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This two-day training course will introduce students to data visualization with a primary focus on visualization of large volumetric data resulting from simulation and instrumentation. We begin with an overview of visualization as a whole, beginning with a simple taxonomy: illustration, which uses techniques of computer graphics and animation to convey concepts, and data-driven visualization which interpret data as imagery to convey the content of the data Data-driven visualization is further divided into two broad areas. Information visualization is concerned with visualizing discrete data such as is often found in Excel spreadsheets and relational databases – think a sales database, including a record for many customer transactions including date, product list and so forth. Scientific data is concerned with continuous data defined over a domain – think weather data, representing variables like temperature and pressure in a three-dimensional region of the atmosphere. This section will include copious examples of each class of visualization.
The next section provides a brief introduction to scientific illustration, in which a specialist will provide a hands on demonstration of an interactive tool for modeling, animating and rendering visualizations. Either Blender or Maya will be demonstrated. This section is intended to give students a broad overview of the capabilities of these systems and to enable them to identify situations in which these are the appropriate tools.
Following will be an introduction to information visualization. We begin with an brief history of info viz, then discuss data and techniques and an overview of the software tools available. Following this will be a demonstration of a commercial end-user application (Tableau).
We then segue to scientific visualization, the focus of the course. In an introductory section we will present an overview of scientific data. We begin with a conceptual approach to scientific data as functions over a time/space domain, then discuss how this data is represented as time/space grids with variables associated with grid points and cells. We discuss how visualization systems transform such data to imagery. We then present hands-on demonstrations of open-source general-purpose tools for scientific visualization – ParaView and VisIt. Following demonstrations of the interactive interface to each of these, we will discuss techniques in which scripting can be used to generate visualizations off-line. We will then discuss issues that arise when datasets become very large, discussing parallel execution of the tools, and data layout for optimal I/O. This will include a presentation of the facilities we maintain at the Texas Advanced Computing Center for high-performance visualization of very large datasets and how we provide these services to remote users.