Excellent comment! I've spent countless hours on trying to get my python environment to just the right state so I can run code associated with scientific papers

Excellent comment! I've spent countless hours on trying to get my python environment to just the right state so I can run code associated with scientific papers, and come to the conclusion that it's just a big, unmaintainable mess. There are, of course, many examples of well written python in the scientific world, but there are many times when I simply haven't been able to get it into a running state. Julia is great in this regard, but it seems that over time, any language with a centralized library repository will become a graveyard for unmaintained legacy code, and this is bad for long term scientific use. The downsides of rolling your own mathematical functions is that it's very time consuming, and there's always a chance you'll get it mostly right. These are the reasons that python, with numpy, scipy, and the huge quantity of other libraries available tend to draw scientific use.