nalgebra 0.2.15

Linear algebra library for computer physics, computer graphics and general low-dimensional linear algebra for Rust.
docs.rs failed to build nalgebra-0.2.15
Please check the build logs for more information.
See Builds for ideas on how to fix a failed build, or Metadata for how to configure docs.rs builds.
If you believe this is docs.rs' fault, open an issue.
Visit the last successful build: nalgebra-0.32.5

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 multidispatch.