nalgebra 0.8.1

Linear algebra library for computer physics, computer graphics and general low-dimensional linear algebra for Rust.

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.