Hi all,
I've merged the latest changes in the yt branch into the
vr-multivariate branch, and I've replicated all my previous volume
rendering results with it. The color transfer function object now
mocks up the old one (you can see this in TransferFunction.py where I
create independent channels, with no density weighting, and then link
them to independent interpolation tables) and I think it's just about
ready to roll out.
If any of you have time, could one or two of you give a go with one of
your existing VR scripts on the vr-multivariate branch of the hg repo,
to make sure there are no surprises? (And if you'd really like to
have a go at the new features, try using the PlanckTransferFunction
instead of the standard ColorTransferFunction! If you do that, toss
your result up on drop.io or a website somewhere so we can see it.)
If it works, I'll merge it in to the main hg-line.
One additional change that I think we'll be able to take advantage of
with this branch is that the partitioning is now two-step: once to
locate the bricks, another to fill them in with vertex centered data.
This might give us some leeway to a second type of brick storage,
where we just store indices into vertex-centered grids.
Thanks,
Matt
On Tue, Mar 16, 2010 at 11:34 PM, Matthew Turk
Hi all,
Over the last little while, the volume renderer has really taken off in popularity! I'm a bit conflicted about this, since I'd rather people were super duper into things like phase plots, radial profiles and clump finding, but it's also good to know people are using yt for volume visualization as well as other types of analysis. I used it myself to what I think was great effect at a conference last week, and I hope that other people are getting similar use out of it.
Anyway, a couple months ago John wrote to yt-users and suggested that we try doing multivariate volume rendering, similar to what's in Kaehler, Wise, & Abel. For a while mostly nothing happened, but over the Friday->Sunday, Jeff and I hammered it out and it's now working. Jeff took care of the emission spectra (with some assistance from John's RGB code), I took a whack at adding multiple variables to the partitioned grids and the volume rendering itself, and we can now do multivariate rendering -- although absorption still needs some work, which I'll discuss below.
The new mechanism for grid partitioning only returns the indices into the vertex-centered data. The actual processing of the vertex-centered data. This means the actual locating of sweeps is much faster -- right now we don't take advantage of that, but I can see it being useful in the future. A few weeks ago I wrote a distributed object collection so that we could do 3D domain decomp to get the bricks, then pass them around between processes for the actual volume rendering, which was parallelized via 2D image plane decomp into decomposed inclined boxes. Now the 3D decomp will only pass around single arrays that describe the indices into the bricks, and then each image-decomp processor will generate and slice up the data as necessary -- this isn't yet working, but it's a clear process and I'll try to take care of it soon.
Transfer functions now have several more parameters, and I'm working to convert the old-style transfer functions to this. The idea is that now you add field interpolation tables which correspond to a specific field, link them to channels (R, Ralpha, G, Galpha, B, Balpha), and optionally have their values weighted by other fields. So for an example, here's how the Planck function sets up its various channels:
http://hg.enzotools.org/yt/file/ede5d76cddd4/yt/extensions/volume_rendering/...
(there's a bit of cruft where I tested adding lines for density absorption.)
It creates a TransferFunction, links it to a field (0) and a weighting field (2), then links it to channels. There's a lot of complexity in this, because it's a complex thing and we want to enable very complex behavior, but for the most part it will be hidden in the ColorTransferFunction, which will abstract this all away.
Absorption works slightly differently now. Because we do back-to-front integration, I believe we do not have to store the accumulated opacity -- so while right now the volume renderer has 6xNxM arrays, I think it should just be 3. Additionally, we're presented with the radiative transfer equation:
dI/ds = -alpha * I_0 + j
Here j would be our emission at a given sample, I_0 is the current value of the image plane, and ds is the distance between samples. Since we're doing back-to-front, I believe we are directly solving this, and so our alpha fed in directly corresponds to the absorption. However, because ds is so low, careful calibration between alpha and emissivity is necessary. Alternately, converting everything back to cgs is probably not difficult... (approximate) Scattering should be easy to put in, it just hasn't been yet.
However, that's neither here nor there. I believe that some exciting things can be done with this (Britton and Jeff and I have chatted about line transfer as well as LyA maps) and I think it's rapidly converging on a workable implementation.
I made up some images with it. The first is a sample dataset of John's with an HII region:
http://yt.enzotools.org/attachment/wiki/Screenshots/hiiregion_rgb.png
The second is one of my protostars from my thesis (I had to aggressively clip the image here) just at around 1.5e4K. This image is 3AU on a side:
http://yt.enzotools.org/attachment/wiki/Screenshots/protostar_rgb.png
And, just for fun, I got the transfer function widget to steal the camera position from the VTK interface, so I made up an image of the two playing nicely together:
http://yt.enzotools.org/attachment/wiki/Screenshots/vtk_tf_vr.png
Anyway, that's my status update. All of the MultiVariate stuff went in the "vr-multivariate" branch, so feel free to take a look. (You'll probably have to re-cython.) An example script is here:
http://paste.enzotools.org/show/367/
I'll shoot another, much shorter, email to the list when it's working and merged back into the main hg branch. We'll then have a parallel software volume renderer that is fully multi-channel, multi-variate, and comes with black body transfer functions. All of this is only achievable thanks to the development community we have, and our team efforts. So, thank you all for your hard work and your enthusiasm.
-Matt