Introduction to Scientific Visualization and OpenDX

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Introduction to Scientific Visualization and
OpenDX

• Instructor Jon Johansson
• Time Saturday Sept 21, 2002
• 100 400 pm
• Place Faculty of Computer Science

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Goals

• Understand digital representation of color
• Relate colormaps to data
• Get data into OpenDX
• Understand the process of creating a
  visualization in general
• Given a visualization of your data, create a movie

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Outline

• What is Visualization?
• Why Visualize?
• Basic principles
• Look at 2D and 3D data sets
• Fields (scalar, vector, tensor?)
• Software packages
• Mpeg encoding

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What is Visualization?

• "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. In many fields it is
  revolutionizing the way scientists do science."
•  
• Visualization in Scientific Computing
• ACM SIGGRAPH, 1987.

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Why Visualize?

• half of the human brain is devoted directly or
  indirectly to vision
• Professor Mriganka Sur, MIT's Department of
  Brain and
• Cognitive Sciences
• The human visual system can detect and
  discriminate between an incredibly diverse
  assortment of stimuli that may be chromatic or
  achromatic, in motion or not, patterned or
  unpatterned, two-dimensional or three.
• Matthew Schmolesky in The Primary Visual
  Cortex
• http//webvision.med.utah.edu/VisualCortex.html

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Scientific Visualization

• Scientific data is often spatial so we have a
  context for viewing the data already (a physical
  or geometric correspondence).
• Scientific visualization usually refers to
  visualizations having a natural physical (3D)
  representation, for example flow fields or
  geographic distributions of changes in
  temperature.

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Visualization Pipeline

• Consider data in one lump an object
• Can think of this in terms of data flow
• To process the data
• Data flows from a source (reader)
• Data is modified/manipulated (filters)
• Data is transformed to geometry (mappers)
• Geometry is rendered
• Data travels between operations (connections)

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Visualization Pipeline

• read/generate data
• apply visualization algorithms (filters)
• modify the data
• map the results to graphics primitives
• points, lines, polygons (triangles,
  quadrilaterals),
• render the results into an image

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Visualization Pipeline
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Visualization Pipeline

• A source of data is providing data to the
  visualization system
• A filter takes data from the source, manipulates
  it in some way and passes it on
• Selecting the data of interest to us
• Transformations, scaling, thresholding,
• A mapper takes the data and creates geometric
  primitives which can then be rendered
• creates objects in the model

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Rendering

• Convert model (geometrical) data into images
• Produce the visualization
• Transforms data into graphical data (primitives)
• points, lines, polygons (triangles,
  quadrilaterals),
• Graphical primitives are rendered
• Image, object or volume rendering

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Image-Based Rendering

• Have a model with some lighting
• Arrange the view (camera)
• Rendering generates the image seen from the
  cameras position

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Image Rendering

• Get an image as seen from the cameras viewpoint
• Lights give shadows, highlights and can modify
  color
• Shows surfaces those nearest the camera occlude
  those behind

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Volume Rendering

• Displays all of the 3-D data at once
• like an X-ray, the denser parts are more opaque.
• User can control the density of data values.

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Volume Rendering Hydrogen
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Visualization Software

• AVS/Express visual programming
• VTK (Visualization Toolkit)
• Requires programming (TCL/TK, Python, C)
• OpenDX visual programming
• Iris Explorer visual programming