1. SciVL: A Descriptive Language for 2D Multivariate Scientific Visualization Synthesis presented by Jason Sobel advisor: Prof. David Laidlaw

2. Road Map Motivation and Introduction Implementation Language Specification Conclusions and Future Work

3. Motivations Good visualizations take time 1. Decide on “visual elements” 2. Code and debug 3. Evaluate and iterate

4. Motivations (cont.) “Optimize” visualizations Find best combinations of visual properties

5. Our Question Can we provide a fast and easy way to prototype visualizations that also allows optimization?

6. Proposed Solution Define a language that can be used to represent a visualization Create an instance in a text file Apply an instance to a dataset to generate an image

7. Goals The language should be: 1. Simple 2. Expressive 3. Flexible 4. Hierarchical 5. Easily broken in to “genes”

8. Contributions Understanding of “key” visual properties Rapid prototyping system Foundation for future work

9. Road Map Motivation and Introduction Implementation Language Specification Conclusions and Future Work

10. Layer System Three types of layers:  Icon  Colorplane  Streamline Each layer defines some number of visual elements

11. Rendering A SciVL file specifies an arbitrary number of layers They are combined to produce the final image

12. Values: Specifying “Numbers” Visual properties are not given number values in the SciVL file They are given abstract Values, one of:  Constant  Random  Data-driven

13. Realization When rendering a layer, we realize a Value to get a number  Use location to map to data

14. Values Example

15. Icon Layer Let’s look at all the properties of an icon layer The following images were made using a gradient dataset  0 on the left to 1 on the right

16. All Forms

17. Circle Form

18. Rectangle Form

19. Triangle Form

20. Multi-Offset Forms

21. Compound Forms

22. Color

23. Color (Partial Range)

24. Alpha

25. Borders

26. Border Color

27. Border Alpha & Width

28. Spacing

29. Orientation

30. Texture

31. Failures

32. Jitter

33. Example Icons

34. Colorplane Layer Used for “regions” or “washes” of color

35. Colorplanes

36. Colorplanes in Use

37. Streamline Layer Useful for visualizing vector data like velocity or vorticity

38. Streamlines Color & Alpha

39. Streamlines Width & Texture

40. Streamline Density

41. Road Map Motivation and Introduction Implementation Language Specification Conclusions and Future Work

42. Layer System The language specifies visual elements layer by layer The syntax is a simple interface to all the properties described above  Allows specifying a Value for each one

43. VisEl Layer BEGIN_LAYER VISEL NVISELS 1 BEGIN_VISEL POISSON POINT Constant.5 Constant.5 Constant 0 NFAILS 0 NFORMS 1 BEGIN_FORMSTAGE SHAPE Constant square NOFFSETS 2 OFFSET POINT Constant 0 Constant 0 Constant 0 OFFSET POINT Constant 5 Constant 0 Constant 0 BEGIN_STYLE NCOLORS 1 POINT Variable gradient_x.4.6 Constant.8 Constant.8 NALPHAS 1 Constant.8 NTEXTURES 0 NORIENTATIONS 1 Random 0.1 NBORDERS 1 COLOR POINT Variable gradient_y 0.3 Constant.7 Constant.8 ALPHA Random.8 1 WIDTH Constant 2 NSCALES 0 NDIMENSIONS 1 POINT Variable gradient_y 3 6 Constant 0 Constant 0 END_STYLE END_FORMSTAGE END_VISEL END_LAYER

44. Demo

45. Colorplane Layers Similar syntax Can control, per vertex:  Failures  Color  Alpha

46. Streamline Layers Similar syntax Can control:  Failures  Vector to follow  Survival  Density  Color/Transparency  Size  Texture

47. Road Map Motivation and Introduction Implementation Language Specification Conclusions and Future Work

48. More Pictures

49. Success? Goals were: 1. Simple 2. Expressive 3. Flexible 4. Hierarchical 5. Easily broken in to “genes” Did we accomplish these goals?

50. Anecdotal Feedback A “design-expert” professor from RISD A scientist with radar polarimetry data

51. Challenges Allowing every possible combination Interfacing with any kind of data Finding “correct” visual elements & properties

52. Future Work Genetic Algorithms  Can we create the perfect visualization?  Was man meant to play God? Visualization “Rules”  Can we find “The Do’s and Don’ts” of Scientific Visualization?

53. Thanks Prof. David Laidlaw Daniel Acevedo Cullen Jackson Eileen Vote David Karelitz Daniel Keefe Prof. Fritz Drury Dean Turner Prof. Andy van Dam Morriah Horani Sci Vis Family and Friends