---
title: "PxrWorley"
canonical: "https://rmanwiki-27.pixar.com/space/REN27/542225558/PxrWorley"
format: markdown
---
![image](media://6ce1be1b-0063-4497-a799-bc71b9f619cf)

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://f7beb8c5-86bf-41d1-9c3f-a45f05ac8bf9) | ![dist_c2_euclidean.png](media://0c701ef0-6e6a-430c-bd77-1b5d32b54ac3) | ![dist_c1c2_euclidean.png](media://526a98b0-4920-46e4-9c01-06f4a0ee4b53) | ![dist_c1-c2_euclidean.png](media://0cd686f6-8502-42e7-94f1-e3097e92d6d8) |

#####   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://dbcd8578-c029-48af-b60d-44b113259319) | ![dist_c2_euclidean_squared.png](media://bcdec5f4-4485-41a2-b112-d201d0c84352) | ![dist_c1c2_euclidean_squared.png](media://e1c16001-e316-4eee-bf62-51bd581ffe7e) | ![dist_c1-c2_euclidean_squared.png](media://b84fb51f-4b7d-4ffd-9754-0347ff068f84) |

#####     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://b24cd5aa-e35c-4628-a374-8be2d700d5dc) | ![dist_c2_manhattan.png](media://78661930-b5f9-4fbb-a0b9-83295932bc21) | ![dist_c1c2_manhattan.png](media://ea9231a3-210c-46e4-9d57-404bc982a192) | ![dist_c1-c2_manhattan.png](media://8c49817a-182f-49e6-885a-4ffecc2dd322) |

#####       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://22760764-6cc4-4433-b92c-f5e8e929abe6) | ![dist_c2_chebyshev.png](media://8fd930ec-c816-4be9-8325-0d7192828ef0) | ![dist_c1c2_chebyshev.png](media://747d0200-01f0-4953-8d1a-80b59d36d10d) | ![dist_c1-c2_chebyshev.png](media://745e7a29-c89e-4f14-a6cb-780085b9ff26) |

   

#####         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://51104487-3fd3-4c1c-a78a-43c1fa70f08d) | ![dist_c2_minkowski.png](media://1d324ca1-806c-49ee-9cfb-34c846a6c0b3) | ![dist_c1c2_minkowski.png](media://a55f0143-ae7c-4e36-8286-4ce15a39d0d1) | ![dist_c1-c2_minkowski.png](media://d0cdab74-bfa7-4474-82ea-60dad09c7147) |

   

> ⚠️ 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://61acab6a-160e-4079-8b39-3218bf9c2f55) | ![jitter_0_25.png](media://c0fdb41b-6a64-4736-8687-901333604fda) | ![jitter_0_50.png](media://de0c1a84-e4a4-47f7-a45d-633bbee6b164) | ![jitter_0_75.png](media://3bca5cdf-78b1-4432-b6bb-4e3f95a8816d) | ![jitter_1_00.png](media://6bef8dc7-e7ee-4f9e-aa54-695ef6211abb) |

#### 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://ea782203-46d1-4ff1-9658-af2aa264cf55)


![minkowskiExp_c2.png](media://95e5dc4e-5446-498c-80ac-4629d392ef27)


#### 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.