#lerp #math #interpolate #numeric #extrapolate

macro lerp_derive

Generic linear interpolation and extrapolation, automatically implemented for a variety of numeric types

1 unstable release

0.4.0 Jan 19, 2021

#59 in #numeric

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1,334 downloads per month
Used in 2 crates (via lerp)

MIT license

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Build Status

Linear interpolation and iteration, automatically implemented over most float-compatible types.

Just need to know what's halfway between three and five?

use lerp::Lerp;

assert_eq!(3.0.lerp(5.0, 0.5), 4.0);

Want to iterate across some points in that range?

// bring the trait into scope
use lerp::LerpIter;

// iterate and produce four items evenly spaced between 3.0 and 5.0
// note that the default, open iterator does not include both endpoints
// this makes chaining lerping iterators simpler
let items: Vec<_> = 3.0_f64.lerp_iter(5.0, 4).collect();
assert_eq!(vec![3.0, 3.5, 4.0, 4.5], items);

// closed iterators return both ends
assert_eq!(vec![3.0, 5.0], 3.0.lerp_iter_closed(5.0, 2).collect::<Vec<_>>());

Of course, the real benefit is that it's derivation is broad enough that it also covers types such as num::Complex<T>. If you have an array-processing library, and the arrays are T: Add<Output = T> + Mul<F: Float, Output = T>, it'll just work for them as well.

Deriving Lerp

As well as working for individual float values, the crate also provides a derive macro, available with the derive feature, which will be able to generate an implementation automatically.

This derive implementation will lerp each field of the struct independently and assumes a generic implementation of Lerp over Float types. If any of the fields is generic only over one of the float values (f32, f64) that can be specified by the #[lerp(f32)] or #[lerp(f64)] attributes respectively.

If you would like for the lerp implementation to ignore a field (or if it does not derive lerp) you can use the #[lerp(skip)] or #[lerp(ignore)] attributes which will produce the value, untouched from the left value.

Not all types are supported in this derive macro. See the github issue for discussion and more information.

lerp = { version = "0.4", features = ["derive"] }
use lerp::Lerp;

#[derive(Lerp, PartialEq, Debug)]
struct Data {
    a: f64,
    b: f64

    Data { a: 0.0, b: 1.0 }.lerp(Data { a: 1.0, b: 0.0 }, 0.5),
    Data { a: 0.5, b: 0.5 }

More derive examples can be seen in the tests


lerp = "0.4"


Auto-built from Travis: https://coriolinus.github.io/lerp-rs/


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