OrangeC VS casadi

No one here speaks of the Orange C++ toolchain 😿 https://github.com/LADSoft/OrangeC

Have you heard of the OrangeC compiler project? https://github.com/LADSoft/OrangeC/blob/master/doc/occil.md

If you know how a C++ lambda works under the hood, you'd understand that C++ lambdas create classes in order to ingest the capture targets then work on it, the code in the Orange C compiler for example does the following:

There's also orange

Pelles C and OrangeC, I don't know about the former, but the latter does it via CreateThread, source.

If you want a context as a why the original question, here's a big issue on why it's relevant at all to anyone who isn't developing libcxx.

Interface for several solvers and integrators.

Howdy! Collocation methods can be... tricky. For NMPC control of vehicles, success has been had using direct multiple shooting. Also easier to implement and more intuitive. In fact, this example from the GH is pretty instructive: https://github.com/casadi/casadi/blob/master/docs/examples/python/race_car.py

JuMP.jl (Julia) or casADi (Python) are good choices.

Python is not mature in this area. If you ask Google what Simulink for Python is, you get responses that point to dead libraries that were never feature complete and slow. The absolute closest is CASADI which is nice for some things but doesn't even have a true causal modeling interface and is mostly abandoned by the developers (they put a patch in every now and then, but just look at the commit graph), and it's slow compared to the Julia tools, so much so that PyBAMM is interfacing with ModelingToolkit.jl in Julia for a performance boost. Python is not the place to be for causal/acausal modeling or controls. Anyone who is saying "Python is mature" here is saying it in the abstract and not in the context of your actual question. Yes, Python has web development frameworks. No it does not have good libraries for tons of areas (control, acausal modeling, pharmacometrics, etc.).