acidflask

Ah here we go. The spectrum for Rhodamine B is noisy and it's throwing the normalization heuristic off. I've added a moving average filter and zeroed out some small noise in the >600 nm range and the colors make more sense now.

There's something odd about how my normalization heuristic behaves with this particular spectrum. I haven't yet looked into it.

Julia dev and computational scientist here. (have nothing to do with OP)

The term "scientific computing" is evolving very rapidly and I thought a recent HN post

https://news.ycombinator.com/item?id=7692200

did a nice job of describing the differences between two major camps that are starting to take shape. To paraphrase and stereotype: one is steeped in MPI and Fortran/C/C++; the other prefers MapReduce.

Julia isn't going to trounce expert MPI implementations designed to squeeze every last flop from their computations, but we do hope that Julia will be a tool that can both interoperate nicely with existing HPC codes and yet be useful to write new code in.

Plug: I'm organizing an upcoming workshop at SC'14 designed to address scalability issues in high level dynamic languages, including (but not limited to) Julia, Python and R, and hope that anyone who's interested in this emerging area of nontraditional HPC to participate.

http://jiahao.github.io/hptcdl-sc14

It depends on what kind of user you are. If you are comfortable with the risks and rewards of being an early adopter using bleeding-edge tools, then by all means try Julia. If you need the stability and functionality of a mature platform, then you may not want to learn Julia as your first tool.