---
title: "PxrWorley"
canonical: "https://rmanwiki-25.pixar.com/space/REN25/20416390/PxrWorley"
format: markdown
---
![image](media://2b2d3309-486e-4b02-98b2-e7cda5493468)

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://4d8ab2fa-128d-4b9f-b95a-1941c0e41396) | ![dist_c2_euclidean.png](media://71e205df-df62-48ca-bb39-7809ec245507) | ![dist_c1c2_euclidean.png](media://81e49122-1445-4629-8861-74e9b878f783) | ![dist_c1-c2_euclidean.png](media://48f19e75-fbd4-4355-8ff7-d059b9c71b1b) |

#####   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://10ecdfc8-5d4d-46a1-89a4-ec0a4842291e) | ![dist_c2_euclidean_squared.png](media://bbf61b13-d2d4-4a41-b3ef-274ecfb1b756) | ![dist_c1c2_euclidean_squared.png](media://1f5a0fc8-f379-47fe-82e0-17fe88cf4dba) | ![dist_c1-c2_euclidean_squared.png](media://9a8c6670-d689-407a-a48a-a8918c7997ca) |

#####     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://bd189b91-9699-4c86-8c78-7f293209d8ab) | ![dist_c2_manhattan.png](media://5d093b46-788d-4329-88b5-011f748d7ba6) | ![dist_c1c2_manhattan.png](media://9756609a-ca1f-4ae1-b976-513cf4cdf0b4) | ![dist_c1-c2_manhattan.png](media://a4569325-adcb-48a7-a5a4-94967f9288ff) |

#####       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://3111e658-3382-4a91-82b6-26e542e10795) | ![dist_c2_chebyshev.png](media://216ab15d-0fef-428f-8e8e-ed223eafc4ab) | ![dist_c1c2_chebyshev.png](media://cc65f80b-088b-4b31-9280-5e68bf322a8d) | ![dist_c1-c2_chebyshev.png](media://be747adb-1adc-44c4-8634-e4e3ca3d471a) |

  

#####         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://8c0ebab6-1c1c-40f1-a29c-e4d2f1480a6c) | ![dist_c2_minkowski.png](media://c41e0dc0-5a05-4d8b-86ae-0da3e1ebdc85) | ![dist_c1c2_minkowski.png](media://5b3f7ffc-947b-4f5d-ab48-6d3910b5f78e) | ![dist_c1-c2_minkowski.png](media://77270772-28af-4edf-b937-d96449f5e509) |

 

> ⚠️ 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://e0fba284-07a5-4f37-8131-8c2ab549ca98) | ![jitter_0_25.png](media://679d5c7f-a231-4fcb-912a-11db0f783d09) | ![jitter_0_50.png](media://dc7a571a-31a1-4d61-92dd-7b57907a1cb2) | ![jitter_0_75.png](media://e7e4dcd7-b243-4d46-a1de-c4592800be96) | ![jitter_1_00.png](media://1e65bda2-fede-460e-9809-0b0cd5698541) |

#### C1

Multiplier for the distances to the first feature.

#### C2

Multiplier for the distance to the second feature.

#### Minkowski Exponent

  


#### 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.