nalgebra 0.6.0

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

Build Status

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.

  • 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_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: Vec1, Vec2, Vec3, Vec4, Vec5, Vec6.
  • Vector with a user-defined static size: VecN (available only with the generic_sizes feature).
  • Points with static sizes: Pnt1, Pnt2, Pnt3, Pnt4, Pnt5, Pnt6.
  • Square matrices with static sizes: Mat1, Mat2, Mat3, Mat4, Mat5, Mat6 .
  • Rotation matrices: Rot2, Rot3
  • Quaternions: Quat, UnitQuat.
  • Isometries (translation ⨯ rotation): Iso2, Iso3
  • Similarity transformations (translation ⨯ rotation ⨯ uniform scale): Sim2, Sim3.
  • 3D projections for computer graphics: Persp3, PerspMat3, Ortho3, OrthoMat3.
  • Dynamically sized heap-allocated vector: DVec.
  • Dynamically sized stack-allocated vectors with a maximum size: DVec1 to DVec6.
  • Dynamically sized heap-allocated (square or rectangular) matrix: DMat.
  • Linear algebra and data analysis operators: Cov, Mean, qr, cholesky.
  • Almost one trait per functionality: useful for generic programming.