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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.

SciPy provides modules for Statistics, Optimization, Numerical integration, Linear algebra, Fourier transforms, Signal processing, Image processing, ODE solvers.

Sourceforge location: http://sourceforge.net/projects/scipy/

Tags | scientific scientific-computing mathematics |

Implementation | Python |

License | BSD |

Platform | Windows Linux MacOS Solaris |

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.

scientific scientific-computing mathematics n-arraySciRust 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 mathsND4J 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-arrayAn implementation of the KPCA plus LDA algorithm of Yang et al, 2005 in Python with the numpy and scipy libraries for scientific computing

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Quick tutorial of scientific computing with Python using a real physics application.