I have access to some of the fastest computers in the world. I can summon thousands of processors, petabytes of disk storage, and terabytes of memory with a few keystrokes. You'd think with that kind of power, analysis could be automated and done in massive pipelines. But that's not always true. Sometimes things are so subtle that the most efficient method is still to use the human eye.

Today I was pretty ill, with a sore throat and congestion. It was the kind of day for lying on the couch and watching movies. Luckily, I was able to accomplish some work that didn't require too much concentration. First, some context.

One of my current projects is looking at the centers of simulated galaxy clusters, and it is actually surprisingly difficult to find the centers of the clusters. Clusters are complicated places, with clumps of matter falling in, sloshing stuff around, making the core not exactly clear. In order to semi-automate the process, I wrote a script that makes pictures of the clusters (which have been previously identified in a automated fashion), that have density contours superimposed. The density contours are analogous to the lines on a topographical map.

The picture above is an example of the output of the script. The colors indicate gas density, blue to red is low to high. If you look at the full-sized image, you can see that there are two dense clumps numbered 0 and 1. I can't just pick the most dense cluster because that might be a tiny blob of matter falling into the larger cluster; more care is needed. So I need to make the decision with my adaptable brain. The script spits out the image, and then I have to input which clump I think is the most central clump, and a record is made which I'll use for the next step in my chain of analysis. I spent most of today looking at these pictures and entering numbers. Yay for tax payers!

This is very similar to the Galaxy Zoo project that aimed to identify the morphology of actual galaxies, but my effort is on a much smaller and simpler scale.

So - which clump do you think is the most central above?

Above is a plot showing how many hours I have logged on my Garmin GPS devices since I got my first one in April 2005. This is mostly cycling, but also walking, running and some hiking. However, this is not a true measure of my physical activity over this period because it is missing data. Up to late 2007, I was active on the velodrome, and I didn't bother using my GPS there. In the last year, I've tried to walk to school more often, and for the most part I haven't used my Garmin on those walks. But it does capture the majority of my physical activity, which is road and mountain biking, where I always use a GPS.

In green I've over-plotted a linear fit to the data. I have chosen a linear fit as it shows the overall trend in the clearest way possible. For some reason, June 2006 shows zero hours, which I believe to be wrong. I can't believe I did nothing at all during any summer month in 2006. Therefore, for purposes of the fit, I have given that month the average hours of the four bracketing months.

Even knowing that lots of data is missing from both the front- and back-ends of the figure, it is still clear what the overall trend was. Down - down significantly from nearly 30 hours a month five years ago to just above 10. There of course is a great variability from month to month, but the fit shows clearly the overall trend of what trying to finish graduate school does to physical activity.

I find the lack of patterns interesting. Some years, I really struggled to stay active during the late fall, which makes a certain amount of sense when weather and schoolwork is factored in. However, other years it apparently didn't affect me at all. The bounce in hours since October 2010 makes sense because that's when I graduated, but the two months in 2010 with 25+ hours is confusing. I can't remember how I achieved that!

Nerd time: I made the figure in Python using matplotlib and numpy. Excel is for suckers!

It's show-and-tell time! Below are two movies I made using the volume rendering tools of yt. I've been using yt for a few years to analyze and visualize the cosmological simulations I make with Enzo, and only recently have I had time to begin to play with the new volume rendering stuff.

The first movie is a slow rotation around the entire volume of a simulation at a contemporary epoch, which means that this image is produced from the state of simulation at its end, 13 billion years after it started. The colors correspond to the density of matter in the volume, from dark blue to white as density increases. The simulation is a periodic cube with dimensions 20 Mpc/h on a side. In comparison, the diameter of our galaxy is somewhere around one thousand times smaller. This means that the whitest areas correspond to clusters of galaxies, and our galaxy would be just a small part of one of the white blobs. Be sure to watch the movie full screen!

Below shows the time evolution of the simulation from beginning to end. This is a thin slab of the center of the simulation (10% thickness) viewed from a corner of the cube. Notice that early on the matter is very clumpy everywhere, but rapidly forms dense knots connected by thin filaments. This is how the real universe looks! After about the half-way point of the movie you'll notice that not much happens. Again, this is how the real universe looks! Much of the large-scale evolution of the universe was finished about 7 billion years ago. This movie uses comoving coordinates, that compensate for the expansion of the universe. If I were to use proper coordinates, which are the kind we use every day to measure normal things with rulers, the movie would show the simulation starting very small and then blowing up. Again, use the full screen option for the best image.

Below is a little animation that I made yesterday that shows what I have been doing lately. The green dots correspond to the positions of dark matter particles, while the little yellow squares are the locations of dark matter haloes that I'm interested in. The white lines are the edges of the simulation volume, and you can see the axes triad in the bottom-left corner. To give you an idea of the scale of what's depicted here, each edge of the cube is 50 Mpc, or 163 million light years. Our entire galaxy is only about 20 kpc or 65,000 light years across.

In the first part of the movie, the cube is rotated about its center. Next, while looking along the z-axis, the volume of the cube plotted is reduced until only 1/10th of the cube is shown. Then, this 1/10th thickness is scanned through the entire cube, and then the volume plotted is replenished back to full.

The things to notice are how the dark matter form areas of high density, which are connected by filaments. Between these are areas of relatively few particles, which are called voids. This is how our universe really looks, with huge collections of galaxies clustered together, separated by huge expanses of nearly empty space. I should point out that there are many more galaxy haloes in this box besides the yellow boxes.

What I was looking for was one of those yellow boxes which is fairly isolated from areas of high density dark matter. I picked one, and now I am doing this same simulation again, but with higher resolution boxes centered on the area of interest. The simulations I'm doing right now are fairly cheap (in computer time currency). I want to be sure that when I run big, time-intensive simulations in the future that I've picked a good area to focus my attention on.

I did this visualization with Visit, a stereo, 4D visualization tool out of the Livermore National Lab. The "stereo" means that it can create two images of the same data that are slightly offset, which create a 3D effect if viewed correctly. The fourth dimension is for time, as it can handle time-ordered data sets. I then used Visit's Python scripting features to output 800 individual PNGs, which I then stitched together (exactly like my time lapse movies) to make this movie.

Check out the panorama I made using my cell phone camera. I would have though the distortion would have been just awful, due to the pinhole lens, but it's not too bad. You'll notice that there are some strange pinkish hues in the sky. Is MCAS Miramar releasing pink dye into the sky for this weekend's air show? My more logical guesses are: A.) dirt on the lens; II.) something funky with the CCD; or 3.)something funky with the JPEG compression algorithm of the cell phone. A.) is likely always the case, but I suspect that II.) and 3.) are also quite likely.

Above is a part of what I've been working on lately. It's a small part of the galaxy family tree that I've derived from a large simulation. In the simulation there are several ingredients that are thrown in the "box." Relevant to this are the dark matter particles, which coalesce into consituents of galaxies. The dark matter particles have unique id numbers. Using a some code I didn't write, I process the simulation data and make a list of galaxies along with the particles in each galaxy. Then, using some code I did write, I track particles and galaxies over a number of time steps, which builds a relational mapping. Then, I use Graphviz to make a nice tree, as you see above.

Inside each box are either three or four data values. The top grid shows what percentage of the particles in that group came from no group, the middle grid shows both the number of dark matter particles that are in the group and the position of the group, while the bottom grid shows the percentage of the particles that go to no group. The simulation takes place inside of a 3D box with length 1 on a side and periodic boundaries (which means the distance between 0.9 and 0.1 is 0.2, not 0.8). The colors of the box correspond to its ranking in size, red is the largest, green the smallest. The numbers next to the arrows are what percentage of the parent group goes to the child group.

The goal of this is to get an idea of how the galaxies form over the course of the simulation. Of course the simulation tries to mirror reality, so this family tree may be worth something.

Do you want to help me get my PhD faster? If so, go on over to this website and buy me one Zenview six screen multidisplay. I'd also like you to buy me a computer capable of running all the monitors.

It's only your money. We're talking about my education here!

For the better part of half a year now I've been working slowly on a project with Mike Norman, my advisor. I'm basically implementing a test of the efficiency & accuracy of the cosmological simulation code his group uses. I do this by sticking a ring of gas into the simulator and watching the gas spread out. I compare the computer simulation to how it should go if all things were perfect (which the simulation is not). The two plots below are a culmination of my accelerating efforts.

Above is a 3D plot of the gas density. The two horizontal coordinates are the x-y position, while the vertical gives the density at that point. I'm doing my simulation in two dimensions right now, eventually I'll go to three.

The white line above (hidden by the red line) is a sideways slice of the density, starting at the center going out. The red line is a best curve fit of the white line. The curve fit is very good because the white line was generated by the same function I'm using for the best fit, but the picture shows my fitting technique works, and that's what matters.

I hope to make further, faster progress! I'll post things here as I go along.