Computational Methods in Physics PHYS 3437

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Computational Methods in Physics PHYS 3437

• Dr Rob Thacker
• Dept of Astronomy Physics (MM-301C)
• thacker_at_ap.smu.ca

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Todays Lecture

• Visualization
• Useful results to know about perception of
  information
• Help you to gain some of idea of why that looks
  bad
• How not to visualize
• Beginning using Opendx (more next lecture)
• Sources for todays lecture
• http//www.research.ibm.com/dx/proceedings/pravda/
  truevis.htm
• Spatial information colour maps
• http//www2.sims.berkeley.edu/courses/is247/f05/sc
  hedule.html
• Lectures by Marti Hearst
• http//www.cs.unb.ca/acrl/training/visual/macphee-
  intro_to_visual/Intro_to_Visual.ppt
• Introduction to Opendx by Chris MacPhee

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Visualization as computing

• 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 use Visualization?
A picture says more than a thousand words. A
picture says more than a thousand numbers. The
purpose of scientific computing is insight, not
numbers. - Dr. Richard Hamming, Naval
Postgraduate School, California ... half of the
human brain is devoted directly or indirectly to
vision ... - Prof. Mriganka Sur, Brain and
Cognative Sciences, MIT
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Visualization vs. Analysis?

• Visualization is best applied to data mining and
  data discovery
• Visualization tools are helpful for exploring
  hunches and presenting results
• Example scatterplots
• Visualization is the WRONG primary tool when the
  goal is to find a good model in a complex
  situation
• May provide hints but wont provide concrete
  answers
• Model building requires insight into the problem
  at hand

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Value of visualization for the cynical!

• Conclude your technical presentation and roll
  the videotape. Audiences love razzle-dazzle
  color graphics, and this material often helps
  deflect attention from the substantive technical
  issues.

David Bailey, NERSC
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Simulation of Hurricane Earl (1998)
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Preattentive Processing

• A limited set of visual properties are processed
  preattentively
• without need for focusing attention
• Note, this is critical in web-site design
• 4 seconds before user decides they dont
  understand the page
• Important for design of visualizations
• what can be perceived immediately
• Does the viewer get the information without
  having to consciously process the image?
• what properties are good discriminators?

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Pre-attentive Processing

• lt 200 - 250ms qualifies as pre-attentive
• eye movements take at least 200ms
• yet certain processing can be done very quickly,
  implying low-level processing in parallel
• If a decision takes a fixed amount of time
  regardless of the amount of information
  presented, it is considered to be preattentive

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Example Color Selection
We can instantly see the red dot we
have preemptively processed the different hue.
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Example Shape Selection
Viewer can rapidly and accurately
determine whether the target (red circle) is
present or absent. Difference detected in form
(curvature)
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Example Conjunction of Features
Finding the target (red circle) requires that we
sequentially search through the image. We cant
rapidly (or accurately) determine the presence of
the target when there are two or more features
that differentiate it from the remaining informati
on (distractors).
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Example Emergent Features
Despite being constructed from similar
components to the distractors, the unique feature
of the target (open sides) allows us to process
its presence preattentively.
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Example Emergent Features
We cannot detect the target preattentively as it
has no unique feature relative to the distractors.
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Asymmetric and Graded Preattentive Properties

• Some properties are asymmetric
• a sloped line among vertical lines is
  preattentive
• a vertical line among sloped ones is not
• Some properties have a gradation
• some more easily discriminated among than others

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SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP
YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS
NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH
RECORDS COLUMNS ECNEICS HSILGNE SDROCER
SNMULOC GOVERNS PRECISE EXAMPLE MERCURY SNREVOG
ESICERP ELPMAXE YRUCREM CERTAIN QUICKLY PUNCHED
METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM GOVERNS
PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE
YRUCREM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS
HSILGNE SDROCER SNMULOC SUBJECT PUNCHED QUICKLY
OXIDIZED TCEJBUS DEHCNUP YLKCIUQ
DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC
YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS
COLUMNS ECNEICS HSILGNE SDROCER SNMULOC
Text is NOT Preattentive!
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Preattentive Visual Properties(Healey 97)

• length Triesman
  Gormican 1988
• width Julesz
  1985
• size Triesman
  Gelade 1980
• curvature Triesman
  Gormican 1988
• number Julesz
  1985 Trick Pylyshyn 1994
• terminators Julesz
  Bergen 1983
• intersection Julesz
  Bergen 1983
• closure Enns
  1986 Triesman Souther 1985
• colour (hue) Nagy
  Sanchez 1990, 1992 D'Zmura 1991
  Kawai et al.
  1995 Bauer et al. 1996
• intensity Beck et
  al. 1983 Triesman Gormican 1988
• flicker Julesz
  1971
• direction of motion Nakayama
  Silverman 1986 Driver McLeod 1992
• binocular lustre Wolfe
  Franzel 1988
• stereoscopic depth Nakayama
  Silverman 1986
• 3-D depth cues Enns 1990
• lighting direction Enns 1990

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Humour 14 ways to say nothing with visualization
Globus Raible, 1994

1. Never include a color legend
2. Avoid annotation
3. Never mention error characteristics
4. When in doubt smooth
5. Avoid providing any performance data
6. Cunningly use stop-frame techniques
7. Never learn anything about the underlying data or
   discipline

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Humour 14 ways to say nothing with visualization
- 2

• (8) Never provide contrasting visualizations to
  other data
• (9) Always ensure you develop your own new tool
  and disregard others as out-dated and out-moded
• (10) Dont cite references for data
• (11) Claim generality but only ever show results
  from one data set
• (12) Use viewing angle to hide unwanted
  information
• (13) If something cant be hidden by choosing an
  angle, use shadows
• (14) This is easily extended to 3d!

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Representing different types of data

• Nominal data
• Data is put into categories with no implicit
  ordering
• e.g. red and blue cars
• Should be represented by distinguishably
  different objects without any perceived ordering
• Ordinal data
• Data is put into categories that have an implied
  ordering structure
• e.g. A,B,C.. grades in a class
• Should be represented by distinguishably
  different objects with a perceived ordering
• Interval data
• Data is not categorized and is instead described
  by a numeric system
• e.g. temperatures, most scientific data
• Equal steps in data value should appear as steps
  of equal perceived magnitude in the
  representation

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HSV colour space

• The HSV (Hue, Saturation, Value) model, defines a
  color space in terms of three constituent
  components
• Hue, the color type (e.g. red, blue, or yellow)
• Ranges from 0-360 (but normalized to 0-100 in
  some applications)
• Saturation, the "vibrancy" of the color
• Ranges from 0-100
• Also sometimes called the "purity" by analogy
  to the colorimetric quantities
  excitation purity and colorimetric
  purity
• The lower the saturation of a color, the more
  "grayness" is
  present and the more faded the
  color will appear
• Value, the brightness of the color
• Ranges from 0-100

Much more strongly related to the human
perception of colour than RGB
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Colour maps

• The most common (default) colormap is the
  rainbow map (shown below)
• maps the lowest value in the variable to blue,
  the highest value to red, and interpolates in
  color space (red, green, blue) to produce a color
  scale.
• Produces several well-documented artifacts
• You will percieve 5 layers in the visualization
• Yellow regions are perceived as more significant
  due to their brighter colour

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All plots use the same data, but different
colourmaps give the appearance of less or more
information
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Perception of magnitude

• Often need to visualize a single variable at many
  places (scalar field)
• In many cases, the interpretation of the data
  depends on having the visual picture accurately
  represent the structure in the data
• In order to accurately represent detailed
  information the visual dimension chosen should
  appear continuous to the user
• Rules out the rainbow colourmap immediately
• Perceived magnitude obeys a power relationship
  with physical luminance over a very large range
  of gray scales
• Explains why grayscale colormaps are commonly
  used in medical imaging
• Another method which displays this behavior is
  color saturation, the progression of a color from
  vivid to pastel

Value increases monotonically, while saturation
becomes more pastel
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Perception of spatial frequency

• The value component in a color (the
  brightness/darkness component) is critical for
  carrying information about high spatial frequency
  variations in the data
• If the colour map does not contain a monotonic
  value variation, fine resolution information will
  not be seen
• The saturation and hue components in color are
  critical for carrying information about low
  spatial frequency variations in the data
• A colour map which only varies in luminance
  (e.g., a grayscale image) cannot adequately
  communicate information about gradual changes in
  the spatial structure of the data

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Low frequency information
Two colours allow you to pick out the larger
systems
High frequency information
Two colour map over emphasizes large scale
features, and some detail around these
features is lost
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Segmented maps
Better low freq info?
Losing high freq info?
But not apples to apples comparison
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Key Questions to Ask about a Viz

• Is it for analysis or presentation?
• What does it teach/show/elucidate?
• What is the key contribution?
• What are some compelling, useful examples?
• Could it have been done more simply?
• Have there been usability studies done? What do
  they show?

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Are we just limited to 3d?

• A visualization can use x, y, and z to represent
  the spatial dimensions of an object
• color can be mapped onto a surface representing a
  fourth
• the surface can be deformed according to a fifth
• isocontour lines can represent a sixth
• coloring them can represent a seventh
• glyphs on the surface can represent a few more,
  not to mention animation, transparency, and
  stereo
• This great flexibility, however, can open a
  Pandora's box of problems for the user,
• can easily give rise to visual representations
  which do not adequately represent the structure
  in the data or which introduce misleading visual
  artifacts

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Public domain viz tools

• While there are a host of public domain tools,
  the two most popular are probably
• VTK The Visualization toolkit
• http//www.vtk.org/
• Mid-level library that requires you construct
  scripts (Tcl-Tk) to run your visualization
• Very powerful, allows you to wrap visualization
  code in with your own C
• Drawback fairly steep learning curve
• Opendx
• Freely available, packaged visualization program
  (as opposed to library)
• Quick to get going with, so well use it in this
  course

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About Opendx

• Began as an IBM product Visualization Data
  Explorer
• IBM released it Open Source and it was renamed
  Opendx
• Note, they held back some patented routines, but
  most of the nuts and bolts are there
• Approach to visualization is to create a network
  of functions that link together within a visual
  program editor (VPE)
• Takes a while to get used to, but once you are
  familiar with it things are very easy
• There is a large body of additional modules made
  available by other users
• Great resource!

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A simple visual program example

• Each module has a specific action
• Many of the modules have hidden features as well
• Why this format?
• Related to the concept of a rendering pipeline

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Getting started with OpenDX

• Windows If you are running the windows version
  youll need an X-server running
• Type startx at the Cygwin prompt to do this
• Linux type dx at the command prompt

Main dx panel
http//www.opendx.org
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Steps in creating a dx program

• While there are many approaches the easiest way
  to begin with is
• Import data into dx
• Click on the Import data button
• You will need to describe the precise format
  though
• Write the visual program using the VPE
• Click on Edit Visual Programs button

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Opendx example

• If time

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Summary

• The HSV colour space is much more closely related
  to human perception than RGB
• Some information can be processed preattentively
  and successful visualizations can exploit this
• The standard rainbow colour map has two
  significant artifacts for visualization
• 5 layers are explicitly represented
• Yellow tends to dominate visually
• Describing high frequency information is best
  achieved using value and saturation based colour
  maps
• Low frequency information is elucidated well
  using hue based maps
• Opendx is very powerful, but free, tool that
  originated out of the IBM Data Explorer project

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Next lecture

• Visualization data representation
• More on Opendx
• 3d visualization methods
• Isosurfaces
• Volume rendering