SciPy - software for Mathematics, Science, and Engineering

•    Python

SciPy (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering. The SciPy library is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.

xarray - N-D labeled arrays and datasets in Python

•    Python

xarray (formerly xray) is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the Common Data Model for self- describing scientific data in widespread use in the Earth sciences: xarray.Dataset is an in-memory representation of a netCDF file.

spack - A flexible package manager that supports multiple versions, configurations, platforms, and compilers

•    Python

Spack is a multi-platform package manager that builds and installs multiple versions and configurations of software. It works on Linux, macOS, and many supercomputers. Spack is non-destructive: installing a new version of a package does not break existing installations, so many configurations of the same package can coexist. Spack offers a simple "spec" syntax that allows users to specify versions and configuration options. Package files are written in pure Python, and specs allow package authors to write a single script for many different builds of the same package. With Spack, you can build your software all the ways you want to.

julia - The Julia Language: A fresh approach to technical computing.

•    Julia

Julia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source code, including instructions for compiling and installing Julia, below. New developers may find the notes in CONTRIBUTING helpful to start contributing to the Julia codebase.

boinc - Open-source software for volunteer computing and grid computing.

•    PHP

The University of California holds the copyright on all BOINC source code. By submitting contributions to the BOINC code, you irrevocably assign all right, title, and interest, including copyright and all copyright rights, in such contributions to The Regents of the University of California, who may then use the code for any purpose that it desires. BOINC is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

gosl - Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more

•    Go

Gosl is a Go library to develop Artificial Intelligence and High-Performance Scientific Computations. The library tries to be as general and easy as possible. Gosl considers the use of both Go concurrency routines and parallel computing using the message passing interface (MPI). Gosl has several modules (sub-packages) for a variety of tasks in scientific computing, image analysis, and data post-processing.

gonum - Gonum is a set of numeric libraries for the Go programming language

•    Go

The core packages of the gonum suite are written in pure Go with some assembly. Installation is done using go get.The gonum packages use a variety of build tags to set non-standard build conditions. Building gonum applications will work without knowing how to use these tags, but they can be used during testing and to control the use of assembly and CGO code.

NumPy - fundamental package for scientific computing with Python

•    Python

NumPy is the fundamental package needed for scientific computing with Python. Numerical Python adds a fast and sophisticated N-dimensional array facility to the Python language. NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

ndarray - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations

•    Rust

The ndarray crate provides an n-dimensional container for general elements and for numerics. The following crate feature flags are available. They are configured in your Cargo.toml.

owl - Owl is an OCaml library for scientific and engineering computing.

•    OCaml

Owl is an emerging numerical library for scientific computing and engineering. The library is developed in the OCaml language and inherits all its powerful features such as static type checking, powerful module system, and superior runtime efficiency. Owl allows you to write succinct type-safe numerical applications in functional language without sacrificing performance, significantly reduces the cost from prototype to production use. Owl's documentation contains a lot of learning materials to help you start. The full documentation consists of two parts: Tutorial Book and API Reference. Both are perfectly synchronised with the code in the repository by the automatic building system. You can access both parts with the following link.

learn-julia-the-hard-way - Learn Julia the hard way!

•    Makefile

The Julia base package is pretty big, although at the same time, there are lots of other packages around to expand it with. The result is that on the whole, it is impossible to give a thorough overview of all that Julia can do in just a few brief exercises. Therefore, I had to adopt a little 'bias', or 'slant' if you please, in deciding what to focus on and what to ignore. Julia is a technical computing language, although it does have the capabilities of any general purpose language and you'd be hard-pressed to find tasks it's completely unsuitable for (although that does not mean it's the best or easiest choice for any of them). Julia was developed with the occasional reference to R, and with an avowed intent to improve upon R's clunkiness. R is a great language, but relatively slow, to the point that most people use it to rapid prototype, then implement the algorithm for production in Python or Java. Julia seeks to be as approachable as R but without the speed penalty.

vexcl - VexCL is a C++ vector expression template library for OpenCL/CUDA

•    C++

VexCL is a vector expression template library for OpenCL/CUDA. It has been created for ease of GPGPU development with C++. VexCL strives to reduce amount of boilerplate code needed to develop GPGPU applications. The library provides convenient and intuitive notation for vector arithmetic, reduction, sparse matrix-vector products, etc. Multi-device and even multi-platform computations are supported. The source code of the library is distributed under very permissive MIT license.

ITK - Insight Segmentation and Registration Toolkit -- Mirror

•    C++

The National Library of Medicine Insight Segmentation and Registration Toolkit (ITK), or Insight Toolkit, is an open-source, cross-platform C++ toolkit for segmentation and registration. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. The toolkit may be built from source using CMake.

Stdlib - Standard library for JavaScript and Node.js.

•    Javascript

Stdlib is a standard library for JavaScript and Node.js, with an emphasis on numeric computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

armadillo-code - Armadillo: fast C++ library for linear algebra & scientific computing - http://arma

Armadillo: fast C++ library for linear algebra & scientific computing - http://arma.sourceforge.net

TileDB - TileDB array data management

•    C++

Array data management made fast and easy. TileDB allows you to manage the massive dense and sparse multi-dimensional array data that frequently arise in many important scientific applications.

The C.I.D.S.L

•    CSharp

A Dynamic Scientific Library For all The Common Intermediate Languages and Scientific Data Extraction

boxx - Tool-box for efficient build and debug in Python

•    Python

Box-X is a Tool-box for Efficient Build and Debug in Python. Especially for Scientific Computing and Computer Vision.

sparse - Sparse matrix formats for linear algebra supporting scientific and machine learning applications

•    Go

Implementations of selected sparse matrix formats for linear algebra supporting scientific and machine learning applications.Machine learning applications typically model entities as vectors of numerical features so that they may be compared and analysed quantitively. Typically the majority of the elements in these vectors are zeros. In the case of text mining applications, each document within a corpus is represented as a vector and its features represent the vocabulary of unique words. A corpus of several thousand documents might utilise a vocabulary of hundreds of thousands (or perhaps even millions) of unique words but each document will typically only contain a couple of hundred unique words. This means the number of non-zero values in the matrix might only be around 1%.

boxtree - Quad/octree building for FMMs in Python and OpenCL

•    Python

boxtree is a package that, given some point locations in two or three dimensions, sorts them into an adaptive quad/octree of boxes, efficiently, in parallel, using PyOpenCL. It can also generate traversal lists needed for adaptive fast multipole methods and related algorithms and tree-based look-up tables for geometric proximity.