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 performing out-of-place operations.

Thus, you can import the whole prelude using:

use nalgebra::*;

However, the recommended 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::{Vector3, Rotation3, Rotation};

fn main() {
    let     a = Vector3::new(1.0f64, 1.0, 1.0);
    let mut b = Rotation3::new(na::zero());

    b.append_rotation_mut(&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 predefined static sizes: Vector1, Vector2, Vector3, Vector4, Vector5, Vector6.
• Vector with a user-defined static size: VectorN (available only with the generic_sizes feature).
• Points with static sizes: Point1, Point2, Point3, Point4, Point5, Point6.
• Square matrices with static sizes: Matrix1, Matrix2, Matrix3, Matrix4, Matrix5, Matrix6 .
• Rotation matrices: Rotation2, Rotation3
• Quaternions: Quaternion, UnitQuaternion.
• Isometries (translation ⨯ rotation): Isometry2, Isometry3
• Similarity transformations (translation ⨯ rotation ⨯ uniform scale): Similarity2, Similarity3.
• 3D projections for computer graphics: Persp3, PerspMatrix3, Ortho3, OrthoMatrix3.
• Dynamically sized heap-allocated vector: DVector.
• Dynamically sized stack-allocated vectors with a maximum size: DVector1 to DVector6.
• Dynamically sized heap-allocated (square or rectangular) matrix: DMatrix.
• Linear algebra and data analysis operators: Covariance, Mean, qr, cholesky.
• Almost one trait per functionality: useful for generic programming.