scivl: a descriptive language for 2d multivariate scientific visualization synthesis

SciVL: A Descriptive Language for 2D Multivariate Scientific Visualization Synthesispresented by Jason Sobel

advisor: Prof. David Laidlaw

Road Map

Motivation and Introduction

Implementation

Language Specification

Conclusions and Future Work

Motivations

Good visualizations take time

1. Decide on “visual elements”

2. Code and debug

3. Evaluate and iterate

Motivations (cont.)

“Optimize” visualizations Find best combinations of visual properties

Our Question

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

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

Goals

The language should be:1. Simple

2. Expressive

3. Flexible

4. Hierarchical

5. Easily broken in to “genes”

Contributions

Understanding of “key” visual properties

Rapid prototyping system

Foundation for future work

Road Map

Motivation and Introduction

Implementation

Language Specification

Conclusions and Future Work

Layer System

Three types of layers: IconColorplaneStreamline

Each layer defines some number of visual elements

Rendering

A SciVL file specifies an arbitrary number of layers

They are combined to produce the final image

Values: Specifying “Numbers”

Visual properties are not given number values in the SciVL file

They are given abstract Values, one of:ConstantRandomData-driven

Realization

When rendering a layer, we realize a Value to get a numberUse location to map to data

Values Example

Icon Layer

Let’s look at all the properties of an icon layer

The following images were made using a gradient dataset0 on the left to 1 on the right

All Forms

Circle Form

Rectangle Form

Triangle Form

Multi-Offset Forms

Compound Forms

Color

Color (Partial Range)

Alpha

Borders

Border Color

Border Alpha & Width

Spacing

Orientation

Texture

Failures

Jitter

Example Icons

Colorplane Layer

Used for “regions” or “washes” of color

Colorplanes

Colorplanes in Use

Streamline Layer

Useful for visualizing vector data like velocity or vorticity

Streamlines Color & Alpha

Streamlines Width & Texture

Streamline Density

Road Map

Motivation and Introduction

Implementation

Language Specification

Conclusions and Future Work

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

VisEl LayerBEGIN_LAYER VISELNVISELS 1BEGIN_VISELPOISSON POINT Constant .5 Constant .5 Constant 0NFAILS 0NFORMS 1BEGIN_FORMSTAGESHAPE Constant squareNOFFSETS 2 OFFSET POINT Constant 0 Constant 0 Constant 0 OFFSET POINT Constant 5 Constant 0 Constant 0BEGIN_STYLENCOLORS 1 POINT Variable gradient_x .4 .6 Constant .8 Constant .8NALPHAS 1 Constant .8NTEXTURES 0NORIENTATIONS 1 Random 0 .1 NBORDERS 1 COLOR POINT Variable gradient_y 0 .3 Constant .7 Constant .8 ALPHA Random .8 1 WIDTH Constant 2NSCALES 0NDIMENSIONS 1 POINT Variable gradient_y 3 6 Constant 0 Constant 0END_STYLEEND_FORMSTAGEEND_VISELEND_LAYER

Demo

Colorplane Layers

Similar syntax Can control, per vertex:

FailuresColorAlpha

Streamline Layers

Similar syntax Can control:

Failures Vector to follow Survival Density Color/Transparency Size Texture

Road Map

Motivation and Introduction

Implementation

Language Specification

Conclusions and Future Work

More Pictures

Success?

Goals were:1. Simple

2. Expressive

3. Flexible

4. Hierarchical

5. Easily broken in to “genes”

Did we accomplish these goals?

Anecdotal Feedback

A “design-expert” professor from RISD

A scientist with radar polarimetry data

Challenges

Allowing every possible combination

Interfacing with any kind of data

Finding “correct” visual elements & properties

Future Work

Genetic AlgorithmsCan 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?

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

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