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SciVL is an innovative descriptive language designed for rapid prototyping and optimization of 2D multivariate scientific visualizations. Developed by Jason Sobel under the guidance of Prof. David Laidlaw, SciVL allows users to specify visual properties in a hierarchical manner, enabling the creation of complex visualizations with ease. The language supports various layer types, including icon, colorplane, and streamline layers, facilitating a flexible approach to visualization. By defining abstract values and a simple syntax, SciVL aims to enhance the creativity and efficiency of scientific data representation and exploration.
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SciVL: A Descriptive Language for 2D Multivariate Scientific Visualization Synthesis presented by Jason Sobel advisor: Prof. David Laidlaw
Road Map • Motivation and Introduction • Implementation • Language Specification • Conclusions and Future Work
Motivations • Good visualizations take time • Decide on “visual elements” • Code and debug • 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: • Simple • Expressive • Flexible • Hierarchical • 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: • Icon • Colorplane • Streamline • 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: • Constant • Random • Data-driven
Realization • When rendering a layer, we realize a Value to get a number • Use location to map to data
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
Colorplane Layer • Used for “regions” or “washes” of color
Streamline Layer • Useful for visualizing vector data like velocity or vorticity
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 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
Colorplane Layers • Similar syntax • Can control, per vertex: • Failures • Color • Alpha
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
Success? • Goals were: • Simple • Expressive • Flexible • Hierarchical • Easily broken in to “genes” • Did we accomplish these goals?
Anecdotal Feedback • A “design-expert” professor from RISD • A scientist with radar polarimetry data