edaeab72ac
Wherever sensible the geometric traits now take `&self` arguments, so that implementations can be invoked as instance methods instead of static methods. This makes some idioms easier to express and generally allows for greater flexibility as the restructured methods can still be invoked like static methods. Fixes #39. |
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| benches | ||
| src | ||
| tests | ||
| .gitignore | ||
| .travis.yml | ||
| Cargo.toml | ||
| LICENSE | ||
| Makefile | ||
| README.md | ||
README.md
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.