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Nifty is a general-purpose numerical computing library for the Swift programming language, made with performance and ease-of-use in mind.

Getting Started

Not sure if Nifty is worth the effort of installing? Check out a simple demo project or peruse the documentation to help you decide.

Nifty is being developed on Ubuntu 14.04/16.04 and on macOS Sierra. Our goal is to stay current as Swift develops, so make sure to install the latest release.

Xcode Installation

Xcode users can just use the included project file. Simply drag the project file into your own Xcode project and import Nifty at the top of any files in which you wish to use Nifty!

Nifty uses BLAS and LAPACK. When built with Xcode, these are provided by the Accelerate framework. Since Accelerate is installed on macOS by default, no additional installation steps are needed.

Swift Package Manager Installation

Linux users (and those on macOS who prefer not to use Xcode) can install Nifty using the Swift Package Manager.

Nifty uses BLAS and LAPACK. When built with the Swift Package Manager, Nifty uses the C interface LAPACKE and the optimized library OpenBLAS. These can be installed with the following commands: - Ubuntu: sudo apt-get install liblapack3 liblapacke liblapacke-dev libopenblas-base libopenblas-dev - Mac: brew install homebrew/dupes/lapack homebrew/science/openblas

Once the dependencies are installed, using Nifty in your project simply requires that you create/modify your project manifest file to point to this repository as a dependency, and then import Nifty in whatever files you want to use it. Your project can then be built by simply running swift build.

Refer to the aforementioned demo project to see an example of what your project manifest (the file called Package.swift) should look like and how easy it is to use Nifty!


Nifty is intended to be simple and easy to use. For this reason, we’ve decided to structure things similarly to MATLAB. In fact, many (most) of the function names in Nifty are the same as MATLAB. The hope is that MATLAB users will feel right at home and that users of similar packages (e.g. NumPy) will have an easy transition as well, making adoption as smooth as possible for as many people as possible. Check out the API!


If you’re having troubles, you may find the following helpful:

The system libraries used by Nifty are provided by the Nifty-libs package. This is used internally by Nifty so you shouldn’t ever need to reference it. One complication that can arise though is if the installed system libraries are in a location not on your linker search path. In that case, you’ll need to tell the linker where to find them when you build, e.g. swift build -Xlinker -L/usr/local/opt/lapack/lib -Xlinker -L/usr/local/opt/openblas/lib

If you decide to use a different system library for one of the required system modules, you’ll need to modify the Nifty-libs module map once the package manager has downloaded the Packages folder.

Nifty Features

Nifty is really new and (obviously) not complete. The library is constantly expanding—if it doesn’t yet have what you need, it will soon! Either come back later and check Nifty out when it’s a little farther along, or, consider contributing!

We are currently working on getting the core set of general math and linear algebra functions finished: - general functions and definitions used throughout Nifty - matrix definition and linear algebra functionality - vector and tensor data structures - wrappers on glibc/math.h - basic functions related to statistics and probability

Once the core set of features is complete, our next focus will be on developing a core set of AI and machine learning functionality.

See our status page for details on the implementation status of all features.

Tests and Benchmarks

Nifty uses the XCTest framework to manage unit tests. Tests can be run directly from Xcode or, if using the Swift Package Manager, by executing swift test in the repository root directory.

The goal is for Nifty to provide correctness and performance similar to other numerical computing standards. We’ll be testing and benchmarking mainly against MATLAB and NumPy. Check out the status page to see where the test coverage is currently at.

You can check out the results of some simple benchmarks here.

Goals & Scope

The goals of Nifty can be summarized as follows: - Provide a viable alternative to packages such as NumPy and MATLAB for those who wish to develop in Swift, whether on macOS or Linux. - Develop fun and interesting code, especially in the areas of artificial intelligence and machine learning. - Do as much in Swift as possible, resorting to external libraries only when necessary. - Make exploration of the code as simple as possible, through plain organization and clean, easy-to-read code. - Serve as a learning opportunity for those wishing to explore numerical computing.

Nifty is intended to be broad in scope; almost any generally interesting tool related to numerical or scientific computing, data structures, algorithms, etc. is fair game. A few of the things that Nifty does not currently intend to provide are listed below. These items would take a lot of effort to do correctly and would distract from getting the core functionality done so, for the time being, we’d rather defer to other projects. - Graphical stuff, e.g. matplotlib - Time series stuff, e.g. pandas - Modeling and simulation stuff, e.g. simulink


All contributions are welcome—whether suggestions, submissions, requests, or whatever! If you think of a nifty feature we ought to have, let us know.

To contribute code to this project:

  1. Fork it!
  2. Create your feature branch: git checkout -b my-new-feature
  3. Check out the style guide
  4. Consider including a test, even if it’s super basic
  5. Commit your changes: git commit -am 'Add some feature'
  6. Push to the branch: git push origin my-new-feature
  7. Submit a pull request!

For anything else, feel free to open an issue!


If you want to statically link Nifty and all its dependencies (e.g. for distribution), follow the steps below… TODO: create guide; for now, here’s a post that may be helpful.


This project is licensed under the Apache License, Version 2.0, a complete copy of which can found in LICENSE, adjacent to this file.