Introduction to Scientific Visualization

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Introduction to Scientific Visualization
Materials from Ken Flurchick Ohio
Supercomputing Center Todd Veltman West Lyden
High School Alan Shih, Dave Bock, Alan Craig,
Polly Baker, Scott Lathrop, Lisa Bievenue, plus
all the researchers who provided
examples National Center for Supercomputing
ApplicationsUniversity of Illinois at
Urbana-Champaign
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Agenda

• What is Visualization?
• Why Do Visualization?
• Why is Visualization Important?
• The Visualization Process
• Doing Visualization
• Data Sources
• Data Variables
• Representation Types
• Visualization Techniques
• Interactive or Batch?
• Data Types and Topologies

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What is Scientific Visualization?

• 1987 NSF Panel Initiative - Formal Definition
• "Visualization is a method of computing. It
  transforms the symbolic into the geometric,
  enabling researchers to observe their simulations
  and computations. Visualization offers a method
  for seeing the unseen. It enriches the process
  of scientific discovery and fosters profound and
  unexpected insights.

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Early Representation

• The Cave of Lascaux, France

• 15,000 years old
• Tells a story
• NOT visualization

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Quantitative Representation
Planetary Orbits

• Tenth century
• Inclinations of the planetary orbits as a
  function of time.
• Oldest known attempt to show changing values
  graphically.

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Scientific Illustration
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Computer Art and Scientific Visualization
Cox, Donna Patterson, Robert Bargar, Robin
Daab, Fred Moore, Michael Moorman, Jan
Waegner, Chris Erickson, Christian Swing,
Chris Conrad, Renee Knocke, Joel Jordan,
Robert Brandys, Mike Fossum, Barbara Colby,
Don McNeil, Mike Bajuk, Mark Arrott, Matthew
Swanson, Amy
Researchers Cerco, Carl Noel, Mark
CEWES Visualizaiton Stein, Robert Shih,
Alan NCSA
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Visualization is a Form of Data Representation

• Choice of appropriate representation

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Why Do Scientific Visualization?

• ?

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Why Do Scientific Visualization?

• Visualization is the practice of mapping data to
  visual form
• for exploration and analysis
• for presentation
• Goal of visualization
• Leverage existing scientific methods by providing
  new scientific insight through visual methods.

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Qualitative vs. Quantitative
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A Basic Example
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Why is Visualization Important?

• ?

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Why is Visualization Important?

• Computers brought about the ability to collect,
  create, and store more information
• rise of computational science in mid-80s
  generated a firehose of data and the subsequent
  need for visualization
• As a process of simulating a relevant subset of
  the laws of nature through a set of equations
• Yields a set of numeric solutions --
• Numbers, LOTS of them
• May not be able to see, much less interpret, all
  of the results.

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Visualization Necessary for Complex Systems
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Visualization of Data

• Try to envision the domain in your mind

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Visualization of Data

• But, with some modifications to the images...

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Visualization of Data

• Interpolated vs. Non-interpolated

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Doing Visualization

• Make Decisions Related to
• Data Sources
• Data Variables
• Representation Types
• Visualization Techniques
• Interactive or Batch?
• Data Types and Topologies

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Data Sources

• Observation
• wind tunnels, field observations, telescopes,
  space probes, water quality
• Simulation
• computational chemistry, fluid dynamics
• Databases
• protein data bank, genome studies

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Data Variables

• Scalar
• temperature, pressure, velocity
• Vector
• magnetic field, speed
• Tensor
• stress, strain
• Multivariate
• weather characteristics, water quality factors

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Data Representation Types

• Scalar
• volume
• isocontour
• height field
• scatter plot
• image
• contour plot
• strip chart

• Vector
• ribbon
• particle traces
• arrow plot

• Tensor
• disk and shaft ellipsoid

• Multivariate
• various glyph shapes

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What are some Visualization Techniques?

• ?

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Visualization Techniques

• 2D and 3D Plot/Graphs
• Tables and Stacked Plots, Scatter plots
• Contour Lines/Isosurfaces
• Color Shading
• Glyphs (Geometric Shapes)
• Vector Fields
• Arrows, Streamlines, Particle Tracing
• Adding Textures
• Volume Visualization
• Animation
• Data Sonification
• Virtual Reality

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Contours with Layers of Information
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Composite Representation
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Color Shading

• Any graphics primitive (pixel, line, glyph,
  polygon...) can be assigned a color.
• Adding color shading to represent a variable is a
  useful method of illustration. It is equivalent
  to adding an extra dimension to the
  visualization.
• Requires appropriate choice of color map.

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Representation Techniques

• False Color
• Height/Deformation

Researchers Kovacic, David A., Romme,
William H., Despain, Don G. Visualization
Craig, Alan NCSA, 1990
Researchers and visualization Haber, Bob Lee,
Hae-Sung Koh, Hyun NCSA, 1989
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Adding a Color Map

• In the example, color values mapped to data
  values using linear interpolation.

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Using Palettes to Emphasis Aspects of Data
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Glyphs (geometric shapes)

• A glyph is a simple shape used to represent a
  position in space.
• Color and size of the glyph represent the data at
  that point.
• The shapes can be spheres, cubes, tetrahedrons,
  arrows, boxes, ...

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Glyph Example (Coal Combustion)

• Spheres are used for glyphs.
• Each glyph is a coal particle.
• Size represents particle mass.
• Color represents temperature.

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Vector Fields - Arrows

• Vector arrows are used to indicate both direction
  and magnitude at points in a vector field (e.g.,
  velocity, magnetic fields)
• Velocity Field for Flow Over a Blunt Fin example.
• When used with a color map, up to three pieces of
  information can be represented by a vector arrow.

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Vector Fields - Streamlines

• Normally associated with velocity fields
• Shows time-history of a massless particle
• Sometimes depicted as a twisted ribbon to show
  effect of rotation about streamline axis.
• Can also show movement of a glyph along the
  computed streamline.

• More work, computational resources required to
  compute motion in a time-varying velocity
  field.

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Representation Techniques

• Particulate/ Trace
• Iso-surfaces

Researchers Wilhelmson, Robert Brooks,
Harold Jewett, Brian Shaw, Crystal Wicker,
Louis Department of Atmospheric Science and
NCSA Visualization Arrott, Matthew Bajuk,
Mark Thingvold, Jeffrey Yost, Jeffery Bushell,
Colleen Brady, Dan Patterson, Bob
Produced by the Visualization Services and
Development Group, NCSA
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Textures

• In addition to surface height, color and vectors
  one can use texture (bump mapping).
• Bump map is a collection of bumps (texture) used
  to add additional information to a graphical
  primitive.
• Interactive adjustment of parameters is desirable
  to obtain best results.
• Careful use is needed as additions to an already
  rough surface can be distracting.

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Texture Maps

• Texture Mapping

Visualization Stein, Robert, Baker, Polly, NCSA,
ongoing Sponsored by ARL
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Contour Surface Volume Visualization
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Volume Rendering

• Ray-Tracing is a common method for rendering
  large volumes.
• Different from surface rendering techniques.
• Tends to give more photo-realistic results.
• Also takes more computational resources.

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Animation

• Time-varying phenomena are best visualized using
  animations.
• Can slow down events that happen too quickly for
  human perception.
• Animation is also useful for showing different
  perspectives of a static object (e.g., "fly-by").
• Animations can be viewed on screen or can be
  recorded to video tape.
• Often need to save a large number of individual
  frames (images), then play them back quickly.

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Animation

• Damage Structure

Researcher Namburu, Raju,
CEWES Visualization Boch, David Heiland,
Randy Baker, Polly NCSA Stephens, Mike
CEWES
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Static vs. Time-Varying Data

• Static
• At an particular instance of time
• Particular Point of View, etc.
• Time-Varying Animation
• Evolving along the time line
• Dynamic Data or Point of View

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More Animations

• Vector Animation
• Volume Animation
• Volume Animation (Layered Isosurfaces)
• Volume Animation (Rotation)

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Interactive or Batch?

• Interactive Visualization
• Allows the Ability to Control in Real-Time
• Limits the Amount of Data to Be Visualized.
• Useful for Analysis and Exploration
• Batch Visualization
• High-Quality, Complex Representation
• No Control in Real Time.
• Useful for Presentation, Communication, high
  complexity

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Data Types

• Topology
• structure, connectivity
• Geometry
• shape
• Variables
• temperature, pressure, velocity
• Metadata
• information about data, e.g., initial conditions,
  data of observation

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Data Topologies

• Data can be
• structured (e.g., gridded data)
• unstructured (e.g., finite element data)
• a combination of both.
• Data can have different dimensions, both spatial
  and computational.

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Data Topologies

• Most data representing physical phenomena are
  sampled at discrete points on a grid or mesh.
• Data on grids that can be mapped into one or more
  rectangles/boxes is called structured data.

• Structured meshes can be uniform, rectilinear, or
  irregular.
• Grid point, i,j , is identified by the
  intersection of grid line, i, and grid line, j.

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Unstructured Data
Unstructured data is normally required where
geometry's are too complex for structured data.
It is a more general data description.

• Nodal Connectivity
• iconn(1,1)1
• iconn(1,2)2
• iconn(1,3)4
• iconn(2,1)2
• iconn(2,2)3
• iconn(2,3)4
• iconn(3,1)3
• iconn(3,2)6
• iconn(3,3)5
• iconn(3,4)4

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Beyond Visual - Perceptualization

• Virtual Reality Environment
• ImmersaDesk
• Cave
• Fully immersive sphere
• Haptic Devices
• Senses of hearing and smelling

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Challenging Issues in SciVis

• Visualization of Large Data Sets
• How to deal with terabytes of data?
• Remote Visualization
• What is the best way to visualize large data sets
  on remote computer system?
• Interactive Computation
• How to monitor and steer ongoing simulations?
• Representation Techniques
• How to represent the data that shows more
  information and shows it more clearly and
  accurately?
• Immersive Technologies

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What are the Challenges for Education?

• ?

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Summary

• The advent of computer capacity and power push
  the envelope of computational sciences and
  scientific visualization (SciVis)
• SciVis has revolutionized the way we do sciences
• SciVis provides scientists a process to probe
  into enormously large data sets, perceive
  incredible details of the domain, and discover
  unexpected insights.
• Challenging issues in SciVis evolve, but we will
  continue to face them, solve the problems, and
  face future challenges.

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More Information

• http//foxtrot.ncsa.uiuc.edu8900/public/VISTUT/
• Login as guest with password of student
• Scott Lathrop scott_at_ncsa.uiuc.edu
• Lisa Bievenue bievenue_at_ncsa.uiuc.edu