Introduction to Scientific Visualization

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Information about Introduction to Scientific Visualization
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Published on November 21, 2007

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An Introduction to Scientific Visualization:  An Introduction to Scientific Visualization By: Martin Dale Lyness (a.k.a. Frank Linux) Adapted from: Mark Shuo Wang’s “Scientific Visualization” About the Author:  About the Author Research Advisor: David A. Yuen Institution University of Minnesota Twin Cities Minnesota Supercomputing Institute Interests Computer Science Visualization Scientific Computation What is Visualization?:  What is Visualization? Visualization is the process of representing abstract objects as concrete images perceivable by the eyes and brain. How about Scientific Visualization? Scientific Visualization is applying the ability to visualize abstract things to help improve your understanding of arbitrary concepts and phenomenon. Often based off of data sets gathered by various instruments or generated by software simulations. The Goal of Visualization:  The Goal of Visualization Simplification and Interpretation The ultimate goal of Visualization is to allow Scientists to more easily understand and share their data. It is much easier to understand a visual image than a plain ASCII text file containing thousands of data points. Pouring concrete onto abstracts to allow for testing and collaboration. Importance of Visualization:  Importance of Visualization Efficient scientific research is not possible without visualization. Whether this visualization occurs in your own head or it occurs on a computer screen, notepad, or other device; Visualization is vital to understanding scientific problems. With all of our technologies enabling visualization it would be ludicrous not to use it! A World Without Visualization:  A World Without Visualization What would happen if you could not apply imaging to the data that you gathered? Would that data still have value if no one understood it? The inability to visualize abstracts on some level would have made it impossible for almost all of the scientific breakthroughs in our history on this planet. Collaborative Visualization :  Collaborative Visualization Visualization can occur mentally via some source of external data, but that visualization is not guaranteed to be the same as what another researcher may see from the same data. Using technologies like the computer to create images allow for collaborative visualization and ensure everyone is on the same page. This also allows for testing of ones logic during the visualization process. Simple Examples of Visualization:  Simple Examples of Visualization At its very simplest, visualization deals with changing of an abstract into an image At its very simplest, visualization deals with changing of an abstract into an image Television Drawings Thought of Dog Paper + Pencil 3 Parts of Visualization:  3 Parts of Visualization The process of visualization contains three main parts Abstract idea/dataset Tools to create images from abstract Display device to show & share visualization This is seen in the previous examples where: 1.) The thought of a dog, 2.) Pencil and hand eye coordination, 3.) Paper Applying 3 steps to abstract data:  Applying 3 steps to abstract data Say you have the following data table to the right. Unless you collected this data, you wouldn't know what it means. Even if we attach some meta data we are not getting everything we can from this data set. Applying 3 steps to abstract data continued…:  Applying 3 steps to abstract data continued… Knowing what the data is representing we can quickly provide a graph that explains it concisely to a general audience. 1. We started with a set of abstract data 2. We used computer graphing software to create a graph. 3. We displayed this graph on a PowerPoint presentation. Bacteria Growth of (insert bacteria here) under direct sunlight NOTE: This is a fictional data set. Applying Visualization:  Applying Visualization Common Tools for Visualization:  Common Tools for Visualization VTK – Scripting language that allows for customizable visualizations using relatively simple syntax ParaView – A GUI (Graphical User Interface) built on VTK OpenDX – Originally built from the ground up by IBM Amira – Commercially licensed visualization software. The Visualization Toolkit (VTK):  The Visualization Toolkit (VTK) Works on all platforms: Unix/Linux, Windows (95/98/ME/NT/2000/XP), Mac OSX Jaguar > C++ Library has bindings in Tcl/TK, Java, and Python Supports many visualization algorithms like: “scalar, vector, tensor, texture, and volumetric methods; and advanced modeling techniques such as implicit modeling, polygon reduction, mesh smoothing, cutting, contouring, and Delancy triangulation.” (vtk.org) VTK Examples:  VTK Examples Lets look at a simple example of using VTK. http://www.vtk.org/example-code.php Two examples, creating a simple sphere and visualizing the quadratic equation Show example on computer Show use in VLab (Charge Density Vis) ParaView:  ParaView ParaView is built on top of VTK. It aims to provide an intuitive graphical interface for using VTK to catalyze the visualization process. http://www.paraview.org/New/index.html Freely available to download http://www.mathematik.uni-dortmund.de/lsiii/static/showpdffile_Hini2007.pdf OpenDX:  OpenDX http://www.opendx.org Originally Designed by IBM Many features Ugly User Interface Counter Intuitive Amira (Commercial) Tai Gui:  Amira (Commercial) Tai Gui http://www.amiravis.com/ Maintained and updated by Mercury Computer Systems, inc. Excellent product with many features. Supports many file formats, and all common visualization techniques http://www.amiravis.com/overview.html Amira & Tsunami:  Amira & Tsunami Extremely exaggerated water heights. Visualized using Amira PSI Format http://www.amiravis.com/usersguide41/hxcluster/HxFileFormat_PSI.html Data was in the form (space)X(space)Y(space)Z(\n) Simple format converter ‘format.c’ modified the data so it could be loaded and visualized in Amira. This is called “Data Massaging” Eric Sevre’s Tsunami Visualization:  Eric Sevre’s Tsunami Visualization How do these tools visualize data sets?:  How do these tools visualize data sets? As a computer scientist this area is of most interest to me. These tools need to be developed so researchers can use them to quickly and intuitively make quality observations about their data. Various techniques have been developed that can be used to visualize different data for detail exploits. Common Visualization Techniques:  Common Visualization Techniques Isosurfaces – Surfaces are created using a Triangulation algorithm that generates a theoretical hull based on the data points given. Volume Rendering – Displaying 3D data as 2D projection Slices and Layering – Detailed 2D views of 3D data sets. Ray Tracing/Casting – Used in volume rendering Non-Photorealistic – Visualizing data sets as if they where hand draw to accent new details. Isosurfaces:  Isosurfaces Give impression of 3D objects Uses many thousands of triangles to create surfaces that interact with a light source. Famous triangulation method Delauncy http://www.cse.unsw.edu.au/~lambert/java/3d/delaunay.html Isosurface Examples:  Isosurface Examples Slices and Layering:  Slices and Layering Allow you to see a slice of a 3D data set. Plane can be at an arbitrary angle about an arbitrary axis Makes it easy to see inner details of a dataset. “Good for analysis of cross-sectional data such as subduction zone” (Mark) Slice Examples:  Slice Examples Volume Rendering:  Volume Rendering Displaying a 2 Dimensional projection from 3 dimensional data. Most commonly 3D data is in RGBA (red, green, blue, alpha) form. Each data point is a voxel with a RGBA value Different techniques are used to determine the pixel RGB value from all of the voxels that are projected to a particular coordinate. Volume Rendered Examples:  Volume Rendered Examples Ray Casting:  Ray Casting Ray Casting very popular technique used in Volume Rendering. Essentially, you create an imaginary image plane for which a ray is cast through each pixel. Then as the ray travels from the camera to the clipping area (end of volume) at regular intervals RGBA values are accumulatively calculated. http://en.wikipedia.org/wiki/Volume_ray_casting Ray Tracing:  Ray Tracing Similar to Ray Casting, uses the say idea of rays originating form the eye and passing through an object. More advanced algorithm; Computationally more intensive and produces higher quality images. Can accurately portray shadows and reflections unlike ray casting. http://en.wikipedia.org/wiki/Ray_tracing Non-Photorealistic:  Non-Photorealistic Uses techniques from artists in history to extrude important details. Visualizations are not cluttered with unimportant pixels only details are shown in a artistic method. Provides a new look at a dataset and may open up new observations. Limitations to Visualization:  Limitations to Visualization As in all areas of science and research there are currently limitations to our ability to visualize data. Computers have given two main limitations Hardware : Unable to process data fast enough Software : Unable to provide useful and efficient algorithms My Latest Work:  My Latest Work PlayStation 3 http://marina.geo.umn.edu/ps3-wiki VLab (vlab.msi.umn.edu) Charge Density Visualization Client http://lilli.msi.umn.edu:8080/vlabportlets/cdc_servlet Research with Yunhai Wang (Cloud Sea/Nine) on integrating non-photorealistic and realistic visualization together utilizing GPU accelerations. Conclusion:  Conclusion Scientific Visualization is the process of making abstract data concretely represented visually. Three Steps: 1. Create an abstract 2. Use tools to visualize abstract 3. Use a display device to observe and share findings. Without visualization on some level, abstract data could not be qualitatively expressed nor studied. Conclusion Continued…:  Conclusion Continued… Various tools are already available to researchers in need of visualizing their data. Visualization tools can use different techniques in combination or separately to help you make observations about your data. New visualization techniques and optimizations methods through software and hardware are part of the research improving current visualization systems. More Information:  More Information Feel free to contact me with follow up questions/comments at: linux@msi.umn.edu These slides and other information about this talk will be posted to my website http://www.martin-lyness.com/ in the near future

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