ProbVis

Visualization and exploration tool for probability
distribution functions

Overview

The ProbVis software tool allows for the interactive display and
exploration of a spatial collection of data distributions. A global
display shows the value of a difference measure across the spatial
domain. The user can change the measure from the L1 Norm to the
Hellinger distance. The user is also given a pointer to explore the
individual distributions which are diplayed as a PDF or CDF in the
lower corner.

Software

ProbVis is written using
the Processing programming
language. To run the standalone applications simply download the
tarball for your platform, uncompress, and double-click on the ProbVis
icon. The source code is a Processing sketch which can be run and
modified using the Processing framework, freely available
at Processing.org.

Processing Source Code:

**ProbVis.tgz**

Data

The software tool allows the user to investigate any dataset, as long
as conforms to our format. The data should be in its own directory,
and separated into 3 files: geometry.txt, connectivity.txt, and
data.txt. The names of these files must match exactly, and none have
headers.

**geometry.txt**This file describes the 3D points of the spatial domain. Each point should be written on a single line, (x, y, z), seperated by a space.**connectivity.txt**This file describes the triangles of the spatial domain. Each triangle is written on a single line, and the three points are defined by indices from geometry.txt.**data.txt**This file holds the data distribution at each point. Each line will contain all data samples for a single point. The order of the points should be the same as geometry.txt. Any number of samples is allowed, as long as it is the same number for every point.

**Alternating**This dataset defines an alternating pattern across the rectangular spatial domain. Gaussian and uniform distributions are alternated along the x-axis, as is the interval width.**Shape**This dataset linearly blends from a normal to a uniform distribution along the x-axis.**Interval**This dataset is uniform across the entire spatial domain, but increases the interval width along the x-axis.**Torso**This dataset is the result of solving the bioelectric forward problem across a 2D torso domain using poynomial chaos.

Publication

Interactive Visualization of Probability and Cumulative Density Functions

Kristin Potter, Robert M. Kirby, Dongbin Xiu, & Chris R. Johnson

*International Journal for Uncertainty Quantification*, to appear. 2011.

Kristin Potter, Robert M. Kirby, Dongbin Xiu, & Chris R. Johnson

Movies

Contact

Kristi Potter kpotter@sci.utah.edu SCI Institute University of Utah |
Robert M. Kirby kirby@sci.utah.edu SCI Institute University of Utah |
Dongbin Xiu dxiu@purdue.edu Dept of Mathematics Purdue University |
Chris R. Johnson crj@sci.utah.edu SCI Institute University of Utah |

Acknowledgements

This is a collaborative research project supported under NSF IIS-0914564, NSF IIS-0914447 and through DOE NETL DE-EE0004449, and
NIH 2P41 RR0112553-12 (Johnson). Infrastructure support provided
through NSF-IIS-0751152.