MathGL is a library for making high-quality scientific graphics under Linux and Windows, fast data plotting and handling of large data arrays, working in window and console modes, and easily embedding into other programs. It has more than 40 general types of graphics for 1d, 2d, and 3d data arrays. It can export graphics to raster and vector (EPS or SVG) formats. It has an OpenGL interface and can be used from console programs. It has functions for data handling and MGL language scripting for simplification of data plotting. It has several types of transparency and smoothed lightning, vector fonts and TeX-like formula drawing, an arbitrary curvilinear coordinate system, and many other useful things.
GAMGI (General Atomistic Modelling Graphic Interface) is a program to build, view, and analyze atomic strucures such as molecules, crystals, glasses, liquids, etc. It aims to be useful for: the scientific community working in Atomistic Modelling that needs a graphic interface to build input data and to view and analyse output data, calculated with Ab-Initio and Molecular Mechanics programs; the scientific community at large studying chemistry, physics, materials science, geology, etc., that needs a graphic interface to view and analyse atomic structural information and to prepare images for presentations in classes and seminars; teaching chemistry and physics in secondary schools and universities; science promotion in schools, exhibitions and science museums.
CCruncher is a project for quantifying portfolio credit risk using the copula approach. It is a framework consisting of two elements: a technical document that explains the theory, and a software program that implements it. CCruncher evaluates the portfolio credit risk by sampling the portfolio loss distribution and computing the Expected Loss (EL), Value at Risk (VaR), and Expected Shortfall (ES) statistics. The portfolio losses are obtained simulating the default times of obligors and simulating the EADs and LGDs of their assets.
The Java Algebra System (JAS) is an object oriented, type safe, multi-threaded approach to computer algebra. JAS provides a well designed software library using generic types for algebraic computations implemented in the Java programming language. The library can be used as any other Java software package, or it can be used interactively or interpreted through a Jython or JRuby front end. The focus at the moment is on commutative and solvable polynomials, power-series, multivariate polynomial factorization, Gröbner bases, and applications.
Gwyddion is a modular SPM (Scanning Probe Microsope) data visualization and analysis tool. It can be used for all most frequently used data processing operations including: leveling, false color plotting, shading, filtering, denoising, data editing, integral transforms, grain analysis, profile extraction, fractal analysis, and many more. The program is primarily focused on SPM data analysis (e.g. data obtained from AFM, STM, NSOM, and similar microscopes). However, it can also be used for analyzing SEM (scaning electron microscopy) data or any other 2D data.
Lynkeos processes astronomical webcam images. By stacking the best images, the signal to noise ratio is increased, and details lost in the noise of individual images become visible in the resulting image. This software accepts, as input, QuickTime, AVI, and MPEG sequences, or still images, in any image format supported by its active plugins (TIFF, FITS) and Cocoa. It generates a 16-bit RGB TIFF or a monochrome FITS image as output, to be further processed with some all-purpose image processing application.
Social Networks Visualizer (SocNetV) is a flexible and user-friendly tool for the analysis and visualization of Social Networks. It lets you construct mathematical graphs with a few clicks on a virtual canvas, load networks of various formats (GraphViz, GraphML, Adjacency, Pajek, UCINET, etc), or create a network by crawling all links in a Web page. The application can compute basic network properties, such as density, diameter, and distances (shortest path lengths), as well as more advanced structural statistics, such as node and network centralities (i.e. closeness, betweenness, graph), clustering coefficient, etc.