Esempio n. 1
0
        /// <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);
        }
Esempio n. 2
0
        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);
        }
Esempio n. 3
0
        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);
        }