Difference between revisions of "SciVisFall2008/Final"

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(New page: This is your final for CS 5630/6630. The assignment is due at midnight on '''December 15th, 2008'''. You will need to use the CADE handin functionality to turn in your assignment. The cla...)
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Revision as of 16:02, 11 December 2008

This is your final for CS 5630/6630.

The assignment is due at midnight on December 15th, 2008. You will need to use the CADE handin functionality to turn in your assignment. The class account is "cs5630".

This assignment was successfully tested in release 1.2.1rev1336. It should work fine in releases >=1.2.1rev1336. Check your release before starting your work and upgrade it if necessary.

As usual, as you work on the assignment, we encourage you to read the available documentation on both python and VTK. Remember that VisTrails allows you to easily access the VTK documentation. To see the documentation of a VTK module in the Modules panel, just right click on its name and choose View Documentation in the context menu. Analogously, you can right click on a method's name in the Methods panel to see it's documentation. Some of the problems will require you to use VTK modules you might not have previously seen.

General Hints

We have provided a PythonSource module that inspects and prints out the dataset. Take advantage of this to fully explore the various properties and fields of this data.

Data

The data for this assignment are in three files:

Final.vt (Final.vt)

stream2q.bin (download)

stream2XYZ.bin (download)


  • Final.vt is the VisTrails file to be used as starting point. This .vt includes HTTPFiles and appropriate readers to download and read in the data. Both data are used to form a single VTK dataset.

Dataset Description

This dataset is the result of astrophysical simulation of a binary star system. This single snapshot of the simulation is from timestep 2172 of the evolving system. While the overall simualtion tracks many variables, here we present just a few.

Tasks

Create 1 or more visualizations that convey interesting aspects of this data to the viewer. Please label the visualizations of interest as follows: Visualization 1, Visualization 2, ... Visualization N

For each visualization produced, describe in the Notes section what aspect, or feature, of the data the visualization highlights and what you learned about the overall data from it.

Hints

Is the dataset symmetric?

Are the various scalar fields in this data correlated?

Potential Problems