When working with scientific computer models, the need to generate graphical representations of the results is ubiquitous. This page offers some good references to free open source tools commonly used in the scientific community.
Open source visualization tools can be found at:
Visualization tools can also be found at:
In what follow we list many specific open source tools to visualize data and do data conversion between different formats:
Octave. Uses gnuplot as the actual plotting engine, so output quality is the same as gnuplot. Having a programming environment makes it useful for combined preprocessing and plotting.
lots of plugins especially with regard to GUI elements
recommendable ( ->nad ) library of mostly graph visualization tools
processingjs (uses only canvas)
The Visualization toolkit - VtK. open-source, freely available software system for 3D computer graphics, image processing? and visualization. VTK consists of a C++ class library and several interpreted interface layers including Tcl/Tk, Java, and Python.
Graphviz is open source graph visualization software. Graph visualization is a way of representing structural information as diagrams of abstract graphs and networks. It has important applications in networking, bioinformatics, software engineering, database and web design, machine learning, and in visual interfaces for other technical domains.
ParaView is an open-source, multi-platform data analysis and visualization application. ParaView users can quickly build visualizations to analyze their data using qualitative and quantitative techniques. The data exploration can be done interactively in 3D or programmatically using ParaView’s batch processing capabilities. ParaView was developed to analyze extremely large datasets using distributed memory computing resources. It can be run on supercomputers to analyze datasets of terascale as well as on laptops for smaller data.
VisIt is a free interactive parallel visualization and graphical analysis tool for viewing scientific data on Unix and PC platforms. Users can quickly generate visualizations from their data, animate them through time, manipulate them, and save the resulting images for presentations. VisIt contains a rich set of visualization features so that you can view your data in a variety of ways. It can be used to visualize scalar and vector fields defined on two- and three-dimensional (2D and 3D) structured and unstructured meshes. VisIt was designed to handle very large data set sizes in the terascale range and yet can also handle small data sets in the kilobyte range.
Isaac Held recommends
IrfanView. Free but not open source. Good set of feature for simple image processing and manipulation. Its screen capture mode is also straight-forward to use.
ImageMagick is a batch tool for many images
Inkscape which is IMHO (->nad:also in my IMHO) the best open source vector drawing package. Why because beside its easy user interface it has a mountain of SVG libraries, like OpenClipart.org and Wikimedia also has a lot of SVG-files under CC-licence, see it categorized. There are 56 images in ecology and over 250 in geology! Openclpipart also has many good initiatives for increasing the amount of clips like remixing.
Scalable Vector Graphics can be created directly on any Azimuth Project wiki page by clicking the “Create SVG graphic” button on the edit page. This opens a separate browser window (on Firefox) and loads svg-edit, an open-source online alternative to Inkscape. Create the graphic using svg-edit, and then select “Main Menu > Save Image”. Optionally select “Main Menu > Document Properties > Select predefined > Fit to Content” before saving. The SVG content will be saved as text to the wiki page. With an-to-date browser it should appear on the page as a graphic. Internet Explorer has started supporting SVG with version 9. Example:
WireIt. Software that may be more a visual user interface library rather than for end-product visualization, but is included here for completeness.
Hadley Wickham, ggplot: Elegant Graphics for Data Analysis, Springer, Berlin, 2009.
Paul Murell, R graphics, 2006.
Georges-Pierre Bonneau, Thomas Ertl and Gregory M. Nielson, Scientific Visualization: the Visual Extraction of Knowledge From Data.