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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.

http://numpy.scipy.org/Tags | scientific scientific-computing mathematics n-array |

Implementation | Python |

License | BSD |

Platform | Windows Linux |

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 mathematicsND4J is an open-sourced scientific computing library for the JVM. Its features include Versatile n-dimensional array object, Multiplatform functionality including GPUs Linear algebra and signal processing functions and lot more.

scientific scalar vector multi-dimensional-arrayScientific and statistical computing in JavaScript.

science statistics mathematicsSciRust is a 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. Its current areas of focus includes Matrices, Linear algebra, Statistics, and Signal processing.

scientific computing algebra matrixSpyder is a Python development environment with advanced editing, interactive testing, debugging and introspection features. It is especially recommended for scientific computing thanks to NumPy (linear algebra), SciPy (signal and image processing), matplotlib (interactive 2D/3D plotting) and MayaVi’s mlab (interactive 3D visualization) support.

ide integrated-development-environment text-editor python-ide scientificThe system is built on Ubuntu 14.04 Server, with a handful of extra Python 3 libraries installed for scientific computing. An iPython Notebook server is set up so that students can present their findings in a human-readable format, with interactive code and graphics.* [Numpy](https://github.com/numpy/numpy)* [SciPy](https://github.com/scipy/scipy)* [Matplotlib](https://github.com/matplotlib/matplotlib)* [Pandas](https://github.com/pydata/pandas)* [iPython](https://github.com/ipython/ipython)Vis

Areas of experience include scientific computing, visualization, and simulations. Looking to expand my knowledge in the fields of scientific computing and visualization. I am interested in systems that enable analysis, archiving, and dissemination of scientific data across broad scientific fields. Currently focused on developing visualization systems using Node.js, AngularJS, and D3.js. I'm passionate about programming, programming languages, and open-source.

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.

stdlib scientific-computing numerical-computing statistics mathsFreeMat is a free environment for rapid engineering and scientific prototyping and data processing. It is similar to commercial systems such as MATLAB and IDL. It has built in arithmetic for manipulation of all supported data types, N-dimensional array manipulation, 2D and 3D plotting and image display, Visualization, Image manipulation, and as well as parallel programming.

matlab matlab-altenative maths tool engineering scientificA Dynamic Scientific Library For all The Common Intermediate Languages and Scientific Data Extraction

scientific-computingImplementations 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%.

matrix scientific-computing machine-learning sparse-matrices matrices csr coo csc dictionary-of-keysThe main goal of these examples is to showcase linalg API. A secondary goal is to compare the ergonomics of doing numerical computing in Rust vs in other languages, to this extend implementations of each example are provided in other languages. And, as this library currently lacks (multi-language) benchmarks, some non-scientific measurements are included (I mainly wanted to check that linalg is not slower than NumPy).

An implementation of the KPCA plus LDA algorithm of Yang et al, 2005 in Python with the numpy and scipy libraries for scientific computing

Generic utilities (used in cluster computing, scientific calculations, etc.)

Implementations for the Scientific Computing lecture at Bielefeld University.

An open-source framework pour scientific computing

a library of scientific computing building blocks in Java

Collection of go programs for my scientific computing class