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SCaVis is an environment for scientific computation, data analysis and data visualization designed for scientists, engineers and students. The program incorporates many open-source software packages into a coherent interface using the concept of dynamic scripting

SCaVis can be used everywhere where an analysis of large numerical data volumes, data mining, statistical analysis and mathematics are essential (natural sciences, engineering, modeling and analysis of financial markets).
SCaVis is fully multiplatform and runs on any platform where Java is installed. As a Java application, SCaVis takes the full advantage of multicore processors.

SCaVis can be used with several scripting languages for the Java platform, such as Jython (the Python programming language) and BeanShell. This brings more power and simplicity for scientific computation. The programming can also be done in native Java. Finally, symbolic calculations can be done using Matlab/Octave high-level interpreted language.

SCaVis is a successor of the popular jHepWork package which has been under intensive development since 2005. It is fully backward compatible with jHepWork 3.9. Any code which runs using jHepWork should also work using SCaVis.

Features

Java libraries for 2D/3D graphics and scientific data visualization. See SCaVis examples. The main programming language in all these examples is Jython, an implementation of the high-level language Python for the Java platform. Examples are also available for JRuby (the Ruby programming language) and BeanShell.
Numerical libraries for data structures and data manipulation. The libraries include numerical and analytical calculations, linear algebra operations, equation solving algorithms. Many libraries are based on the JAIDA classes for data manipulation, construction of histograms and functions. SCaVis includes high-level data types for manipulations, calculations of systematical errors and their visualization which have no analogy in other data-analysis software. Programs written using the SCaVis framework are very short, have remarkable power with very clear syntax due to high-level constructions of the jHPlot package.
It can run in either a GUI-driven mode or a batch mode.
Analytic (symbolic) computations. In this mode, Matlab/Octave high-level interpreted language can be used.
Comprehensive statistical packages. More than 10 thousand JAVA classes and methods are packed in 50 MB library pack (this excludes those from the native Java API). For example, the core package jHPlot (the main numerical package) has about 2000 classes and methods. The rest are third-party JAVA or Python libraries. One can perform any statistical data analysis, linear or non-linear fits, cluster analysis, generate random numbers, construct neural networks etc.
Comprehensive API for data input and output (I/O). Being Java, it includes the native Java I/O from the java.io package. Being Python, it includes the native Python I/O methods and classes. In addition, the native SCaVis I/O contains Java classes for persistent data storage in ZIP-format, Java-serialisation format and XML-formats. SCaVis contains packages for several Java-native SQL databases (including SQLite) and object-based databases. It complies with the ROOT and AIDA data formats. Finally, it uses several platform-neutral file formats, including the compressed Google's Protocol Buffers format.
Professional output graphics for data and function visualization. All plots can be exported to raster formats (PDF, PNG, JPG, etc.) and high-quality vector formats (PostScript, EPS) ready to be included into presentations and scientific papers (including the LaTeX format).
Enhanced Jython shell and BeanShell consoles with syntax highlighting, command line completion and built-in macro language for custom commands. One can easily script your data-analysis or scientific algorithm in a completely interactive way in a few seconds.
Powerful IDE with a code assist which is especially design