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.

You are right: for every upload there must be a download, so, by definition, the average seed ratio is exactly 1.