I just looked into Julia (1.1) for scientific use (simulation of very simple dynamical systems) a few days ago. I have to admit that by the end of the day I was surprisingly frustrated. I felt that type annotations were insufficient (one of the reasons to move away from Python); in particular, I didn't find a way to specify statically sized array types as you can do with Eigen, a feature that I find incredibly useful to find mistakes at compile time. Furthermore, just plotting something (using Gadfly) took about 30 seconds the first time after Julia was started and about 20 seconds every consecutive call (on a high-end workstation, mind you).
The next day I just ended up using C++/Eigen with a simple matplotlib binding [1]. The code is nearly indistinguishable from Python/Julia (except for having more verbose types where it makes sense, using "auto" otherwise), and the entire compile+run cycle takes less time for some short runs than it takes Julia to print "Hello World".
That being said, I'm not advocating for people to use C++. I would love to use Julia, and applaud the developers for their hard work and contribution to scientific computing, but as it stands right now, it doesn't seem to be the right tool for me, since I'm relying on fast editing/execution cycles.
[1] https://github.com/lava/matplotlib-cpp
