I’ve been warned that I sometimes veer too far in the direction of toolmaker away from the standard path followed by most scientists. Try as I might, I cannot seem to avoid finding the process of doing science nearly as interesting as the goal of getting that science done. And so, my mind has been orbiting around a problem I suspect is endemic amongst all physicists, if not all scientists. That problem, captured so nicely by this PhD comic is that of filesystem cruft. Science, being at it’s core an experimental art, produces for every successful idea a whole panoply of failed experiments, mistakes, and generally messed-up crap. Being paranoid creatures consumed by our own fears, along with the awareness that serendipity has been a cornerstone of great work, we are loathe to sweep these ill-fated children of the mind into the trash where they (mostly) belong. And so those of us who rely on computers for most of our day-to-day work end up with home directories filled to the brim with old scripts, corrupted data files, a dozen different versions of the same list of values, and other digital detritus. And this situation makes for errors, confusion, thousand yard stare, anal leakage, and other evils too foul to discuss in polite company. Just looking at my /home directory on my workstation at the University, I have more than 100,000 files sitting around, waiting for me to stare at them for a quarter hour trying to remember what they were for.
I just got back from the University of Calgary’s fantastic Rothney Astrophysical Observatory. Since there is a new moon in Calgary, we have had late night open houses yesterday, today, and one tomorrow from 10PM until 2AM. Since I am exhausted, let me show you the awesome picture of the beautiful tendrils of cool dust in the Eagle Nebula we were able to capture using the 16″ Clark-Milone Telescope:
Continuing my series on using python and matplotlib to generate common plots and figures, today I will be discussing how to make histograms, a plot type used to show the frequency across a continuous or discrete variable. Histograms are useful in any case where you need to examine the statistical distribution over a variable in some sample, like the brightness of radio galaxies, or the distance of quasars.
I feel like yesterday’s depressing (but popular!) post painted a bit gloomier picture of the future of astronomy and space science than the reality warrants. Today, I thought I might cheer you up with an inside view of one of the neatest pieces of scientific instrumentation under construction today: a fantastic radio telescope called the Australian Square Kilometer Array (SKA) Pathfinder (ASKAP).
This morning, I watched with tears in my eyes as the last flight of the space shuttle pierced the clouds above Cape Canaveral. After the joy of watching four human beings rise above the atmosphere carried by little more than a few thousand tonnes of metal, plastic, and ceramic safely and in less time than it takes me to drive to work, I started to look back upon a week that has been, if I may put it bluntly, disastrous for NASA, and for the American space effort.
Continuing my series on using matplotlib and python to generate figures, I’d like to get now to the meat of the topic: actually making a figure or two. I’ll be starting with the simplest kind of figure: a line plot, with points plotted on an X-Y Cartesian plane.
I’m sure many of my fellow scientists spend a relatively large chunk of their time making plots, graphs, and figures of one sort or another. There are a plethora of cool tools out there for doing this, from proprietary tools like Mathematica or IDL to free software kits like GNUplot. While GNUplot is useful and handy (and IDL is powerful and expensive), I’m a python guy primarily, so I like my tools to interface well with my existing code, and has a more pythonic interface. For this, I turn to matplotlib, a powerful suite for generating all sorts of plots from python.
This week’s BMC post will be a bit of a short, focused post about phase transitions and a nifty mathematical tool for looking at how they behave. I’m in the foggy head space of 4 hours of sleep right now, so forgive me if this is less of a science primer and more the ramblings of a madman.
So, today’s BMC post will touch on a topic near and dear to all my readers hearts: magnets, how do they work? Well, dear reader, they are not in fact miracles, but the result of the interactions of spin lattices that can be described using the Ising model of magnetism. I shall endeavor to explain a few key features of this model to you, and let you know about a few cool things we can do with it. Continue reading
We’ve discussed in a couple of the past BMC posts the difference between Bosons and Fermions, and the degenerate states that fermions can form. Well, today I’m going to talk about something that you may have heard of, but probably have never had explained to you: Bose-Einstein Condensation. I’ll tell you a bit about what it is, and tell you about a cool application of them: atomtronics.