using PyCall
np = pyimport("numpy")
res = np.fft.fft(rand(ComplexF64, 10))
The interop with matlab and cpp is similarly painless.
It seems insane to me that it's this easy in Julia, yet most other languages are completely incompatible without transpiling one into the other.
This is not to say macros aren't awesome.
I've arguably got a lot more experience doing scientific programming in c++ and FORTAN, than I do in python. Yet for quick prototyping where performance is almost never an issue, I still elect to use python because it lets me wrap my head around the problem faster. I spend less time fixing stupid errors and thinking about coding and more problem thinking about the problem I really need to solve.
Julia might be even better, I've heard good things, I've just never used it though. In the scientific computing and data science world that I deal with, I'd say at least 95% of the time there's never a need to jump from a hacked together prototype to a more production level product.
I ended up making a custom system image with the libraries I always use (Plots, etc) which does help on that front.
That's a fairly minor gripe, mind you. It's still great.
You suggest alternative B?
Pyro can scale to pretty big datasets thanks to custom inference via guides. So you can do inference on pretty sophisticated models such as Deep Markov.
Whereas Turing is mostly for small or medium sized models. For learning purposes, for smaller models or for non-parametrics Turing might be a much better choice, though.
Within Julia, ForneyLab.jl is quite cool for non-deep state space models. I also like Turing.jl, but it has different tradeoffs.
In my experience, Pyro (generally variational inference) is more like a Bayesian optimization than full inference, since it will simply miss multimodality or non-Gaussian tails, etc.