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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.

scientific scientific-computing mathematicsxarray (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.

scientific-computing netcdf numpy data-science pandas dataframes data-analysis pydataSpack 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.

spack package-manager hpc scientific-computing cray supercomputer r govJulia 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.

julia julia-language programming-language scientific-computing high-performance-computing numerical-computation machine-learningThe 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.

boinc distributed-computing volunteer-computing high-performance-computing citizen-science scientific-computing grid-computing science high-throughput-computing c-plus-plusGosl 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.

scientific-computing visualization linear-algebra differential-equations sparse-systems plotting mkl parallel-computations computational-geometry graph-theory tensor-algebra fast-fourier-transform eigenvalues eigenvectors hacktoberfest machine-learning artificial-intelligence optimization optimization-algorithms linear-programmingThe 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.

scientific-computing data-analysis matrix statistics graphNumPy 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.

scientific scientific-computing mathematics n-arrayThe 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.

numerics scientific-computing rust-sciOwl 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.

matrix linear-algebra ndarray statistical-functions topic-modeling regression maths gsl plotting sparse-linear-systems scientific-computing numerical-calculations statistics mcmc optimization autograd algorithmic-differentation automatic-differentiation machine-learning neural-networkThe 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.

julia learning-julia language learning learning-by-doing julia-language julialang data-science statistics technical-computing hpc scientific-computingReflow is a system for incremental data processing in the cloud. Reflow enables scientists and engineers to compose existing tools (packaged in Docker images) using ordinary programming constructs. Reflow then evaluates these programs in a cloud environment, transparently parallelizing work and memoizing results. Reflow was created at GRAIL to manage our NGS (next generation sequencing) bioinformatics workloads on AWS, but has also been used for many other applications, including model training and ad-hoc data analyses. Reflow thus allows scientists and engineers to write straightforward programs and then have them transparently executed in a cloud environment. Programs are automatically parallelized and distributed across multiple machines, and redundant computations (even across runs and users) are eliminated by its memoization cache. Reflow evaluates its programs incrementally: whenever the input data or program changes, only those outputs that depend on the changed data or code are recomputed.

bioinformatics-pipeline scientific-computing language runtime data-science cloud-computing aws analysis-pipelineVexCL 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.

opencl cuda c-plus-plus gpgpu scientific-computing cpp11The 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.

itk insight-toolkit c-plus-plus image-analysis medical-imaging scientific-computing open-science open-source reproducible-researchStdlib 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.

stdlib scientific-computing numerical-computing statistics mathsArmadillo: fast C++ library for linear algebra & scientific computing - http://arma.sourceforge.net

linear-algebra matrix matrix-functions linear-algebra-library statistics matlab blas lapack hpc scientific-computing mkl machine-learning armadillo openmp gaussian-mixture-models cpp11 vector sparse-matrix expression-template matrix-factorizationArray 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.

tiledb arrays storage-engine scientific-computing data-analysis hdfs s3 s3-storageA Dynamic Scientific Library For all The Common Intermediate Languages and Scientific Data Extraction

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

debug toolbox computer-vision scientific-computing awesome-python debugging hack pytorchGPU accelerated micromagnetic simulator. Contributions are gratefully accepted. To contribute code, fork our repo on github and send a pull request.

scientific-computing micromagnetics cuda finite-difference-time-domain
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