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Description

SciPy is an open source, freely distributed and cross-platform library software implemented in and designed for the Python programming language. It aims to offer a set of command-line utilities that can be used for engineering and science tasks in Python applications.

It is part of a huge project that provides several Python libraries heavily used on today’s apps, such as NumPy, a base N-dimensional array library, Sympy, a library for symbolic mathematics, Matplotlib, a comprehensive 2D plotting library, IPython, an enhanced interactive console, the nose framework for testing Python code, as well as the pandas library for data structures and analysis.

SciPy includes modules for statistics, optimization, integration, Fourier transforms, linear algebra, signal and image processing, genetic algorithms, ODE solvers, etc. It also provides numerous efficient and user-friendly numerical routines, such as routines for numerical optimization and integration.

To install and use the SciPy library on your GNU/Linux operating system, you will have to download the latest version of the program from the official homepage of the project or via Softpedia, where it is distributed as a source tarball.

Save the package on your computer, unpack it, open the Terminal app, navigate to the location of the extracted archive files (e.g. cd /home/softpedia/scipy-0.14.0), and run the ‘sudo python setup.py install’ command. All the files will be installed system wide and you can use them on your Python apps. Comprehensive documentation can be found on the project’s website.

SciPy is a platform-independent application written entirely in the Python programming language. It has been successfully tested on a wide range of GNU/Linux distributions, but it is also compatible with the FreeBSD operating system. Being written in Python, it might be easily ported to the Microsoft Windows and Mac OS X operating systems.

User Reviews for SciPy FOR LINUX 1

  • for SciPy FOR LINUX
    SciPy FOR LINUX provides a comprehensive set of tools for engineering and science tasks in Python. Installation process is clear and it offers efficient numerical routines.
    Reviewer profile placeholder Hannah Thompson
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