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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. ## 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: ```.ignore use nalgebra::*; ``` However, the recommended way to use **nalgebra** is to import types and traits explicitly, and call free-functions using the `na::` prefix: ```.rust 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.