nalgebra

nalgebra is a low-dimensional linear algebra library written for Rust targeting:

• general-purpose linear algebra (still lacks a lot of features…).
• real time computer graphics.
• real time computer physics.

An on-line version of this documentation is available here.

Using nalgebra

All the functionality of nalgebra is grouped in one place: the root module nalgebra::. This module re-exports everything and includes free functions for all traits methods doing out-of-place modifications.

• You can import the whole prelude using:

use nalgebra::*;

The preferred way to use nalgebra is to import types and traits explicitly, and call free-functions using the na:: prefix:

extern crate "nalgebra" as na;
use na::{Vec3, Rot3, Rotation};

fn main() {
    let     a = Vec3::new(1.0f64, 1.0, 1.0);
    let mut b = Rot3::new(na::zero());

    b.append_rotation(&a);

    assert!(na::approx_eq(&na::rotation(&b), &a));
}

Features

nalgebra is meant to be a general-purpose, low-dimensional, linear algebra library, with an optimized set of tools for computer graphics and physics. Those features include:

• Vectors with static sizes: Vec0, Vec1, Vec2, Vec3, Vec4, Vec5, Vec6.
• Points with static sizes: Pnt0, Pnt1, Pnt2, Pnt3, Pnt4, Pnt5, Pnt6.
• Square matrices with static sizes: Mat1, Mat2, Mat3, Mat4, Mat5, Mat6 .
• Rotation matrices: Rot2, Rot3, Rot4.
• Quaternions: Quat, UnitQuat.
• Isometries: Iso2, Iso3, Iso4.
• 3D projections for computer graphics: Persp3, PerspMat3, Ortho3, OrthoMat3.
• Dynamically sized vector: DVec.
• Dynamically sized (square or rectangular) matrix: DMat.
• A few methods for data analysis: Cov, Mean.
• Almost one trait per functionality: useful for generic programming.
• Operator overloading using the double trait dispatch trick. For example, the following works:

extern crate "nalgebra" as na;
use na::{Vec3, Mat3};

fn main() {
    let v: Vec3<f64> = na::zero();
    let m: Mat3<f64> = na::one();

    let _ = m * v;      // matrix-vector multiplication.
    let _ = v * m;      // vector-matrix multiplication.
    let _ = m * m;      // matrix-matrix multiplication.
    let _ = v * 2.0f64; // vector-scalar multiplication.
}