---
title: "PxrWorley"
canonical: "https://rmanwiki-26.pixar.com/space/REN26/19661725/PxrWorley"
format: markdown
---
![image](media://71e34ca7-f920-4fa0-bfa5-a6a64025fa09)

Like all texture style nodes, this node takes a manifold that describes either a 2D or 3D domain to apply a Worley noise texture to. The default behavior, if no manifold is attached, is to apply over P in 3D. This node computes [Worley noise](http://www.rhythmiccanvas.com/research/papers/worley.pdf), as described by Steven Worley.

## Input Parameters

#### Surface Position

The noise can be computed based on the **Current Position** or the **Undisplaced Position** (the position of the surface prior to displacement).

If you want your displacement and shading patterns to match, use the **Undisplaced Position**.

#### Frequency

Controls the size of the cells. Higher frequencies make smaller cells.

#### Distance Metric

The means to measure distances to neighboring cells. Manhattan distance gives more rectangular shapes and Euclidian distance gives more spherical shapes.

##### Euclidean

<span style="color: #333333">Computes the euclidean distance to the nearest points. It looks a bit more pointy than Squared Euclidean distance.</span>

|  |  |  |  |
| --- | --- | --- | --- |
| ![dist_c1_euclidean.png](media://edb5e916-a1cc-4f2e-a864-e89806263d90) | ![dist_c2_euclidean.png](media://a03fc45d-e780-47aa-9eb2-cca1731ae270) | ![dist_c1c2_euclidean.png](media://d6b2acd2-48eb-434b-af1a-f6d8c189f75f) | ![dist_c1-c2_euclidean.png](media://4ee9889e-01b9-4b8c-8ff1-24c50b5ed761) |

#####   Euclidean Squared  

   Computes the squared euclidean distance to the nearest points. It looks rounder than pure Euclidean distance and more organic.   

|  |  |  |  |
| --- | --- | --- | --- |
| ![dist_c1_euclidean_squared.png](media://58715854-547a-40f7-b4e6-f40672354c75) | ![dist_c2_euclidean_squared.png](media://6143b257-0ee3-4259-b5e7-4787d15a2f9c) | ![dist_c1c2_euclidean_squared.png](media://7de5d87b-57f2-46ec-8d27-3e724816a5e1) | ![dist_c1-c2_euclidean_squared.png](media://05c91302-6f3f-417b-892f-360e2ca1f76c) |

#####     Manhattan    

     Inspired by the grid-like organization of Manhattan, this is the distance to the nearest points when you can only travel around the cell's boundaries.     

|  |  |  |  |
| --- | --- | --- | --- |
| ![dist_c1_manhattan.png](media://03c44346-d5cd-4055-9782-d9258e7090af) | ![dist_c2_manhattan.png](media://dbbec41a-e550-40a9-8414-9084999ab865) | ![dist_c1c2_manhattan.png](media://72e4b23e-edd6-4c76-9037-690aafe59efe) | ![dist_c1-c2_manhattan.png](media://73de167d-a0d3-40ed-aa0d-6c84de6449a4) |

#####       Chebyshev      

       Named after [Pafnutty Chebyshev](https://en.wikipedia.org/wiki/Pafnuty_Chebyshev), it is also known as the Chessboard Distance. It is somewhat similar to the Manhattan distance, but with 45 degrees rotation.   

|  |  |  |  |
| --- | --- | --- | --- |
| ![dist_c1_chebyshev.png](media://ed49b007-9216-4225-98bf-a4d4a3c8bf8f) | ![dist_c2_chebyshev.png](media://f7673137-44f9-4cbc-a0e7-f8a0d77feb8d) | ![dist_c1c2_chebyshev.png](media://81e9fbfe-ef3a-46dc-90f6-9e2d39631de2) | ![dist_c1-c2_chebyshev.png](media://c81eec4a-ef57-4081-8bca-043ff42a5ae6) |

   

#####         Minkowski        

          [Minkowski](https://en.wikipedia.org/wiki/Hermann_Minkowski) is a generalization of both euclidean and Manhattan distance. The exponent will make the pattern transition from Euclidian to Manhattan.      

|  |  |  |  |
| --- | --- | --- | --- |
| ![dist_c1_minkowski.png](media://a70f80fe-74b2-4870-91ef-b4893c95b421) | ![dist_c2_minkowski.png](media://298a22f2-4629-424d-80a6-bb14900ee50b) | ![dist_c1c2_minkowski.png](media://4ab56618-636c-44b4-b2fa-8f0342a48130) | ![dist_c1-c2_minkowski.png](media://c3fbbd6e-9cac-4003-a5fd-b87dac1f1926) |

   

> ⚠️ Minkowski is more expensive than the other distance metrics, but it is fine for displacement as you will pay the cost only once when the geometry is displaced.

  


#### Jitter

Controls the distortion of the cells.

|  |  |  |  |  |
| --- | --- | --- | --- | --- |
| ![jitter_0_00.png](media://68dcbc70-07c3-41d9-9854-f4320cdb0f7e) | ![jitter_0_25.png](media://03bac060-2fa3-4964-95d7-184755cb7258) | ![jitter_0_50.png](media://85fde13d-138a-4e0a-99e0-29dae0f25b58) | ![jitter_0_75.png](media://807fc894-0a4d-44ae-874d-52164b8ce322) | ![jitter_1_00.png](media://cfa4e514-b74a-4f42-b2b7-61ddff168329) |

#### C1

Multiplier for the distances to the first feature.

#### C2

Multiplier for the distance to the second feature.

#### Minkowski Exponent


Makes the distance transition smoothly from Manhattan (1.0) to Euclidean (2.0) to weird un-explored territories.


![minkowskiExp_c1.png](media://cd8d7fe0-3062-4e29-9ac9-5ee14e069033)


![minkowskiExp_c2.png](media://21ef3043-63dc-45de-9b37-79d251ddf5c9)


#### Shape

Modifies the computed distances to create different shapes. The example below uses c1 = 1.0 and c2 = 0.0.


#### Clamp Output

Causes resulting distances to be clamped to the range 0.0 to 1.0.

<sup> </sup><sup>**c1**</sup><sup>: 1.0    </sup><sup>**c2**</sup><sup>: -0.95    </sup><sup>**distancemetric**</sup><sup>: Euclidean</sup>

  


  


#### Invert

Inverts the final pattern.

  


#### Random Scale

This will randomly scale the features' amplitude and give a slightly more regular appearance.

#### Random Scale Center

This is applying a an offset to the signal before applying the random scale. Use this to create more variations.

  


#### Manifold

The manifold over which to apply the noise. (The default is P).

You can connect a 3D or 2D manifold.

  


### Adjust Output

#### Color Scale

A multiplier for the color values in a texture can be used to adjust brightness or manipulate individual color channels

  


#### Color Offset

Apply an offset to the result, shifting the colors of the result

  


#### Float Scale

Scalar Float value

#### Float Offset

Float Offset value

  


## Output Parameters

#### resultF

The result of Worley noise texture.

#### resultRGB

The texture as a monochrome color.