Maths

Easing functions and probability distributions for Kotlin Multiplatform.

Animations and randomness, done right

Two things come up constantly when building interactive UIs, simulations, or generative content: smooth transitions and controlled randomness. Most projects end up copy-pasting easing formulas from the web and rolling ad-hoc random distributions. Maths packages both into a small, tested library.

Easing functions

28 easing functions organized into Ease.In, Ease.Out, and Ease.InOut, plus basic ones like linear, stuck, and immediate. All implement Ease.Fn — a functional interface that takes progress (0.0–1.0) and returns the eased value.

import io.peekandpoke.ultra.maths.Ease

// Simple easing
val progress = 0.5
val eased = Ease.Out.cubic(progress)  // 0.875

// Bind to a range: ease from 0 to 100
val bound = Ease.InOut.sine.bindFromTo(0, 100)
println(bound(0.5))  // 50.0 (midpoint of sine InOut)

// Time-based: animate from 0 to 255 over 500ms
val timed = Ease.Out.quad.timed(from = 0, to = 255, duration = 500.milliseconds)
while (!timed.isDone) {
    val value = timed()  // current eased value based on elapsed time
}

Probability distributions

Four bucketed distributions — uniform, truncated normal, exponential, and triangular — all sampled via inverse-CDF interpolation. Distributions can be inverted, reversed, and blended.

import io.peekandpoke.ultra.maths.stochastic.BucketedDistribution
import kotlin.random.Random

val normal = BucketedDistribution.createTruncatedNormal(mean = 0.5, std = 0.15)
val sample = normal.sample(Random)  // values clustered around 0.5

// Map to a custom range
val damage = normal.sample(Random, from = 10.0, to = 50.0)

// Blend two distributions
val skewed = normal.blend(
    BucketedDistribution.createExponential(lambda = 2.0),
    ratio = 0.3,
)

What you get