SciVL: A Descriptive Language for 2D Multivariate Scientific Visualization Synthesis

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 • Decide on “visual elements” • Code and debug • 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: • Simple • Expressive • Flexible • Hierarchical • 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: • Simple • Expressive • Flexible • Hierarchical • 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