forked from M-Labs/nalgebra
61 lines
2.3 KiB
Markdown
61 lines
2.3 KiB
Markdown
[](https://travis-ci.org/sebcrozet/nalgebra)
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nalgebra
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========
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**nalgebra** is a low-dimensional linear algebra library written for Rust targeting:
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* general-purpose linear algebra (still lacks a lot of features…).
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* real time computer graphics.
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* real time computer physics.
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An on-line version of this documentation is available [here](http://nalgebra.org/doc/nalgebra).
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## Using **nalgebra**
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All the functionality of **nalgebra** is grouped in one place: the root module `nalgebra::`. This
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module re-exports everything and includes free functions for all traits methods performing
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out-of-place operations.
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* You can import the whole prelude using:
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```.ignore
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use nalgebra::*;
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```
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The preferred way to use **nalgebra** is to import types and traits explicitly, and call
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free-functions using the `na::` prefix:
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```.rust
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extern crate nalgebra as na;
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use na::{Vec3, Rot3, Rotation};
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fn main() {
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let a = Vec3::new(1.0f64, 1.0, 1.0);
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let mut b = Rot3::new(na::zero());
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b.append_rotation_mut(&a);
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assert!(na::approx_eq(&na::rotation(&b), &a));
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}
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```
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## Features
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**nalgebra** is meant to be a general-purpose, low-dimensional, linear algebra library, with
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an optimized set of tools for computer graphics and physics. Those features include:
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* Vectors with predefined static sizes: `Vec1`, `Vec2`, `Vec3`, `Vec4`, `Vec5`, `Vec6`.
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* Vector with a user-defined static size: `VecN`.
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* Points with static sizes: `Pnt1`, `Pnt2`, `Pnt3`, `Pnt4`, `Pnt5`, `Pnt6`.
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* Square matrices with static sizes: `Mat1`, `Mat2`, `Mat3`, `Mat4`, `Mat5`, `Mat6 `.
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* Rotation matrices: `Rot2`, `Rot3`
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* Quaternions: `Quat`, `UnitQuat`.
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* Isometries (translation ⨯ rotation): `Iso2`, `Iso3`
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* Similarity transformations (translation ⨯ rotation ⨯ uniform scale): `Sim2`, `Sim3`.
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* 3D projections for computer graphics: `Persp3`, `PerspMat3`, `Ortho3`, `OrthoMat3`.
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* Dynamically sized heap-allocated vector: `DVec`.
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* Dynamically sized stack-allocated vectors with a maximum size: `DVec1` to `DVec6`.
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* Dynamically sized heap-allocated (square or rectangular) matrix: `DMat`.
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* Linear algebra and data analysis operators: `Cov`, `Mean`, `qr`, `cholesky`.
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* Almost one trait per functionality: useful for generic programming.
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