The problem with this theory is that in a medium of refractive index n>1, light does not travel at the same speed in all directions: light travels slightly faster in the direction of the flow of the medium, and slightly slower in the opposite direction. As the earth goes round we would pass from one situation to the other, and notice this slight difference in the speed of light. This is basically the Michelson-Morley experiment, which has been repeated to huge precisions over the last century.
Wayne Kerr electronics (http://www.waynekerrtest.com/) made me lough when I saw them in a physics lab. I guess British English counts as a foreign language too
Hi, I see your point: Python is getting a lot better for scientific use, maybe not so much due to the changes in 3.0 but rather because the community has grown (e.g. Python(x,y), Enthought). There are a few things that make Python what I use most of the time for scientific work: - The language is better thought out i.e. the Matlab tradition of having one function per file is just annoying. - The quality of the old Fortran algorithms which scipy wraps is consistently better than that of Matlab functions e.g. Matlab fitting routines are a mess, I get much more accurate results with scipy. - Compatibility between versions: matlab code from my colleagues always needs some work to run: either because there have been some changes between matlab versions, or they use a function from a toolbox that I don't have, even though there is an equivalent one in standard matlab. Since we changed to Python all is fine. - When you cannot vectorize a small piece of code, scipy offers a few ways in which compiled code can be added transparently: cython, pyrex, f2py and even pycuda. Much easier than
.mex in matlab.
- Python has a large set of very useful libraries for doing scientific work e.g. networkx, vpython ...
- Thanks to Python's large set of other libraries, it is trivial to do things such as parsing complex files, interfacing with lab equipment (pyvisa, ...) interfacing with the windows/linux/mac GUIs, using databases, sending data over the network etc. All these things are really handy in the lab.
- I don't mind paying for software, but the license management is really a problem: It has happened quite a few times, that Matlab has stopped working because something in the license management had changed. Loosing a day of work of a research group is expensive.
- Of course, the fact that students can just install Python for free, and maybe use it in their future non-scientific job is a plus.
agoston.horvath writes "I've written a HOWTO on replacing Mac OS X's built-in encryption (FileVault) with the well-known FUSE-based EncFS. It worked well for me, and most importantly: it is a lot handier than what Apple has put together. This is especially useful if you are using a backup solution like Time Machine. Includes Whys, Why Nots, and step-by-step instructions."
You are right: for every upload there must be a download, so, by definition, the average seed ratio is exactly 1.