I have some data taken from scanning a log (part of the trunk of a tree)
from a lumber mill which I would like to visualize with visad. For each
data point I have theta, x, y, z, and three measured properties p_1, p_2
The data points are organized by increasing z. For each z value there
are a varying number of points ordered by increasing theta. The theta
values are irregular, the z-values could be easily "adjusted" to be at
regular intervals. (z is along the long axis of the log, theta measures
around the ring of a fixed z from a moving center, (x, y, z) are the
"real" location of the data.)
What I want to see are contour graphs of the p_i's on a 2D surface in
3-space, at least initially.
My first guess would be to do an organization like
((theta,z)->((x,y,z),(p_1,p_2,p_3))) and having (theta, z) an irregular
2D structure. But since the z data could be a regular 1D set, is there
any advantage to try and exploit this? Currently the number of theta's
vary from z to z.
The other option is to go to a regular 2D set and interpolate the
theta data to fit. Would the gain in speed be worth this effort?
Each ring (a particular z value) has about 125 data points and each log
has about 330 rings. p_3 is the "amplitude" of the returned laser signal
and p_1 and p_2 are "potential correcting factors". The goal is to
hunt for knots.
It has been a while since I've used visad, so I will not be offended by
"here is a better way" suggestions.
Thanks in advance.