Yes, if that tool were created, I bet it would be really popular. But it hasn't been. Python+Numpy+Scipy isn't it by virtue of the fact that we have to specify three different software packages, haha.
I've done tiny prototypes that I later turned into a nice little Python+Numpy+Scipy scripts, and the final Python version was much nicer, but writing it was still more like a couple hour project, compared to the real-time process of figuring out what I wanted, done in Octave.
It compares quite favorably. The nice thing is that most of Numpy and Octave functionality is already builtin to Julia (multidimensional matrices are first class as they are in Matlab/Octave). Julia performance is quite good and multiple dispatch is great for composing packages (once you get used to it).
Check out SciML, Julia's answer to scipy (https://sciml.ai/) and Flux Julia's answer to PyTorch/Tensorflow (https://fluxml.ai/). Are these projects as mature as their Python counterparts? To be honest, no they're not, but they are making pretty rapid progress.
I haven't tried it out. It seems really interesting, though. A guy in my group loved it, but he graduated. I haven't yet so I guess we can put at least one point on the board for Julia!
