SciRust

Scientific computing library written in Rust programming language.

The objective is to design a generic library which can be used as a backbone for scientific computing.

Current emphasis is less on performance and more on providing a comprehensive API.

NOTE: The library currently doesn't build against the stable release of Rust. It builds against the nightly release of Rust. This scenario is likely to stay till Q3 2015.

Current areas of focus

Fundamental algebraic structures
Matrices
Linear algebra
Statistics
Signal processing

For more details and examples, see the API Documentation .

A discussion group is setup at SciRust Google Group.

Features

General

Pure Rust implementation
Focus on generic programming
Extensive unit tests for all features
Column major implementation

Matrices

Generic matrix class supporting various data-types (u8, i8, u16, i16, ... , f32, f64, Complex32, Complex64)
Views over parts of matrices
Comprehensive support for operations on matrices.
Views over sub-matrices with similar operations.
Special support for triangular matrices.

Linear algebra

Solving systems of linear equations
LDU factorization
Rank, Determinant, Inverse

About Rust and Building the project

If you are unfamiliar with Rust, you are recommended to go through The Rust Guide.

The library can be built and used using Cargo which is the official dependency management and build tool for Rust.

Working with matrices requires a lot of low level code. As a user of the library, we expect that you won't have to write the low level code yourself. If you are reading or debugging through the source code of the library, you would see a lot of low level code. Familiarity with following guides will help you sail through them.

The library code is full of unit tests. These unit tests serve multiple purposes

Making sure that the functions work as advertised.
Extensively testing those functions which use unsafe and low level features of Rust.
Learning about how to use the library features.