This last week saw SIGGRAPH 2010 in Los Angeles, sunny California. It was there that I gave my first talk at a real conference. One request that I had not anticipated was for the slides from the talk, and so I post them here now.
Pending some red tape resolution, I hope to post the live-working demos soon. Until then, I hope that this video from the original conference submission will whet your appetite!
For those particularly curious, feel free to contact me or refer to the abstract.
This week for Scientific Visualization, we’re talking about volume rendering and using VTK to explore some data. I got some datasets from The Volume Library and after a little tinkering, got VTK to render them. (And now a quick aside on how to do this as I didn’t find much information on the subject).
I used a tool (pvm2raw) available as part of the V^3 library to convert the pvm files to raw, but VTK requires its own simple header. I actually found that this particular header didn’t work (perhaps a VTK versioning problem?) and so taking guidance from this, checked the header of one of the VTK-included volumes:
bash $> head VTKData/ironProt.vk
This header more or less included a little information on the grid size, spacing and representation of the data:
# vtk DataFile Version 1.0
<Name of File>
BINARY
DATASET STRUCTURED_POINTS
DIMENSIONS <x> <y> <z>
ASPECT_RATIO 1 <y/x> <z/x>
ORIGIN 0 0 0
POINT_DATA <x * y * z>
SCALARS scalars <unsigned_char|unsigned_short>
LOOKUP_TABLE default
<remember to include a newline here>
Concatenating the header with the raw:
bash $> cat header CT-Head.raw > CT-Head.vtk
At that point, I was in business and was able to move on to generating pretty pictures. Granted, these datasets are pretty sparse, but still VTK did a pretty reasonable job. Update: a comment asked for a little bit more detail on this assignment, and so I’m including my report for the project.
I was amazed today that we can see inside of things… without taking them apart. What an age to live in. Especially the virtual autopsy table I read about recently. In 20 years, we’ll have Firefly-style real-time holographic body scans (ignore music, skip to 0:45):
A professor of mine recently criticized some graphs I submitted on a paper and handed me a book by Edward Tufte called The Visual Display of Quantitative Information
He talks about the misconception that graphics lie. Of course some do, but his attitude encapsulates well what I think is great about visualization – good representations convey understanding. Graphics can be the most effective way to get a handle on data, or a trend, and they should reveal what underlies the numbers. But in a world of Excel and every insignificant and meaningless piece interrelationship being plotted in an impressive-looking format, it’s easy to forget this.
A quote I heard recently in my Scientific Visualization course (thanks, Thomas!) puts it well:
Visualize to inform, not to impress. If you really inform, you will impress. – Fred Brooks. SIGGRAPH 2003
Although a child can understand a time series, it wasn’t until a couple hundred years ago that they were actually used, as Tufte points out, but its power to convey is obvious. Similarly, just from glancing at a map like this one from the census bureau, one can almost instantly understand the distribution of income across the United States – literally tens of thousands of pieces of data.
A US Census Bureau graphic depicting the income of the 3000+ counties of the United States.
In this vein of conveying understanding, I remember several years ago now watching a TED Talk that immediately captivated me with visualization. Hans Rosling talks about how often when we see the rows about rows and tables upon tables of the massive amounts of census data, not only do our eyes glaze over but it becomes very difficult to keep it all in one’s head at any one time. Visualizing the data is thus a key tool for gaining the insight we seek.
The book is full of tremendous insight about how ink should be used as efficiently as possible (within reason – Tufte is quick to emphasize this point) and that the human eye has a great capacity for handling dense data sets if presented efficiently. It is an entirely necessary resource for anyone who intends to pursue any science, nay, anyone intends to pursue any discipline dealing with numbers.
He has several other books, all of which I intend to read as I was virtually unable to put down the book; I was constantly floored by the myriad examples of strong and weak graphics alike. He also published a book by his mother that I happened to encounter recently via Cool Tools.
I’ll close with a brief excerpt from his book with which I was taken:
Words and pictures belong together. Viewers need the help that words can provide. Words on graphics are data-ink, making effective use of the space freed up by erasing redundant and non-data-ink. It is nearly always helpful to write little messages on the plotting field to explain the data, to label outliers and interesting data points, to write equations and sometimes table son the graphic itself, and to integrate the caption and legend into the design so that the eye is not required to dart back and fort between textual material and the graphic.