/// <summary>
/// Method that returns a Turbulence noise with the speficied parameters that has a customizable filter.
/// </summary>
/// <param name="period">Size of the noise.</param>
/// <param name="steps">Detail of the noise</param>
/// <param name="seed">The seed used</param>
/// <returns>A SumNoise with several noises.</returns>
public static Noise TurbulenceNoise(float period, int steps, int seed)
{
SumNoise sumNoise = new SumNoise();
Random rdm = new Random(seed);
BasicGradientNoise noise;
float[] weights = new float[steps];
weights[0] = 0.5f;
float weightSum = 0.5f;
for (int i = 1; i < steps; i++)
{
weights[i] = weights[i - 1] * 0.5f;
weightSum += weights[i];
}
noiseSumFunction simpleSum = (float a, float b) => a + b;
noiseSumFunction lastFunc = (float a, float b) => ((a + b) - 0.5f) * 2f;
//float weight;
for (int i = 0; i < steps; i++)
{
noise = new BasicGradientNoise(rdm.Next(), period);
float weight = weights[i] + (weights[i] / weightSum) * (1f - weightSum);
noise.filter = (float value, Vector2 position) => Math.Abs(value) * weight;
//noise.filter = (float value, Vector2 position) => (Math.Abs(value) - 0.5f)*2f*weight;
if (i != steps - 1)
{
sumNoise.addNoise(noise, (float a, float b) => a + b);
}
else
{
sumNoise.addNoise(noise, (float a, float b) => ((a + b) - 0.5f) * 2f);
}
period *= 0.5f;
}
//sumNoise.filter = (float a, Vector2 b) => { return (a + 1f) / 2f; };
sumNoise.filter = (float a, Vector2 b) => a;
return(sumNoise);
}
public static Noise MixedNoise(float period, int BasicRandomNoiseSteps, int BasicGradientNoiseSteps, int seed)
{
SumNoise sumNoise = new SumNoise();
Random rdm = new Random(seed);
Noise noise;
int steps = BasicRandomNoiseSteps + BasicGradientNoiseSteps;
float[] weights = new float[steps];
weights[0] = 0.5f;
float weightSum = 0.5f;
for (int i = 1; i < steps; i++)
{
weights[i] = weights[i - 1] * 0.5f;
weightSum += weights[i];
}
for (int i = 0; i < steps; i++)
{
float weight = weights[i] + (weights[i] / weightSum) * (1f - weightSum);
if (i < BasicRandomNoiseSteps)
{
noise = new BasicRandomNoise(rdm.Next(), period);
noise.filter = (float value, Vector2 position) => value * weight;
}
else
{
noise = new BasicGradientNoise(rdm.Next(), period);
noise.filter = (float value, Vector2 position) => (Math.Abs(value) - 0.5f) * 2f * weight;
}
sumNoise.addNoise(noise, (float a, float b) => a + b);
period *= 0.5f;
}
return(sumNoise);
}
public static Noise RegularNoise(float period, int steps, int seed)
{
if (steps == 0)
{
throw new Exception("Can't create a noise regular noise with 0 steps");
}
SumNoise sumNoise = new SumNoise();
Random rdm = new Random(seed);
BasicGradientNoise noise;
float[] weights = new float[steps];
weights[0] = 0.5f;
float weightSum = 0.5f;
for (int i = 1; i < steps; i++)
{
weights[i] = weights[i - 1] * 0.5f;
weightSum += weights[i];
}
//float weight;
noiseSumFunction simpleSum = (float a, float b) => { return(a + b); };
for (int j = 0; j < steps; j++)
{
noise = new BasicGradientNoise(rdm.Next(), period);
float weight = weights[j] / weightSum;
noise.filter = (float value, Vector2 position) => { return(value * (weight)); };
sumNoise.addNoise(noise, simpleSum);
period *= 0.5f;
}
return(sumNoise);
}
