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◆  Alloc()

allocator for common use.

◆  Init()

Initializes the noise. A permutationTablePower of 10 results in 1024 elements, which is a good compromise between too frequent repetition and memory consumption.

参数

[in]	seed	Start value for the random table generation.
[in]	permutationTablePower	This specifies the size of the permutation table, which will have (2 ^ permutationTablePower) entries. permutationTablePower must be [5..16], otherwise initialization fails.

返回

OK on success. IllegalArgumentError is returned if the permutationTablePower was out of range.

◆  GetSeed()

Returns the seed value the noise was initialized with.

返回

Seed value of that was passed to Init() or 0 if noise was not initialized.

◆  GetPermutationTablePower()

Returns the power of the permutation table the noise was initialized with.

返回

Value that was passed to Init() or 0 if noise was not initialized.

◆  GetPermutationTable()

Returns a pointer to the permutation table. The table is only valid as long as the class exists and no further Init() is called. All values are read-only. The permutation table has at least (1 << permutationTablePower) entries. Any additional entries are repetitions of the original elements.

返回

The permutation table or nullptr if noise was not initialized. Ownership doesn't change - the table is still owned by the NoiseRef class.

◆  GetGradientTable()

Fills an array with the gradient data.

参数

[in]	gradient3D	If this parameter is true, the table for the 3D gradient is returned, otherwise a 4D gradient is returned.
[in]	gradient	The gradient array to fill.

返回

OK on success.

◆  GetFbmTable()

Fills an array with the FBM data. The table must have been initialized with InitFbm, otherwise the function will fail.

参数

[in]	table	Table that was initialized with InitFbm.
[in]	fbm	The table array to fill.

返回

OK on success.

◆  GetRandomTable()

Returns a pointer to the random point table. Each component of each point is in the range of [0..1]. The table is only valid as long as the class exists and no further Init() is called. All values are read-only. The random table has at least (1 << permutationTablePower) entries. Any additional entries are repetitions of the original elements.

返回

The random table or nullptr if noise was not initialized. Ownership doesn't change - the table is still owned by the NoiseRef class.

◆  SNoise() [1/3]

Calculates an 'Improved Perlin Noise' value for three dimensions. The noise will repeat itself after a distance of (1 << permutationTablePower). Note that calling with Vector4d32 and the fourth (w) component set to zero delivers different results as the 4-dimensional tables are different.

参数

[in]

p

Point for noise calculation.

返回

Noise value in the range of [-1..1]

◆  SNoise() [2/3]

Calculates an 'Improved Perlin Noise' value for four dimensions. The noise will repeat itself after a distance of (1 << permutationTablePower).

参数

[in]

p

Point for noise calculation.

返回

Noise value in the range of [-1..1]

◆  SNoise() [3/3]

Calculates an 'Improved Perlin Noise' value for four dimensions. The noise will repeat itself after a distance of (1 << permutationTablePower). This is a convenience function that assigns the time to the fourth (w) component.

参数

[in]	p	Point for noise calculation.
[in]	time	Time (fourth dimension) for noise calculation.

返回

Noise value in the range of [-1..1]

◆  PeriodicSNoise() [1/2]

Calculates a periodic 'Improved Perlin Noise' value for three dimensions. The noise will repeat itself after a distance of repeatX/repeatY/repeatZ. Note that calling with Vector4d32 and the fourth (w) component set to zero delivers different results as the 4-dimensional tables are different. Periodic noise is more than 2x slower than regular noise. If you have repetitions that are a power of 2 use a noise with fitting permutationTable instead.

参数

[in]	p	Point for noise calculation.
[in]	repeatX	X repetition. Needs to be in the range of [2..(1 << permutationTablePower)].
[in]	repeatY	Y repetition. Needs to be in the range of [2..(1 << permutationTablePower)].
[in]	repeatZ	Z repetition. Needs to be in the range of [2..(1 << permutationTablePower)].

返回

Noise value in the range of [-1..1]

◆  PeriodicSNoise() [2/2]

Calculates a periodic 'Improved Perlin Noise' value for four dimensions. The noise will repeat itself after a distance of repeatX/repeatY/repeatZ/repeatT. Periodic noise is more than 2x slower than regular noise. If you have repetitions that are a power of 2 use a noise with fitting permutationTable instead.

参数

[in]	p	Point for noise calculation.
[in]	repeatX	X repetition. Needs to be in the range of [2..(1 << permutationTablePower)].
[in]	repeatY	Y repetition. Needs to be in the range of [2..(1 << permutationTablePower)].
[in]	repeatZ	Z repetition. Needs to be in the range of [2..(1 << permutationTablePower)].
[in]	repeatT	T repetition. Needs to be in the range of [2..(1 << permutationTablePower)].

返回

Noise value in the range of [-1..1]

◆  Voronoi() [1/2]

Calculates voronoi noise for three dimensions. The noise will repeat itself after a distance of (1 << permutationTablePower). Make sure to choose maximumOrder as small as possible to optimize speed. Also passing nullptr for index will increase calculation speed. Note that calling with Vector4d32 and the fourth (w) component set to zero delivers different results as the 4-dimensional tables are different.

参数

[in]	p	Point for noise calculation.
[in]	maximumOrder	Maximum order that will be calculated. This value must be in the range [1..3], otherwise undefined behaviour will happen.
[out]	distance	Pointer to an array that will be filled with distance values. It is guaranteed that distance[i] <= distance[i + 1]. The array must at least have maximumOrder elements, otherwise the routine will crash.
[out]	index	Nullptr or pointer to an array that will be filled with indices to the noise permutation table that correspond to the distance values. The array must at least have maximumOrder elements, otherwise the routine will crash.

◆  Voronoi() [2/2]

Calculates voronoi noise for four dimensions. The noise will repeat itself after a distance of (1 << permutationTablePower). Make sure to choose maximumOrder as small as possible to optimize speed. Also passing nullptr for index will increase calculation speed.

参数

[in]	p	Point for noise calculation.
[in]	maximumOrder	Maximum order that will be calculated. This value must be in the range [1..3], otherwise undefined behaviour will happen.
[out]	distance	Pointer to an array that will be filled with distance values. It is guaranteed that distance[i] <= distance[i + 1]. The array must at least have maximumOrder elements, otherwise the routine will crash.
[out]	index	Nullptr or pointer to an array that will be filled with indices to the noise permutation table that correspond to the distance values. The array must at least have maximumOrder elements, otherwise the routine will crash.

◆  InitFbm()

Initializes the Fractal Brownian Motion coefficients. The standard is lacunarity 2.0 and gain 0.5. http://code.google.com/p/fractalterraingeneration/wiki/Fractional_Brownian_Motion .

参数

[in]	lacunarity	Frequency multiplier between successive octaves, must be >0.0. A lacunarity of 2.0 means that the frequency doubles each octave.
[in]	gain	Value that shrinks the amplitude. Each octave the amplitude is multiplied by the gain. Values need to be >0.0.

返回

Reference to precomputed Fbm coefficients or nullptr if not enough memory.

◆  Fbm() [1/2]

Calculates Fractal Brownian Motion noise.

参数

[in]	table	Table that was initialized with InitFbm.
[in]	p	Point for noise calculation.
[in]	octaves	Number of octaves to be calculated in the range of [0..15]. The higher the number, the more computationally expensive the function.

返回

Noise value. The range dependens on the values passed to InitFbm.

◆  Fbm() [2/2]

Calculates Fractal Brownian Motion noise. Note that calling with Vector4d32 and the fourth (w) component set to zero delivers different results as the 4-dimensional tables are different.

参数

[in]	table	Table that was initialized with InitFbm.
[in]	p	Point for noise calculation.
[in]	octaves	Number of octaves to be calculated in the range of [0..15]. The higher the number, the more computationally expensive the function.

返回

Noise value. The range dependens on the values passed to InitFbm.

◆  Turbulence() [1/2]

Calculates Perlin's Turbulence function. Note that calling with Vector4d32 and the fourth (w) component set to zero delivers different results as the 4-dimensional tables are different.

参数

[in]	p	Point for noise calculation.
[in]	octaves	Number of octaves to be calculated. The higher the number, the more computationally expensive the function.
[in]	absolute	If true the absolute values of each octave are summed.

返回

Turbulence value, in the range of [-1.75..1.75]

◆  Turbulence() [2/2]

Calculates Perlin's Turbulence function.

参数

[in]	p	Point for noise calculation.
[in]	octaves	Number of octaves to be calculated. The higher the number, the more computationally expensive the function.
[in]	absolute	If true the absolute values of each octave are summed.

返回

Turbulence value, in the range of [-1.75..1.75]

◆  RidgedMultifractal() [1/2]

Calculates Musgraves Ridged Multifractal function. Note that calling with Vector4d32 and the fourth (w) component set to zero delivers different results as the 4-dimensional tables are different.

参数

[in]	table	Table that was initialized with InitFbm.
[in]	p	Point for noise calculation.
[in]	octaves	Number of octaves to be calculated. The higher the number, the more computationally expensive the function.
[in]	offset	Must be >0.0. Offset where the details begin to ramp sharply. A good start value is 1.0.
[in]	threshold	Must be >0.0. The higher the value, the more sharp details / peaks. A good start value is 2.0.

返回

Multifractal value. The range depends on the used parameters.

◆  RidgedMultifractal() [2/2]

Calculates Musgraves Ridged Multifractal function.

参数

[in]	table	Table that was initialized with InitFbm.
[in]	p	Point for noise calculation.
[in]	octaves	Number of octaves to be calculated. The higher the number, the more computationally expensive the function.
[in]	offset	Must be >0.0. Offset where the details begin to ramp sharply. A good start value is 1.0.
[in]	threshold	Must be >0.0. The higher the value, the more sharp details / peaks. A good start value is 2.0.

返回

Multifractal value. The range depends on the used parameters.

• maxon
• NoiseInterface