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.
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.
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.
Four examplary data sets are provided:
- 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.
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.