Custom Types

If you want to make something but don’t see how to do it with only what noiz provides, you may need to make a custom noise type. This page covers some ideas for how to do that.

• Custom modifiers: If you want to change or morph the output of a noise function(s) in a custom way, you’ll want to make a NoiseFunction. If your modifier is math based, you can ignore the supplied NoiseRng. If you are passing it to other noise functions, consider calling NoiseRng::re_seed, which will prevent repetition between the noise functions.

• Custom noise algorithms: If you want to make a unique algorithm, that will be a NoiseFunction. To drive randomness, use the supplied NoiseRng paired with types that are NoiseRngInputs. To make that output meaningful, use AnyValueFromBits or other functions in rng. If you are dividing floats that may be zero, see also force_float_non_zero. Consider also differentiating the function analytically with calculus. Note also that many noise algorithms should be made generic over a Partitioner when possible.

• Custom shapes: If you want to create uniquely shaped noise, you’ll need a custom Partitioner, DomainCell, etc. Depending on how you want it to be used, there’s a variety of traits you may wish to implement. See the cells module for more information.

• Custom curves: Noiz is powered by bevy_math, so you can use and create Curves of your own. If you want this to be usable in the context of derivatives, make sure to implement SampleDerivative.

• Custom layers: If you want to modify an input across a LayeredNoise’s layers, you’ll need to make a custom noise layer. See the layering module for examples of this.

• Custom layer configuring: If you want more than Normed, NormedByDerivative and Persistence, see the layering module. Also note that depending on what you want, it may be more performant to make a separate layering system. For example, masking in noiz could be implemented as layering, but is faster through Masked, etc.

• Custom inputs: If you want to use custom noise inputs, use RawNoise instead of Noise. You may build more abstractions from there if you wish.

• Custom outputs: If you want to do something that can’t be represented as a NoiseFunction and want full control, use Sampleable::sample_raw, which provides access to the entropy for that sample.

Regardless of what you’re making, keep these ideas in mind:

• Be careful when working with WithGradient values; remember the chain rule.
• Always #[inline] your functions. This will make the Noise-level functions very fast. The caller can decide whether to inline the final noise function or not.