1. Computational Methods in Physics PHYS 3437 Dr Rob Thacker Dept of Astronomy & Physics (MM-301C) thacker@ap.smu.ca

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

3. 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 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 ACM SIGGRAPH, 1987

4. 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

5. Visualization vs. Analysis? Visualization is best applied to data mining and data discovery Visualization is best applied to data mining and data discovery Visualization tools are helpful for exploring hunches and presenting results Visualization tools are helpful for exploring hunches and presenting results Example: scatterplots Example: scatterplots Visualization is the WRONG primary tool when the goal is to find a good model in a complex situation Visualization is the WRONG primary tool when the goal is to find a good model in a complex situation May provide hints but won’t provide concrete answers May provide hints but won’t provide concrete answers Model building requires insight into the problem at hand Model building requires insight into the problem at hand

6. 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

7. Simulation of Hurricane Earl (1998)

8. Preattentive Processing A limited set of visual properties are processed preattentively A limited set of visual properties are processed preattentively without need for focusing attention without need for focusing attention Note, this is critical in web-site design Note, this is critical in web-site design 4 seconds before user decides they don’t understand the page… 4 seconds before user decides they don’t understand the page… Important for design of visualizations Important for design of visualizations what can be perceived immediately what can be perceived immediately Does the viewer get the information without having to consciously process the image? Does the viewer get the information without having to consciously process the image? what properties are good discriminators? what properties are good discriminators?

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

10. Example: Color Selection We can instantly see the red dot – we have preemptively processed the different hue.

11. Example: Shape Selection Viewer can rapidly and accurately determine whether the target (red circle) is present or absent. Difference detected in form (curvature)

12. Example: Conjunction of Features Finding the target (red circle) requires that we sequentially search through the image. We can’t rapidly (or accurately) determine the presence of the target when there are two or more features that differentiate it from the remaining information (distractors).

13. 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.

14. Example: Emergent Features We cannot detect the target preattentively as it has no unique feature relative to the distractors.

15. Asymmetric and Graded Preattentive Properties Some properties are asymmetric Some properties are asymmetric a sloped line among vertical lines is preattentive a sloped line among vertical lines is preattentive a vertical line among sloped ones is not a vertical line among sloped ones is not Some properties have a gradation Some properties have a gradation some more easily discriminated among than others some more easily discriminated among than others

16. 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!

17. Preattentive Visual Properties (Healey 97) length Triesman & Gormican [1988] length Triesman & Gormican [1988] width Julesz [1985] width Julesz [1985] size Triesman & Gelade [1980] size Triesman & Gelade [1980] curvature Triesman & Gormican [1988] curvature Triesman & Gormican [1988] number Julesz [1985]; Trick & Pylyshyn [1994] number Julesz [1985]; Trick & Pylyshyn [1994] terminators Julesz & Bergen [1983] terminators Julesz & Bergen [1983] intersection Julesz & Bergen [1983] intersection Julesz & Bergen [1983] closure Enns [1986]; Triesman & Souther [1985] closure Enns [1986]; Triesman & Souther [1985] colour (hue) Nagy & Sanchez [1990, 1992]; D'Zmura [1991] Kawai et al. [1995]; Bauer et al. [1996] 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] intensity Beck et al. [1983]; Triesman & Gormican [1988] flicker Julesz [1971] flicker Julesz [1971] direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992] direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992] binocular lustre Wolfe & Franzel [1988] binocular lustre Wolfe & Franzel [1988] stereoscopic depth Nakayama & Silverman [1986] stereoscopic depth Nakayama & Silverman [1986] 3-D depth cues Enns [1990] 3-D depth cues Enns [1990] lighting direction Enns [1990] lighting direction Enns [1990]

18. Humour: 14 ways to say nothing with visualization (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 Globus & Raible, 1994

19. 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) Don’t 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 can’t be hidden by choosing an angle, use shadows (14) “This is easily extended to 3d!”

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

21. HSV colour space The HSV (Hue, Saturation, Value) model, defines a color space in terms of three constituent components: 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): Hue, the color type (e.g. red, blue, or yellow): Ranges from 0-360 (but normalized to 0-100% in some applications) Ranges from 0-360 (but normalized to 0-100% in some applications) Saturation, the "vibrancy" of the color: Saturation, the "vibrancy" of the color: Ranges from 0-100% Ranges from 0-100% Also sometimes called the "purity" by analogy to the colorimetric quantities excitation purity and colorimetric purity 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 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: Value, the brightness of the color: Ranges from 0-100% Ranges from 0-100% Much more strongly related to the human perception of colour than RGB

22. 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

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

24. Perception of magnitude Often need to visualize a single variable at many places (scalar field) 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 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 In order to accurately represent detailed information the “visual dimension” chosen should appear continuous to the user Rules out the rainbow colourmap immediately Rules out the rainbow colourmap immediately Perceived magnitude obeys a power relationship with physical luminance over a very large range of gray scales 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 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 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

25. 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 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 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 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 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

26. Low frequency information Two colours allow you to pick out the larger systems Two colour map over emphasizes large scale features, and some detail around these features is lost High frequency information

27. Segmented maps Losing high freq info? Better low freq info? But not apples to apples comparison

28. Key Questions to Ask about a Viz 1. Is it for analysis or presentation? 2. What does it teach/show/elucidate? 3. What is the key contribution? 4. What are some compelling, useful examples? 5. Could it have been done more simply? 6. Have there been usability studies done? What do they show?

29. Are we just limited to 3d? A visualization can use x, y, and z to represent the spatial dimensions of an object 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 color can be mapped onto a surface representing a fourth the surface can be deformed according to a fifth the surface can be deformed according to a fifth isocontour lines can represent a sixth isocontour lines can represent a sixth coloring them can represent a seventh coloring them can represent a seventh glyphs on the surface can represent a few more, not to mention animation, transparency, and stereo 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, 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 can easily give rise to visual representations which do not adequately represent the structure in the data or which introduce misleading visual artifacts

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

31. About Opendx Began as an IBM product: “Visualization Data Explorer” Began as an IBM product: “Visualization Data Explorer” IBM released it Open Source and it was renamed Opendx 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 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”) 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 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 There is a large body of additional modules made available by other users Great resource! Great resource!

32. 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

33. Getting started with OpenDX Windows: If you are running the windows version you’ll need an X- server running Type startx at the Cygwin prompt to do this Linux: type dx at the command prompt Linux: type dx at the command prompt Main dx panel http://www.opendx.org

34. Steps in creating a dx program While there are many approaches the easiest way to begin with is While there are many approaches the easiest way to begin with is Import data into dx Import data into dx Click on the “Import data” button Click on the “Import data” button You will need to describe the precise format though You will need to describe the precise format though Write the visual program using the VPE Write the visual program using the VPE Click on “Edit Visual Programs” button Click on “Edit Visual Programs” button

35. Opendx example If time… If time…

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

37. Next lecture Visualization & data representation Visualization & data representation More on Opendx More on Opendx 3d visualization methods 3d visualization methods Isosurfaces Isosurfaces Volume rendering Volume rendering