SwiftVis Wiki


Welcome to SwiftVis

This is the beginning of a Wiki reference for SwiftVis. If you want to get access to edit or contribute information simply contact Mark Lewis.

The main SwiftVis page is currently located at http://www.cs.trinity.edu/~mlewis/SwiftVis/. We are moving material from there to this site. This Wiki will have the most recent documentation and we will stop updates on the old site with the exception of links to JAR files.

To get SwiftVis, download the latest JAR file at http://www.cs.trinity.edu/~mlewis/SwiftVis/SwiftVis.jar.

I have also posted SwiftVis on Google Code.

General Information

SwiftVis is a data analysis and visualization package that was originally developed for working with the files produced by the SWIFT planetary simulation package. However, the framework has proven to be extremely flexible and it has been extended so that it can work with many other types of data. It is best suited for doing analysis and visualization of N-body simulations, but it has the ability to work with just about any type of data. At its heart, SwiftVis is basically a visual data-flow programming environment that is easy to use for anyone with some capabilities in math, even if they don't know how to program. In fact, one of the design goals was to make it so that people could use SwiftVis without having a detailed knowledge of programming.

The main window of SwiftVis displays a graph that shows the elements data moves through. The graph is made of three main components: Sources, Filters, and Sinks. The design of SwiftVis makes it highly extensible so that it is easy to add new types of these elements that might allow it to read new types of data files, process data in different ways, or plot data differently.

For help getting started in SwiftVis, see the Tutorials.


Version 0.3.0 has been posted. This release includes a significant change, hence the advancement to another secondary version number. This advance is the introduction of Streams. Streams serve two main purposes in SwiftVis. First, they allow filters like the Linear Fit Filter to output more than one set of data by separating the data into different streams. Second, in situations where you want to perform identical processing to several different sources of data, the different sources can be merged into a single filter that can be worked on without duplicating all the processing filters.

Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License