Nodes# Coherent Noise

Coherent Noise

Simplex

Smooth gradient noise from an N dimensional simplex grid
Developed by Ken Perlin in 2001

Feature Scale = 100.0f

Effectively 1.0 / frequency

Seed Offset = 0

This value is added to the seed before generation
Doesn't affect the seed passed to child nodes
Useful if you have multiple nodes of the same type and want them to give different outputs

Output Min = -1.0f

Minimum bound of output range

Output Max = 1.0f

Maximum bound of output range

Super Simplex

Extra smooth gradient noise from an N dimensional simplex grid
Slower to generate than Simplex noise
Developed by K.jpg

Feature Scale = 100.0f

Effectively 1.0 / frequency

Seed Offset = 0

This value is added to the seed before generation
Doesn't affect the seed passed to child nodes
Useful if you have multiple nodes of the same type and want them to give different outputs

Output Min = -1.0f

Minimum bound of output range

Output Max = 1.0f

Maximum bound of output range

Perlin

Smooth gradient noise from N dimensional grid
Developed by Ken Perlin in 1983

Feature Scale = 100.0f

Effectively 1.0 / frequency

Seed Offset = 0

This value is added to the seed before generation
Doesn't affect the seed passed to child nodes
Useful if you have multiple nodes of the same type and want them to give different outputs

Output Min = -1.0f

Minimum bound of output range

Output Max = 1.0f

Maximum bound of output range

Value

Smooth gradient noise from N dimensional grid

Feature Scale = 100.0f

Effectively 1.0 / frequency

Seed Offset = 0

This value is added to the seed before generation
Doesn't affect the seed passed to child nodes
Useful if you have multiple nodes of the same type and want them to give different outputs

Output Min = -1.0f

Minimum bound of output range

Output Max = 1.0f

Maximum bound of output range

Cellular Value

Returns value of Nth closest cell
Value is generated using white noise

Minkowski P = 1.5f - Hybrid Lookup

Only affects Minkowski distance function
1 = Manhattan
2 = Euclidean

Grid Jitter = 1.0f - Hybrid Lookup

How much to displace cells from their uniform grid position
0.0 will output a uniform grid
Above 1.0 will cause grid artifacts

Size Jitter = 0.0f - Hybrid Lookup

Apply a random multiplier each cell's distance calculation
Causes more variation in cell size
Can cause grid artifacts

Feature Scale = 100.0f

Effectively 1.0 / frequency

Seed Offset = 0

This value is added to the seed before generation
Doesn't affect the seed passed to child nodes
Useful if you have multiple nodes of the same type and want them to give different outputs

Output Min = -1.0f

Minimum bound of output range

Output Max = 1.0f

Maximum bound of output range

Distance Function = Euclidean Squared - Enum

How distance to closest cells is calculated
Hybrid is EuclideanSquared + Manhattan

• Euclidean
• Euclidean Squared (Default)
• Manhattan
• Hybrid
• Max Axis
• Minkowski

Value Index = 0

Nth closest cell

Cellular Distance

Returns distance of Nth closest cell
Distance of Index0 and Index1 are combined according to return type
Returned value is always positive except when using Index0Sub1 and Index0 > Index1

Minkowski P = 1.5f - Hybrid Lookup

Only affects Minkowski distance function
1 = Manhattan
2 = Euclidean

Grid Jitter = 1.0f - Hybrid Lookup

How much to displace cells from their uniform grid position
0.0 will output a uniform grid
Above 1.0 will cause grid artifacts

Size Jitter = 0.0f - Hybrid Lookup

Apply a random multiplier each cell's distance calculation
Causes more variation in cell size
Can cause grid artifacts

Feature Scale = 100.0f

Effectively 1.0 / frequency

Seed Offset = 0

This value is added to the seed before generation
Doesn't affect the seed passed to child nodes
Useful if you have multiple nodes of the same type and want them to give different outputs

Output Min = -1.0f

Minimum bound of output range

Output Max = 1.0f

Maximum bound of output range

Distance Function = Euclidean Squared - Enum

How distance to closest cells is calculated
Hybrid is EuclideanSquared + Manhattan

• Euclidean
• Euclidean Squared (Default)
• Manhattan
• Hybrid
• Max Axis
• Minkowski

Distance Index 0 = 0

Nth closest cell

Distance Index 1 = 1

Nth closest cell

Return Type = Index0 - Enum

How to combine Index 0 & Index 1

• Index0 (Default)
• Index0Add1
• Index0Sub1
• Index0Mul1
• Index0Div1

Cellular Lookup

Returns value of closest cell
Value is generated at the cell center using the lookup source

Lookup - Node Lookup

Used to generate cell values

Minkowski P = 1.5f - Hybrid Lookup

Only affects Minkowski distance function
1 = Manhattan
2 = Euclidean

Grid Jitter = 1.0f - Hybrid Lookup

How much to displace cells from their uniform grid position
0.0 will output a uniform grid
Above 1.0 will cause grid artifacts

Size Jitter = 0.0f - Hybrid Lookup

Apply a random multiplier each cell's distance calculation
Causes more variation in cell size
Can cause grid artifacts

Feature Scale = 100.0f

Effectively 1.0 / frequency

Seed Offset = 0

This value is added to the seed before generation
Doesn't affect the seed passed to child nodes
Useful if you have multiple nodes of the same type and want them to give different outputs

Distance Function = Euclidean Squared - Enum

How distance to closest cells is calculated
Hybrid is EuclideanSquared + Manhattan

• Euclidean
• Euclidean Squared (Default)
• Manhattan
• Hybrid
• Max Axis
• Minkowski