -0.5162925989d, 0.6286591339d, 0,
|
||||
-0.03703704785d, 0.8273785755d, 0.5604221175d, 0, -0.5119692504d, 0.7953543429d, -0.3244980058d, 0, -0.2682417366d,
|
||||
-0.9572290247d, -0.1084387619d, 0, -0.2322482736d, -0.9679131102d, -0.09594243324d, 0, 0.3554328906d, -0.8881505545d,
|
||||
0.2913006227d, 0, 0.7346520519d, -0.4371373164d, 0.5188422971d, 0, 0.9985120116d, 0.04659011161d, -0.02833944577d, 0,
|
||||
-0.3727687496d, -0.9082481361d, 0.1900757285d, 0, 0.91737377d, -0.3483642108d, 0.1925298489d, 0, 0.2714911074d, 0.4147529736d,
|
||||
-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,
|
||||
};
|
||||
|
||||
|
||||
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 = fastRound(x);
|
||||
int yr = fastRound(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;
|
||||
|
||||
Vector2 center = new Vector2(x, y);
|
||||
|
||||
switch(distanceFunction) {
|
||||
default:
|
||||
case Euclidean:
|
||||
case EuclideanSq:
|
||||
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 = vecX * vecX + vecY * vecY;
|
||||
|
||||
distance1 = fastMax(fastMin(distance1, newDistance), distance0);
|
||||
if(newDistance < distance0) {
|
||||
distance0 = newDistance;
|
||||
closestHash = hash;
|
||||
center.setX((xi + RAND_VECS_2D[idx] * cellularJitter) / frequency);
|
||||
center.setZ((yi + RAND_VECS_2D[idx | 1] * cellularJitter) / frequency);
|
||||
} else if(newDistance < distance1) {
|
||||
distance2 = distance1;
|
||||
distance1 = newDistance;
|
||||
} else if(newDistance < distance2) {
|
||||
distance2 = newDistance;
|
||||
}
|
||||
yPrimed += PRIME_Y;
|
||||
}
|
||||
xPrimed += PRIME_X;
|
||||
}
|
||||
break;
|
||||
case Manhattan:
|
||||
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 = fastAbs(vecX) + fastAbs(vecY);
|
||||
|
||||
distance1 = fastMax(fastMin(distance1, newDistance), distance0);
|
||||
if(newDistance < distance0) {
|
||||
distance0 = newDistance;
|
||||
closestHash = hash;
|
||||
center.setX((xi + RAND_VECS_2D[idx] * cellularJitter) / frequency);
|
||||
center.setZ((yi + RAND_VECS_2D[idx | 1] * cellularJitter) / frequency);
|
||||
} else if(newDistance < distance1) {
|
||||
distance2 = distance1;
|
||||
distance1 = newDistance;
|
||||
} else if(newDistance < distance2) {
|
||||
distance2 = newDistance;
|
||||
}
|
||||
yPrimed += PRIME_Y;
|
||||
}
|
||||
xPrimed += PRIME_X;
|
||||
}
|
||||
break;
|
||||
case Hybrid:
|
||||
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 = (fastAbs(vecX) + fastAbs(vecY)) + (vecX * vecX + vecY * vecY);
|
||||
|
||||
distance1 = fastMax(fastMin(distance1, newDistance), distance0);
|
||||
if(newDistance < distance0) {
|
||||
distance0 = newDistance;
|
||||
closestHash = hash;
|
||||
center.setX((xi + RAND_VECS_2D[idx] * cellularJitter) / frequency);
|
||||
center.setZ((yi + RAND_VECS_2D[idx | 1] * cellularJitter) / frequency);
|
||||
} else if(newDistance < distance1) {
|
||||
distance2 = distance1;
|
||||
distance1 = newDistance;
|
||||
} else if(newDistance < distance2) {
|
||||
distance2 = newDistance;
|
||||
}
|
||||
yPrimed += PRIME_Y;
|
||||
}
|
||||
xPrimed += PRIME_X;
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
||||
if(distanceFunction == DistanceFunction.Euclidean && returnType != ReturnType.CellValue) {
|
||||
distance0 = fastSqrt(distance0);
|
||||
if(returnType != ReturnType.CellValue) {
|
||||
distance1 = fastSqrt(distance1);
|
||||
}
|
||||
}
|
||||
|
||||
return switch(returnType) {
|
||||
case CellValue -> closestHash * (1 / 2147483648.0);
|
||||
case Distance -> distance0 - 1;
|
||||
case Distance2 -> distance1 - 1;
|
||||
case Distance2Add -> (distance1 + distance0) * 0.5 - 1;
|
||||
case Distance2Sub -> distance1 - distance0 - 1;
|
||||
case Distance2Mul -> distance1 * distance0 * 0.5 - 1;
|
||||
case Distance2Div -> distance0 / distance1 - 1;
|
||||
case NoiseLookup -> noiseLookup.getNoiseSeeded(sl, center.getX(), center.getZ());
|
||||
case Distance3 -> distance2 - 1;
|
||||
case Distance3Add -> (distance2 + distance0) * 0.5 - 1;
|
||||
case Distance3Sub -> distance2 - distance0 - 1;
|
||||
case Distance3Mul -> distance2 * distance0 - 1;
|
||||
case Distance3Div -> distance0 / distance2 - 1;
|
||||
};
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getNoiseRaw(long sl, double x, double y, double z) {
|
||||
int seed = (int) sl;
|
||||
int xr = fastRound(x);
|
||||
int yr = fastRound(y);
|
||||
int zr = fastRound(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;
|
||||
|
||||
Vector3 center = new Vector3(x, y, z);
|
||||
|
||||
switch(distanceFunction) {
|
||||
case Euclidean:
|
||||
case EuclideanSq:
|
||||
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 = vecX * vecX + vecY * vecY + vecZ * vecZ;
|
||||
|
||||
if(newDistance < distance0) {
|
||||
distance0 = newDistance;
|
||||
closestHash = hash;
|
||||
center.setX((xi + RAND_VECS_3D[idx] * cellularJitter) / frequency);
|
||||
center.setY((yi + RAND_VECS_3D[idx | 1] * cellularJitter) / frequency);
|
||||
center.setZ((zi + RAND_VECS_3D[idx | 2] * cellularJitter) / frequency);
|
||||
} else if(newDistance < distance1) {
|
||||
distance2 = distance1;
|
||||
distance1 = newDistance;
|
||||
} else if(newDistance < distance2) {
|
||||
distance2 = newDistance;
|
||||
}
|
||||
zPrimed += PRIME_Z;
|
||||
}
|
||||
yPrimed += PRIME_Y;
|
||||
}
|
||||
xPrimed += PRIME_X;
|
||||
}
|
||||
break;
|
||||
case Manhattan:
|
||||
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 = fastAbs(vecX) + fastAbs(vecY) + fastAbs(vecZ);
|
||||
|
||||
if(newDistance < distance0) {
|
||||
distance0 = newDistance;
|
||||
closestHash = hash;
|
||||
center.setX((xi + RAND_VECS_3D[idx] * cellularJitter) / frequency);
|
||||
center.setY((yi + RAND_VECS_3D[idx | 1] * cellularJitter) / frequency);
|
||||
center.setZ((zi + RAND_VECS_3D[idx | 2] * cellularJitter) / frequency);
|
||||
} else if(newDistance < distance1) {
|
||||
distance2 = distance1;
|
||||
distance1 = newDistance;
|
||||
} else if(newDistance < distance2) {
|
||||
distance2 = newDistance;
|
||||
}
|
||||
zPrimed += PRIME_Z;
|
||||
}
|
||||
yPrimed += PRIME_Y;
|
||||
}
|
||||
xPrimed += PRIME_X;
|
||||
}
|
||||
break;
|
||||
case Hybrid:
|
||||
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 = (fastAbs(vecX) + fastAbs(vecY) + fastAbs(vecZ)) +
|
||||
(vecX * vecX + vecY * vecY + vecZ * vecZ);
|
||||
|
||||
distance1 = fastMax(fastMin(distance1, newDistance), distance0);
|
||||
if(newDistance < distance0) {
|
||||
distance0 = newDistance;
|
||||
closestHash = hash;
|
||||
center.setX((xi + RAND_VECS_3D[idx] * cellularJitter) / frequency);
|
||||
center.setY((yi + RAND_VECS_3D[idx | 1] * cellularJitter) / frequency);
|
||||
center.setZ((zi + RAND_VECS_3D[idx | 2] * cellularJitter) / frequency);
|
||||
} else if(newDistance < distance1) {
|
||||
distance2 = distance1;
|
||||
distance1 = newDistance;
|
||||
} else if(newDistance < distance2) {
|
||||
distance2 = newDistance;
|
||||
}
|
||||
zPrimed += PRIME_Z;
|
||||
}
|
||||
yPrimed += PRIME_Y;
|
||||
}
|
||||
xPrimed += PRIME_X;
|
||||
}
|
||||
break;
|
||||
default:
|
||||
break;
|
||||
}
|
||||
|
||||
if(distanceFunction == DistanceFunction.Euclidean && returnType != ReturnType.CellValue) {
|
||||
distance0 = fastSqrt(distance0);
|
||||
if(returnType != ReturnType.CellValue) {
|
||||
distance1 = fastSqrt(distance1);
|
||||
}
|
||||
}
|
||||
|
||||
return switch(returnType) {
|
||||
case CellValue -> closestHash * (1 / 2147483648.0);
|
||||
case Distance -> distance0 - 1;
|
||||
case Distance2 -> distance1 - 1;
|
||||
case Distance2Add -> (distance1 + distance0) * 0.5 - 1;
|
||||
case Distance2Sub -> distance1 - distance0 - 1;
|
||||
case Distance2Mul -> distance1 * distance0 * 0.5 - 1;
|
||||
case Distance2Div -> distance0 / distance1 - 1;
|
||||
case NoiseLookup -> noiseLookup.getNoiseSeeded(sl, center.getX(), center.getY(), center.getZ());
|
||||
case Distance3 -> distance2 - 1;
|
||||
case Distance3Add -> (distance2 + distance0) * 0.5 - 1;
|
||||
case Distance3Sub -> distance2 - distance0 - 1;
|
||||
case Distance3Mul -> distance2 * distance0 - 1;
|
||||
case Distance3Div -> distance0 / distance2 - 1;
|
||||
};
|
||||
}
|
||||
|
||||
public enum DistanceFunction {
|
||||
Euclidean,
|
||||
EuclideanSq,
|
||||
Manhattan,
|
||||
Hybrid
|
||||
}
|
||||
|
||||
|
||||
public enum ReturnType {
|
||||
CellValue,
|
||||
Distance,
|
||||
Distance2,
|
||||
Distance2Add,
|
||||
Distance2Sub,
|
||||
Distance2Mul,
|
||||
Distance2Div,
|
||||
NoiseLookup,
|
||||
Distance3,
|
||||
Distance3Add,
|
||||
Distance3Sub,
|
||||
Distance3Mul,
|
||||
Distance3Div
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,22 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise;
|
||||
|
||||
/**
|
||||
* Sampler3D implementation that returns a constant.
|
||||
*/
|
||||
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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,45 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise;
|
||||
|
||||
import com.dfsek.paralithic.Expression;
|
||||
import com.dfsek.paralithic.eval.parser.Parser;
|
||||
import com.dfsek.paralithic.eval.parser.Scope;
|
||||
import com.dfsek.paralithic.eval.tokenizer.ParseException;
|
||||
import com.dfsek.paralithic.functions.Function;
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,107 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise;
|
||||
|
||||
import net.jafama.FastMath;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.random.WhiteNoiseSampler;
|
||||
|
||||
|
||||
public class GaborNoiseSampler extends NoiseFunction {
|
||||
private final WhiteNoiseSampler rand;
|
||||
private double k = 1.0;
|
||||
private double a = 0.1;
|
||||
private double f0 = 0.625;
|
||||
private double kernelRadius = (FastMath.sqrt(-FastMath.log(0.05) / Math.PI) / a);
|
||||
private double omega0 = Math.PI * 0.25;
|
||||
private boolean isotropic = true;
|
||||
private double impulsesPerKernel = 64d;
|
||||
private double impulseDensity = (impulsesPerKernel / (Math.PI * kernelRadius * kernelRadius));
|
||||
private double impulsesPerCell = impulseDensity * kernelRadius * kernelRadius;
|
||||
private double g = FastMath.exp(-impulsesPerCell);
|
||||
|
||||
|
||||
public GaborNoiseSampler() {
|
||||
rand = new WhiteNoiseSampler();
|
||||
}
|
||||
|
||||
private void recalculateRadiusAndDensity() {
|
||||
kernelRadius = (FastMath.sqrt(-FastMath.log(0.05) / Math.PI) / this.a);
|
||||
impulseDensity = (impulsesPerKernel / (Math.PI * kernelRadius * kernelRadius));
|
||||
impulsesPerCell = impulseDensity * kernelRadius * kernelRadius;
|
||||
g = FastMath.exp(-impulsesPerCell);
|
||||
}
|
||||
|
||||
private double gaborNoise(long seed, double x, double y) {
|
||||
x /= kernelRadius;
|
||||
y /= kernelRadius;
|
||||
int xi = fastFloor(x);
|
||||
int yi = fastFloor(y);
|
||||
double xf = x - xi;
|
||||
double yf = y - yi;
|
||||
double noise = 0;
|
||||
for(int dx = -1; dx <= 1; dx++) {
|
||||
for(int dz = -1; dz <= 1; dz++) {
|
||||
noise += calculateCell(seed, xi + dx, yi + dz, xf - dx, yf - dz);
|
||||
}
|
||||
}
|
||||
return noise;
|
||||
}
|
||||
|
||||
private double calculateCell(long seed, int xi, int yi, double x, double y) {
|
||||
long mashedSeed = 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);
|
||||
}
|
||||
return noise;
|
||||
}
|
||||
|
||||
private double gabor(double omega_0, double x, double y) {
|
||||
return k * (FastMath.exp(-Math.PI * (a * a) * (x * x + y * y)) * fastCos(2 * Math.PI * f0 * (x * fastCos(omega_0) + y * fastSin(
|
||||
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);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,143 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise;
|
||||
|
||||
import net.jafama.FastMath;
|
||||
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
@SuppressWarnings("ManualMinMaxCalculation")
|
||||
public abstract class NoiseFunction implements NoiseSampler {
|
||||
// Hashing
|
||||
protected static final int PRIME_X = 501125321;
|
||||
protected static final int PRIME_Y = 1136930381;
|
||||
protected static final int PRIME_Z = 1720413743;
|
||||
static final int precision = 100;
|
||||
static final int modulus = 360 * precision;
|
||||
static final double[] sin = new double[360 * 100]; // lookup table
|
||||
static {
|
||||
for(int i = 0; i < sin.length; i++) {
|
||||
sin[i] = (float) Math.sin((double) (i) / (precision));
|
||||
}
|
||||
}
|
||||
protected double frequency = 0.02d;
|
||||
protected long salt;
|
||||
|
||||
public NoiseFunction() {
|
||||
this.salt = 0;
|
||||
}
|
||||
|
||||
protected static int fastFloor(double f) {
|
||||
return f >= 0 ? (int) f : (int) f - 1;
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
protected static int fastRound(double f) {
|
||||
return f >= 0 ? (int) (f + 0.5f) : (int) (f - 0.5);
|
||||
}
|
||||
|
||||
protected static double lerp(double a, double b, double t) {
|
||||
return a + t * (b - a);
|
||||
}
|
||||
|
||||
protected static double interpHermite(double t) {
|
||||
return t * t * (3 - 2 * t);
|
||||
}
|
||||
|
||||
protected static double interpQuintic(double t) {
|
||||
return t * t * t * (t * (t * 6 - 15) + 10);
|
||||
}
|
||||
|
||||
protected static double cubicLerp(double a, double b, double c, double d, double t) {
|
||||
double p = (d - c) - (a - b);
|
||||
return t * t * t * p + t * t * ((a - b) - p) + t * (c - a) + b;
|
||||
}
|
||||
|
||||
protected static double fastMin(double a, double b) {
|
||||
return a < b ? a : b;
|
||||
}
|
||||
|
||||
protected static double fastMax(double a, double b) {
|
||||
return a > b ? a : b;
|
||||
}
|
||||
|
||||
protected static double fastAbs(double f) {
|
||||
return f < 0 ? -f : f;
|
||||
}
|
||||
|
||||
protected static double fastSqrt(double f) {
|
||||
return FastMath.sqrt(f);
|
||||
}
|
||||
|
||||
protected static int fastCeil(double f) {
|
||||
int i = (int) f;
|
||||
if(i < f) i++;
|
||||
return i;
|
||||
}
|
||||
|
||||
/**
|
||||
* Murmur64 hashing function
|
||||
*
|
||||
* @param h Input value
|
||||
*
|
||||
* @return Hashed value
|
||||
*/
|
||||
protected static long murmur64(long h) {
|
||||
h ^= h >>> 33;
|
||||
h *= 0xff51afd7ed558ccdL;
|
||||
h ^= h >>> 33;
|
||||
h *= 0xc4ceb9fe1a85ec53L;
|
||||
h ^= h >>> 33;
|
||||
return h;
|
||||
}
|
||||
|
||||
protected static double fastSin(double a) {
|
||||
return sinLookup((int) (a * precision + 0.5f));
|
||||
}
|
||||
|
||||
protected static double fastCos(double a) {
|
||||
return sinLookup((int) ((a + Math.PI / 2) * precision + 0.5f));
|
||||
}
|
||||
|
||||
private static double sinLookup(int a) {
|
||||
return a >= 0 ? sin[a % (modulus)] : -sin[-a % (modulus)];
|
||||
}
|
||||
|
||||
public void setSalt(long salt) {
|
||||
this.salt = salt;
|
||||
}
|
||||
|
||||
public double getFrequency() {
|
||||
return frequency;
|
||||
}
|
||||
|
||||
public void setFrequency(double frequency) {
|
||||
this.frequency = frequency;
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getNoiseSeeded(long seed, double x, double y) {
|
||||
return getNoiseRaw(seed + salt, x * frequency, y * frequency);
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getNoiseSeeded(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);
|
||||
}
|
||||
@ -0,0 +1,47 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.fractal;
|
||||
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
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.getNoiseSeeded(seed++, x, y);
|
||||
sum += noise * amp;
|
||||
amp *= lerp(1.0, fastMin(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.getNoiseSeeded(seed++, x, y, z);
|
||||
sum += noise * amp;
|
||||
amp *= lerp(1.0, (noise + 1) * 0.5, weightedStrength);
|
||||
|
||||
x *= lacunarity;
|
||||
y *= lacunarity;
|
||||
z *= lacunarity;
|
||||
amp *= gain;
|
||||
}
|
||||
|
||||
return sum;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,48 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.fractal;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction;
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
public abstract class FractalNoiseFunction extends NoiseFunction {
|
||||
protected final NoiseSampler input;
|
||||
protected double fractalBounding = 1 / 1.75;
|
||||
protected int octaves = 3;
|
||||
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 = fastAbs(this.gain);
|
||||
double amp = gain;
|
||||
double ampFractal = 1.0;
|
||||
for(int i = 1; i < octaves; i++) {
|
||||
ampFractal += amp;
|
||||
amp *= gain;
|
||||
}
|
||||
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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,59 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.fractal;
|
||||
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
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.getNoiseSeeded(seed++, x, y) + 1) * pingPongStrength);
|
||||
sum += (noise - 0.5) * 2 * amp;
|
||||
amp *= 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.getNoiseSeeded(seed++, x, y, z) + 1) * pingPongStrength);
|
||||
sum += (noise - 0.5) * 2 * amp;
|
||||
amp *= lerp(1.0, noise, weightedStrength);
|
||||
|
||||
x *= lacunarity;
|
||||
y *= lacunarity;
|
||||
z *= lacunarity;
|
||||
amp *= gain;
|
||||
}
|
||||
|
||||
return sum;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,48 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.fractal;
|
||||
|
||||
import com.dfsek.terra.api.noise.NoiseSampler;
|
||||
|
||||
|
||||
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 = fastAbs(input.getNoiseSeeded(seed++, x, y));
|
||||
sum += (noise * -2 + 1) * amp;
|
||||
amp *= 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 = fastAbs(input.getNoiseSeeded(seed++, x, y, z));
|
||||
sum += (noise * -2 + 1) * amp;
|
||||
amp *= lerp(1.0, 1 - noise, weightedStrength);
|
||||
|
||||
x *= lacunarity;
|
||||
y *= lacunarity;
|
||||
z *= lacunarity;
|
||||
amp *= gain;
|
||||
}
|
||||
|
||||
return sum;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,39 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.random;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction;
|
||||
|
||||
|
||||
/**
|
||||
* NoiseSampler implementation to provide random, normally distributed (Gaussian) noise.
|
||||
*/
|
||||
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;
|
||||
do {
|
||||
v1 = whiteNoiseSampler.getNoiseSeeded(seed++, x, y);
|
||||
v2 = whiteNoiseSampler.getNoiseSeeded(seed++, x, y);
|
||||
s = v1 * v1 + v2 * v2;
|
||||
} while(s >= 1 || s == 0);
|
||||
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;
|
||||
do {
|
||||
v1 = whiteNoiseSampler.getNoiseSeeded(seed++, x, y, z);
|
||||
v2 = whiteNoiseSampler.getNoiseSeeded(seed++, x, y, z);
|
||||
s = v1 * v1 + v2 * v2;
|
||||
} while(s >= 1 || s == 0);
|
||||
double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s) / s);
|
||||
return v1 * multiplier;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,53 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.random;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction;
|
||||
|
||||
|
||||
/**
|
||||
* NoiseSampler implementation to produce random, uniformly distributed (white) noise.
|
||||
*/
|
||||
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 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 murmur64(hash);
|
||||
}
|
||||
|
||||
public double getNoiseRaw(long seed) {
|
||||
return (Double.longBitsToDouble((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);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,272 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.simplex;
|
||||
|
||||
/**
|
||||
* NoiseSampler implementation to provide OpenSimplex2 (Smooth Variant) noise.
|
||||
*/
|
||||
public class OpenSimplex2SSampler extends SimplexStyleSampler {
|
||||
@Override
|
||||
@SuppressWarnings("NumericOverflow")
|
||||
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 = fastFloor(x);
|
||||
int j = fastFloor(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) {
|
||||
if(xi + xmyi > 1) {
|
||||
double x2 = x0 + (3 * G2 - 2);
|
||||
double y2 = y0 + (3 * G2 - 1);
|
||||
double a2 = (2.0 / 3.0) - x2 * x2 - y2 * y2;
|
||||
if(a2 > 0) {
|
||||
value += (a2 * a2) * (a2 * a2) * gradCoord(seed, i + (PRIME_X << 1), j + PRIME_Y, x2, y2);
|
||||
}
|
||||
} else {
|
||||
double x2 = x0 + G2;
|
||||
double y2 = y0 + (G2 - 1);
|
||||
double a2 = (2.0 / 3.0) - x2 * x2 - y2 * y2;
|
||||
if(a2 > 0) {
|
||||
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);
|
||||
double a3 = (2.0 / 3.0) - x3 * x3 - y3 * y3;
|
||||
if(a3 > 0) {
|
||||
value += (a3 * a3) * (a3 * a3) * gradCoord(seed, i + PRIME_X, j + (PRIME_Y << 1), x3, y3);
|
||||
}
|
||||
} else {
|
||||
double x3 = x0 + (G2 - 1);
|
||||
double y3 = y0 + G2;
|
||||
double a3 = (2.0 / 3.0) - x3 * x3 - y3 * y3;
|
||||
if(a3 > 0) {
|
||||
value += (a3 * a3) * (a3 * a3) * gradCoord(seed, i + PRIME_X, j, x3, y3);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
if(xi + xmyi < 0) {
|
||||
double x2 = x0 + (1 - G2);
|
||||
double y2 = y0 - G2;
|
||||
double a2 = (2.0 / 3.0) - x2 * x2 - y2 * y2;
|
||||
if(a2 > 0) {
|
||||
value += (a2 * a2) * (a2 * a2) * gradCoord(seed, i - PRIME_X, j, x2, y2);
|
||||
}
|
||||
} else {
|
||||
double x2 = x0 + (G2 - 1);
|
||||
double y2 = y0 + G2;
|
||||
double a2 = (2.0 / 3.0) - x2 * x2 - y2 * y2;
|
||||
if(a2 > 0) {
|
||||
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);
|
||||
double a2 = (2.0 / 3.0) - x2 * x2 - y2 * y2;
|
||||
if(a2 > 0) {
|
||||
value += (a2 * a2) * (a2 * a2) * gradCoord(seed, i, j - PRIME_Y, x2, y2);
|
||||
}
|
||||
} else {
|
||||
double x2 = x0 + G2;
|
||||
double y2 = y0 + (G2 - 1);
|
||||
double a2 = (2.0 / 3.0) - x2 * x2 - y2 * y2;
|
||||
if(a2 > 0) {
|
||||
value += (a2 * a2) * (a2 * a2) * gradCoord(seed, i, j + PRIME_Y, x2, y2);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return value * 18.24196194486065;
|
||||
}
|
||||
|
||||
@Override
|
||||
@SuppressWarnings("NumericOverflow")
|
||||
public double getNoiseRaw(long sl, double x, double y, double z) {
|
||||
int seed = (int) sl;
|
||||
// 3D OpenSimplex2S case uses two offset rotated cube grids.
|
||||
final double R3 = (2.0 / 3.0);
|
||||
double r = (x + y + z) * R3; // Rotation, not skew
|
||||
x = r - x;
|
||||
y = r - y;
|
||||
z = r - z;
|
||||
|
||||
|
||||
int i = fastFloor(x);
|
||||
int j = fastFloor(y);
|
||||
int k = fastFloor(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);
|
||||
|
||||
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);
|
||||
} 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);
|
||||
}
|
||||
|
||||
double a4 = xAFlipMask1 + a1;
|
||||
if(a4 > 0) {
|
||||
double x4 = (xNMask | 1) + x1;
|
||||
value += (a4 * a4) * (a4 * a4) * gradCoord(seed2, i + (xNMask & (PRIME_X << 1)), j + PRIME_Y, k + PRIME_Z, x4, y1, z1);
|
||||
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);
|
||||
} 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);
|
||||
}
|
||||
|
||||
double a8 = yAFlipMask1 + a1;
|
||||
if(a8 > 0) {
|
||||
double y8 = (yNMask | 1) + y1;
|
||||
value += (a8 * a8) * (a8 * a8) * gradCoord(seed2, i + PRIME_X, j + (yNMask & (PRIME_Y << 1)), k + PRIME_Z, x1, y8, z1);
|
||||
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);
|
||||
} 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);
|
||||
}
|
||||
|
||||
double aC = zAFlipMask1 + a1;
|
||||
if(aC > 0) {
|
||||
double zC = (zNMask | 1) + z1;
|
||||
value += (aC * aC) * (aC * aC) * gradCoord(seed2, i + PRIME_X, j + PRIME_Y, k + (zNMask & (PRIME_Z << 1)), x1, y1, zC);
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
return value * 9.046026385208288;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,153 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.simplex;
|
||||
|
||||
/**
|
||||
* NoiseSampler implementation to provide OpenSimplex2 noise.
|
||||
*/
|
||||
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 = fastFloor(x);
|
||||
int j = fastFloor(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 {
|
||||
double x2 = x0 + (2 * G2 - 1);
|
||||
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);
|
||||
double b = 0.5 - x1 * x1 - y1 * y1;
|
||||
if(b <= 0) n1 = 0;
|
||||
else {
|
||||
n1 = (b * b) * (b * b) * gradCoord(seed, i, j + PRIME_Y, x1, y1);
|
||||
}
|
||||
} else {
|
||||
double x1 = x0 + (G2 - 1);
|
||||
double y1 = y0 + G2;
|
||||
double b = 0.5 - x1 * x1 - y1 * y1;
|
||||
if(b <= 0) n1 = 0;
|
||||
else {
|
||||
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;
|
||||
// 3D OpenSimplex2Sampler case uses two offset rotated cube grids.
|
||||
final double R3 = (2.0 / 3.0);
|
||||
double r = (x + y + z) * R3; // Rotation, not skew
|
||||
x = r - x;
|
||||
y = r - y;
|
||||
z = r - z;
|
||||
|
||||
|
||||
int i = fastRound(x);
|
||||
int j = fastRound(y);
|
||||
int k = fastRound(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) {
|
||||
b -= 1;
|
||||
value += (b * b) * (b * b) * gradCoord(seed, i - xNSign * PRIME_X, j, k, x0 + xNSign, y0, z0);
|
||||
}
|
||||
} else if(ay0 > ax0 && ay0 >= az0) {
|
||||
double b = a + ay0 + ay0;
|
||||
if(b > 1) {
|
||||
b -= 1;
|
||||
value += (b * b) * (b * b) * gradCoord(seed, i, j - yNSign * PRIME_Y, k, x0, y0 + yNSign, z0);
|
||||
}
|
||||
} else {
|
||||
double b = a + az0 + az0;
|
||||
if(b > 1) {
|
||||
b -= 1;
|
||||
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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,67 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.simplex;
|
||||
|
||||
/**
|
||||
* NoiseSampler implementation to provide Perlin Noise.
|
||||
*/
|
||||
public class PerlinSampler extends SimplexStyleSampler {
|
||||
@Override
|
||||
public double getNoiseRaw(long sl, double x, double y) {
|
||||
int seed = (int) sl;
|
||||
int x0 = fastFloor(x);
|
||||
int y0 = fastFloor(y);
|
||||
|
||||
double xd0 = x - x0;
|
||||
double yd0 = y - y0;
|
||||
double xd1 = xd0 - 1;
|
||||
double yd1 = yd0 - 1;
|
||||
|
||||
double xs = interpQuintic(xd0);
|
||||
double ys = interpQuintic(yd0);
|
||||
|
||||
x0 *= PRIME_X;
|
||||
y0 *= PRIME_Y;
|
||||
int x1 = x0 + PRIME_X;
|
||||
int y1 = y0 + PRIME_Y;
|
||||
|
||||
double xf0 = lerp(gradCoord(seed, x0, y0, xd0, yd0), gradCoord(seed, x1, y0, xd1, yd0), xs);
|
||||
double xf1 = lerp(gradCoord(seed, x0, y1, xd0, yd1), gradCoord(seed, x1, y1, xd1, yd1), xs);
|
||||
|
||||
return lerp(xf0, xf1, ys) * 1.4247691104677813;
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getNoiseRaw(long sl, double x, double y, double z) {
|
||||
int seed = (int) sl;
|
||||
int x0 = fastFloor(x);
|
||||
int y0 = fastFloor(y);
|
||||
int z0 = fastFloor(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 = interpQuintic(xd0);
|
||||
double ys = interpQuintic(yd0);
|
||||
double zs = 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 = lerp(gradCoord(seed, x0, y0, z0, xd0, yd0, zd0), gradCoord(seed, x1, y0, z0, xd1, yd0, zd0), xs);
|
||||
double xf10 = lerp(gradCoord(seed, x0, y1, z0, xd0, yd1, zd0), gradCoord(seed, x1, y1, z0, xd1, yd1, zd0), xs);
|
||||
double xf01 = lerp(gradCoord(seed, x0, y0, z1, xd0, yd0, zd1), gradCoord(seed, x1, y0, z1, xd1, yd0, zd1), xs);
|
||||
double xf11 = lerp(gradCoord(seed, x0, y1, z1, xd0, yd1, zd1), gradCoord(seed, x1, y1, z1, xd1, yd1, zd1), xs);
|
||||
|
||||
double yf0 = lerp(xf00, xf10, ys);
|
||||
double yf1 = lerp(xf01, xf11, ys);
|
||||
|
||||
return lerp(yf0, yf1, zs) * 0.964921414852142333984375;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,242 @@
|
||||
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),
|
||||
};
|
||||
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),
|
||||
};
|
||||
|
||||
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 = fastFloor(x + t);
|
||||
int j = fastFloor(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;
|
||||
j1 = 0;
|
||||
} else {
|
||||
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;
|
||||
} else {
|
||||
t *= t;
|
||||
n0 = t * t * gradCoord2D(seed, i, j, x0, y0);
|
||||
}
|
||||
|
||||
t = 0.5 - x1 * x1 - y1 * y1;
|
||||
if(t < 0) {
|
||||
n1 = 0;
|
||||
} else {
|
||||
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;
|
||||
} else {
|
||||
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;
|
||||
double t = (x + y + z) * F3;
|
||||
int i = fastFloor(x + t);
|
||||
int j = fastFloor(y + t);
|
||||
int k = fastFloor(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;
|
||||
j1 = 0;
|
||||
k1 = 0;
|
||||
i2 = 1;
|
||||
j2 = 1;
|
||||
k2 = 0;
|
||||
} else if(x0 >= z0) {
|
||||
i1 = 1;
|
||||
j1 = 0;
|
||||
k1 = 0;
|
||||
i2 = 1;
|
||||
j2 = 0;
|
||||
k2 = 1;
|
||||
} else // x0 < z0
|
||||
{
|
||||
i1 = 0;
|
||||
j1 = 0;
|
||||
k1 = 1;
|
||||
i2 = 1;
|
||||
j2 = 0;
|
||||
k2 = 1;
|
||||
}
|
||||
} else // x0 < y0
|
||||
{
|
||||
if(y0 < z0) {
|
||||
i1 = 0;
|
||||
j1 = 0;
|
||||
k1 = 1;
|
||||
i2 = 0;
|
||||
j2 = 1;
|
||||
k2 = 1;
|
||||
} else if(x0 < z0) {
|
||||
i1 = 0;
|
||||
j1 = 1;
|
||||
k1 = 0;
|
||||
i2 = 0;
|
||||
j2 = 1;
|
||||
k2 = 1;
|
||||
} else // x0 >= z0
|
||||
{
|
||||
i1 = 0;
|
||||
j1 = 1;
|
||||
k1 = 0;
|
||||
i2 = 1;
|
||||
j2 = 1;
|
||||
k2 = 0;
|
||||
}
|
||||
}
|
||||
|
||||
double x1 = x0 - i1 + G3;
|
||||
double y1 = y0 - j1 + G3;
|
||||
double z1 = z0 - k1 + G3;
|
||||
double x2 = x0 - i2 + F3;
|
||||
double y2 = y0 - j2 + F3;
|
||||
double z2 = z0 - k2 + F3;
|
||||
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;
|
||||
} else {
|
||||
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;
|
||||
} else {
|
||||
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;
|
||||
} else {
|
||||
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;
|
||||
this.z = z;
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,97 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.simplex;
|
||||
|
||||
import com.dfsek.terra.addons.noise.samplers.noise.NoiseFunction;
|
||||
|
||||
|
||||
/**
|
||||
* Abstract NoiseSampler implementation for simplex-style noise functions.
|
||||
*/
|
||||
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,
|
||||
};
|
||||
|
||||
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
|
||||
};
|
||||
|
||||
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;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,102 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.value;
|
||||
|
||||
public class ValueCubicSampler extends ValueStyleNoise {
|
||||
@Override
|
||||
public double getNoiseRaw(long sl, double x, double y) {
|
||||
int seed = (int) sl;
|
||||
int x1 = fastFloor(x);
|
||||
int y1 = fastFloor(y);
|
||||
|
||||
double xs = x - x1;
|
||||
double ys = y - y1;
|
||||
|
||||
x1 *= PRIME_X;
|
||||
y1 *= PRIME_Y;
|
||||
int x0 = x1 - PRIME_X;
|
||||
int y0 = y1 - PRIME_Y;
|
||||
int x2 = x1 + PRIME_X;
|
||||
int y2 = y1 + PRIME_Y;
|
||||
int x3 = x1 + (PRIME_X << 1);
|
||||
int y3 = y1 + (PRIME_Y << 1);
|
||||
|
||||
return cubicLerp(
|
||||
cubicLerp(valCoord(seed, x0, y0), valCoord(seed, x1, y0), valCoord(seed, x2, y0), valCoord(seed, x3, y0),
|
||||
xs),
|
||||
cubicLerp(valCoord(seed, x0, y1), valCoord(seed, x1, y1), valCoord(seed, x2, y1), valCoord(seed, x3, y1),
|
||||
xs),
|
||||
cubicLerp(valCoord(seed, x0, y2), valCoord(seed, x1, y2), valCoord(seed, x2, y2), valCoord(seed, x3, y2),
|
||||
xs),
|
||||
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 = fastFloor(x);
|
||||
int y1 = fastFloor(y);
|
||||
int z1 = fastFloor(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;
|
||||
int x2 = x1 + PRIME_X;
|
||||
int y2 = y1 + PRIME_Y;
|
||||
int z2 = z1 + PRIME_Z;
|
||||
int x3 = x1 + (PRIME_X << 1);
|
||||
int y3 = y1 + (PRIME_Y << 1);
|
||||
int z3 = z1 + (PRIME_Z << 1);
|
||||
|
||||
return cubicLerp(
|
||||
cubicLerp(
|
||||
cubicLerp(valCoord(seed, x0, y0, z0), valCoord(seed, x1, y0, z0), valCoord(seed, x2, y0, z0),
|
||||
valCoord(seed, x3, y0, z0), xs),
|
||||
cubicLerp(valCoord(seed, x0, y1, z0), valCoord(seed, x1, y1, z0), valCoord(seed, x2, y1, z0),
|
||||
valCoord(seed, x3, y1, z0), xs),
|
||||
cubicLerp(valCoord(seed, x0, y2, z0), valCoord(seed, x1, y2, z0), valCoord(seed, x2, y2, z0),
|
||||
valCoord(seed, x3, y2, z0), xs),
|
||||
cubicLerp(valCoord(seed, x0, y3, z0), valCoord(seed, x1, y3, z0), valCoord(seed, x2, y3, z0),
|
||||
valCoord(seed, x3, y3, z0), xs),
|
||||
ys),
|
||||
cubicLerp(
|
||||
cubicLerp(valCoord(seed, x0, y0, z1), valCoord(seed, x1, y0, z1), valCoord(seed, x2, y0, z1),
|
||||
valCoord(seed, x3, y0, z1), xs),
|
||||
cubicLerp(valCoord(seed, x0, y1, z1), valCoord(seed, x1, y1, z1), valCoord(seed, x2, y1, z1),
|
||||
valCoord(seed, x3, y1, z1), xs),
|
||||
cubicLerp(valCoord(seed, x0, y2, z1), valCoord(seed, x1, y2, z1), valCoord(seed, x2, y2, z1),
|
||||
valCoord(seed, x3, y2, z1), xs),
|
||||
cubicLerp(valCoord(seed, x0, y3, z1), valCoord(seed, x1, y3, z1), valCoord(seed, x2, y3, z1),
|
||||
valCoord(seed, x3, y3, z1), xs),
|
||||
ys),
|
||||
cubicLerp(
|
||||
cubicLerp(valCoord(seed, x0, y0, z2), valCoord(seed, x1, y0, z2), valCoord(seed, x2, y0, z2),
|
||||
valCoord(seed, x3, y0, z2), xs),
|
||||
cubicLerp(valCoord(seed, x0, y1, z2), valCoord(seed, x1, y1, z2), valCoord(seed, x2, y1, z2),
|
||||
valCoord(seed, x3, y1, z2), xs),
|
||||
cubicLerp(valCoord(seed, x0, y2, z2), valCoord(seed, x1, y2, z2), valCoord(seed, x2, y2, z2),
|
||||
valCoord(seed, x3, y2, z2), xs),
|
||||
cubicLerp(valCoord(seed, x0, y3, z2), valCoord(seed, x1, y3, z2), valCoord(seed, x2, y3, z2),
|
||||
valCoord(seed, x3, y3, z2), xs),
|
||||
ys),
|
||||
cubicLerp(
|
||||
cubicLerp(valCoord(seed, x0, y0, z3), valCoord(seed, x1, y0, z3), valCoord(seed, x2, y0, z3),
|
||||
valCoord(seed, x3, y0, z3), xs),
|
||||
cubicLerp(valCoord(seed, x0, y1, z3), valCoord(seed, x1, y1, z3), valCoord(seed, x2, y1, z3),
|
||||
valCoord(seed, x3, y1, z3), xs),
|
||||
cubicLerp(valCoord(seed, x0, y2, z3), valCoord(seed, x1, y2, z3), valCoord(seed, x2, y2, z3),
|
||||
valCoord(seed, x3, y2, z3), xs),
|
||||
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));
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,52 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.value;
|
||||
|
||||
public class ValueSampler extends ValueStyleNoise {
|
||||
@Override
|
||||
public double getNoiseRaw(long sl, double x, double y) {
|
||||
int seed = (int) sl;
|
||||
int x0 = fastFloor(x);
|
||||
int y0 = fastFloor(y);
|
||||
|
||||
double xs = interpHermite(x - x0);
|
||||
double ys = interpHermite(y - y0);
|
||||
|
||||
x0 *= PRIME_X;
|
||||
y0 *= PRIME_Y;
|
||||
int x1 = x0 + PRIME_X;
|
||||
int y1 = y0 + PRIME_Y;
|
||||
|
||||
double xf0 = lerp(valCoord(seed, x0, y0), valCoord(seed, x1, y0), xs);
|
||||
double xf1 = lerp(valCoord(seed, x0, y1), valCoord(seed, x1, y1), xs);
|
||||
|
||||
return lerp(xf0, xf1, ys);
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getNoiseRaw(long sl, double x, double y, double z) {
|
||||
int seed = (int) sl;
|
||||
int x0 = fastFloor(x);
|
||||
int y0 = fastFloor(y);
|
||||
int z0 = fastFloor(z);
|
||||
|
||||
double xs = interpHermite(x - x0);
|
||||
double ys = interpHermite(y - y0);
|
||||
double zs = 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 = lerp(valCoord(seed, x0, y0, z0), valCoord(seed, x1, y0, z0), xs);
|
||||
double xf10 = lerp(valCoord(seed, x0, y1, z0), valCoord(seed, x1, y1, z0), xs);
|
||||
double xf01 = lerp(valCoord(seed, x0, y0, z1), valCoord(seed, x1, y0, z1), xs);
|
||||
double xf11 = lerp(valCoord(seed, x0, y1, z1), valCoord(seed, x1, y1, z1), xs);
|
||||
|
||||
double yf0 = lerp(xf00, xf10, ys);
|
||||
double yf1 = lerp(xf01, xf11, ys);
|
||||
|
||||
return lerp(yf0, yf1, zs);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,23 @@
|
||||
package com.dfsek.terra.addons.noise.samplers.noise.value;
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,119 @@
|
||||
/*
|
||||
Copyright 2009 Sandia Corporation. Under the terms of Contract
|
||||
DE-AC04-94AL85000 with Sandia Corporation, the U.S. Government
|
||||
retains certain rights in this software.
|
||||
|
||||
BSD Open Source License.
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions are met:
|
||||
|
||||
* Redistributions of source code must retain the above copyright notice,
|
||||
this list of conditions and the following disclaimer.
|
||||
* Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
* Neither the name of Sandia National Laboratories nor the names of its
|
||||
contributors may be used to endorse or promote products derived from
|
||||
this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
|
||||
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
|
||||
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
||||
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
||||
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
||||
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
|
||||
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGE.
|
||||
*/
|
||||
|
||||
package com.dfsek.terra.addons.noise.util;
|
||||
|
||||
import java.io.Serializable;
|
||||
|
||||
|
||||
public abstract class HashIntrinsic implements Serializable {
|
||||
public static final int FLOAT_EXP_BIT_MASK = 2139095040;
|
||||
public static final int FLOAT_SIGNIF_BIT_MASK = 8388607;
|
||||
public static final long DOUBLE_EXP_BIT_MASK = 9218868437227405312L;
|
||||
public static final long DOUBLE_SIGNIF_BIT_MASK = 4503599627370495L;
|
||||
protected static final int DEFAULT_INITIAL_CAPACITY = 16;
|
||||
protected static final int MAXIMUM_CAPACITY = 1073741824;
|
||||
protected static final float DEFAULT_LOAD_FACTOR = 0.75F;
|
||||
private static final long serialVersionUID = 8058099372006904458L;
|
||||
protected int size;
|
||||
protected int threshold;
|
||||
protected float loadFactor;
|
||||
protected int capMinus1;
|
||||
|
||||
protected HashIntrinsic(int initialCapacity, float loadFactor) {
|
||||
if(initialCapacity <= 0) {
|
||||
throw new IllegalArgumentException("Illegal initial capacity: " + initialCapacity);
|
||||
} else if(!(loadFactor <= 0.0F) && !Float.isNaN(loadFactor)) {
|
||||
if(initialCapacity > 1073741824) {
|
||||
initialCapacity = 1073741824;
|
||||
}
|
||||
|
||||
int capacity;
|
||||
for(capacity = 1; capacity < initialCapacity; capacity <<= 1) {
|
||||
}
|
||||
|
||||
this.capMinus1 = capacity - 1;
|
||||
this.loadFactor = loadFactor;
|
||||
this.threshold = (int) ((float) capacity * loadFactor);
|
||||
} else {
|
||||
throw new IllegalArgumentException("Illegal load factor: " + loadFactor);
|
||||
}
|
||||
}
|
||||
|
||||
public static int floatToIntBits(float value) {
|
||||
int result = Float.floatToRawIntBits(value);
|
||||
if((result & 2139095040) == 2139095040 && (result & 8388607) != 0) {
|
||||
result = 2143289344;
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
public static long doubleToLongBits(double value) {
|
||||
long result = Double.doubleToRawLongBits(value);
|
||||
if((result & 9218868437227405312L) == 9218868437227405312L && (result & 4503599627370495L) != 0L) {
|
||||
result = 9221120237041090560L;
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
protected static int hashCodeLong(long value) {
|
||||
return (int) (value ^ value >>> 32);
|
||||
}
|
||||
|
||||
protected static int hashCodeFloat(float value) {
|
||||
return floatToIntBits(value);
|
||||
}
|
||||
|
||||
protected static int hashCodeDouble(double value) {
|
||||
long bits = doubleToLongBits(value);
|
||||
return (int) (bits ^ bits >>> 32);
|
||||
}
|
||||
|
||||
protected static int tableIndex(int hc, int lm1) {
|
||||
hc ^= hc >>> 20 ^ hc >>> 12;
|
||||
hc ^= hc >>> 7 ^ hc >>> 4;
|
||||
return hc & lm1;
|
||||
}
|
||||
|
||||
public int size() {
|
||||
return this.size;
|
||||
}
|
||||
|
||||
public abstract void clear();
|
||||
|
||||
public boolean isEmpty() {
|
||||
return this.size == 0;
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,295 @@
|
||||
/*
|
||||
Copyright 2009 Sandia Corporation. Under the terms of Contract
|
||||
DE-AC04-94AL85000 with Sandia Corporation, the U.S. Government
|
||||
retains certain rights in this software.
|
||||
|
||||
BSD Open Source License.
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without
|
||||
modification, are permitted provided that the following conditions are met:
|
||||
|
||||
* Redistributions of source code must retain the above copyright notice,
|
||||
this list of conditions and the following disclaimer.
|
||||
* Redistributions in binary form must reproduce the above copyright
|
||||
notice, this list of conditions and the following disclaimer in the
|
||||
documentation and/or other materials provided with the distribution.
|
||||
* Neither the name of Sandia National Laboratories nor the names of its
|
||||
contributors may be used to endorse or promote products derived from
|
||||
this software without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
|
||||
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
|
||||
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
||||
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
||||
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
||||
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
|
||||
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGE.
|
||||
*/
|
||||
|
||||
package com.dfsek.terra.addons.noise.util;
|
||||
|
||||
import java.io.Serializable;
|
||||
import java.util.Arrays;
|
||||
import java.util.NoSuchElementException;
|
||||
|
||||
|
||||
public class HashMapDoubleDouble extends HashIntrinsic {
|
||||
private static final long serialVersionUID = 2109458761298324234L;
|
||||
private HashMapDoubleDouble.Entry[] table;
|
||||
|
||||
public HashMapDoubleDouble(int initialCapacity, float loadFactor) {
|
||||
super(initialCapacity, loadFactor);
|
||||
this.table = this.createTable(this.capMinus1 + 1);
|
||||
}
|
||||
|
||||
public HashMapDoubleDouble(int initialCapacity) {
|
||||
this(initialCapacity, 0.75F);
|
||||
}
|
||||
|
||||
public HashMapDoubleDouble() {
|
||||
this(16, 0.75F);
|
||||
}
|
||||
|
||||
public final boolean contains(double key) {
|
||||
int i = tableIndex(hashCodeDouble(key), this.capMinus1);
|
||||
|
||||
for(HashMapDoubleDouble.Entry e = this.table[i]; e != null; e = e.next) {
|
||||
if(e.key == key) {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
|
||||
public boolean containsValue(double value) {
|
||||
for(Entry entry : this.table) {
|
||||
for(Entry e = entry; e != null; e = e.next) {
|
||||
if(value == e.value) {
|
||||
return true;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
|
||||
public double get(double key) {
|
||||
int i = tableIndex(hashCodeDouble(key), this.capMinus1);
|
||||
|
||||
for(HashMapDoubleDouble.Entry e = this.table[i]; e != null; e = e.next) {
|
||||
if(key == e.key) {
|
||||
return e.value;
|
||||
}
|
||||
}
|
||||
|
||||
return 4.9E-324D;
|
||||
}
|
||||
|
||||
public double put(double key, double value) {
|
||||
int i = tableIndex(hashCodeDouble(key), this.capMinus1);
|
||||
|
||||
for(HashMapDoubleDouble.Entry e = this.table[i]; e != null; e = e.next) {
|
||||
if(key == e.key) {
|
||||
double oldValue = e.value;
|
||||
e.value = value;
|
||||
return oldValue;
|
||||
}
|
||||
}
|
||||
|
||||
this.addEntry(key, value, i);
|
||||
return 4.9E-324D;
|
||||
}
|
||||
|
||||
public void resize(int newCapacity) {
|
||||
int oldCapacity = this.table.length;
|
||||
if(oldCapacity == 1073741824) {
|
||||
this.threshold = 2147483647;
|
||||
} else {
|
||||
HashMapDoubleDouble.Entry[] newTable = this.createTable(newCapacity);
|
||||
this.capMinus1 = newCapacity - 1;
|
||||
this.transfer(newTable);
|
||||
this.table = newTable;
|
||||
this.threshold = (int) ((float) newCapacity * this.loadFactor);
|
||||
}
|
||||
}
|
||||
|
||||
public final HashMapDoubleDouble.Entry remove(double key) {
|
||||
int i = tableIndex(hashCodeDouble(key), this.capMinus1);
|
||||
HashMapDoubleDouble.Entry prev = this.table[i];
|
||||
|
||||
HashMapDoubleDouble.Entry e;
|
||||
HashMapDoubleDouble.Entry next;
|
||||
for(e = prev; e != null; e = next) {
|
||||
next = e.next;
|
||||
if(key == e.key) {
|
||||
--this.size;
|
||||
if(prev == e) {
|
||||
this.table[i] = next;
|
||||
} else {
|
||||
prev.next = next;
|
||||
}
|
||||
|
||||
return e;
|
||||
}
|
||||
|
||||
prev = e;
|
||||
}
|
||||
|
||||
return e;
|
||||
}
|
||||
|
||||
public void clear() {
|
||||
Arrays.fill(this.table, null);
|
||||
|
||||
this.size = 0;
|
||||
}
|
||||
|
||||
public long memoryEstimate(int ptrsize) {
|
||||
return (long) ptrsize * (long) (this.capMinus1 + this.size + 1) + (this.size * 64L / 4);
|
||||
}
|
||||
|
||||
public HashMapDoubleDouble.Iterator iterator() {
|
||||
return new HashMapDoubleDouble.Iterator();
|
||||
}
|
||||
|
||||
private void addEntry(double key, double value, int index) {
|
||||
HashMapDoubleDouble.Entry e = this.table[index];
|
||||
this.table[index] = new HashMapDoubleDouble.Entry(key, value, e);
|
||||
if(this.size++ >= this.threshold) {
|
||||
this.resize(2 * this.table.length);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
private void transfer(HashMapDoubleDouble.Entry[] newTable) {
|
||||
for(int j = 0; j < this.table.length; ++j) {
|
||||
HashMapDoubleDouble.Entry e = this.table[j];
|
||||
if(e != null) {
|
||||
this.table[j] = null;
|
||||
|
||||
HashMapDoubleDouble.Entry next;
|
||||
do {
|
||||
next = e.next;
|
||||
int i = tableIndex(hashCodeDouble(e.key), this.capMinus1);
|
||||
e.next = newTable[i];
|
||||
newTable[i] = e;
|
||||
e = next;
|
||||
} while(next != null);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
private HashMapDoubleDouble.Entry[] createTable(int capacity) {
|
||||
return new HashMapDoubleDouble.Entry[capacity];
|
||||
}
|
||||
|
||||
public HashMapDoubleDouble.Entry getEntry(double key) {
|
||||
int i = tableIndex(hashCodeDouble(key), this.capMinus1);
|
||||
|
||||
for(HashMapDoubleDouble.Entry e = this.table[i]; e != null; e = e.next) {
|
||||
if(key == e.key) {
|
||||
return e;
|
||||
}
|
||||
}
|
||||
|
||||
return null;
|
||||
}
|
||||
|
||||
|
||||
public static class Entry implements Serializable {
|
||||
private static final long serialVersionUID = 7972173983741231238L;
|
||||
private final double key;
|
||||
private double value;
|
||||
private HashMapDoubleDouble.Entry next;
|
||||
|
||||
public Entry(double key, double val, HashMapDoubleDouble.Entry n) {
|
||||
this.key = key;
|
||||
this.value = val;
|
||||
this.next = n;
|
||||
}
|
||||
|
||||
public final double setValue(double newValue) {
|
||||
double oldValue = this.value;
|
||||
this.value = newValue;
|
||||
return oldValue;
|
||||
}
|
||||
|
||||
public final double getKey() {
|
||||
return this.key;
|
||||
}
|
||||
|
||||
public final double getValue() {
|
||||
return this.value;
|
||||
}
|
||||
|
||||
public final int hashCode() {
|
||||
return hashCodeDouble(key) + hashCodeDouble(value);
|
||||
}
|
||||
|
||||
public final boolean equals(Object o) {
|
||||
HashMapDoubleDouble.Entry e = (HashMapDoubleDouble.Entry) o;
|
||||
return this.key == e.key && this.value == e.value;
|
||||
}
|
||||
|
||||
public final String toString() {
|
||||
return this.key + " = " + this.value;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
public class Iterator {
|
||||
HashMapDoubleDouble.Entry next;
|
||||
int index;
|
||||
HashMapDoubleDouble.Entry current;
|
||||
|
||||
Iterator() {
|
||||
if(HashMapDoubleDouble.this.size > 0) {
|
||||
while(this.index < HashMapDoubleDouble.this.table.length &&
|
||||
(this.next = HashMapDoubleDouble.this.table[this.index++]) == null) {
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
public HashMapDoubleDouble.Entry nextEntry() {
|
||||
HashMapDoubleDouble.Entry e = this.next;
|
||||
if(e == null) {
|
||||
throw new NoSuchElementException();
|
||||
} else {
|
||||
if((this.next = e.next) == null) {
|
||||
while(this.index < HashMapDoubleDouble.this.table.length &&
|
||||
(this.next = HashMapDoubleDouble.this.table[this.index++]) == null) {
|
||||
}
|
||||
}
|
||||
|
||||
this.current = e;
|
||||
return e;
|
||||
}
|
||||
}
|
||||
|
||||
public double next() {
|
||||
return this.nextEntry().value;
|
||||
}
|
||||
|
||||
public void remove() {
|
||||
if(this.current == null) {
|
||||
throw new IllegalStateException();
|
||||
} else {
|
||||
double k = this.current.key;
|
||||
this.current = null;
|
||||
HashMapDoubleDouble.this.remove(k);
|
||||
}
|
||||
}
|
||||
|
||||
public final boolean hasNext() {
|
||||
return this.next != null;
|
||||
}
|
||||
}
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user