[adamnemecek]

I invite everyone to check out julia. The language is pleasant and gets out of the way. The interop is nuts. To call say numpy fft, you just do

using PyCall

np = pyimport("numpy")

np.fft.fft(rand(ComplexF64, 10))

Thats it. You call it with a julia native array, the result is in a julia native array as well.

Same with cpp

https://github.com/JuliaInterop/Cxx.jl

Or matlab

https://github.com/JuliaInterop/MATLAB.JL

It's legit magic

[fny]

How does Julia handle typing for interop?

[StefanKarpinski]

If I understand your question correctly, the answer is that there are a fixed number of native types supported by Python and NumPy, all of which correspond naturally to Julia types and are converted bidirectionally by PyCall. Julia and NumPy arrays are memory-compatible and Julia knows how to handle arrays with memory allocated by other systems, so conversion back and forth between Julia arrays and NumPy arrays is zero-copy. Other types like Python dicts are proxied in Julia as special types that Julia knows how to work with as dictionaries (user-defined data types are common in Julia), while general Python objects are just proxied transparently and `obj.method` calls are passed through to the embedded Python runtime. You can even define a function object `f` in Python and call it using `f()` syntax in Julia and vice versa. It's all highly transparent and smooth.

[adamnemecek]

What do you mean?