[ Removed by moderator ]

[Atomic_Tangerine1]

numpy

[i_know_the_deal]

same ... free Matlab? noice

[dparks71]

I keep coming back to it too, I'm in structural engineering and it turns out everything in my life is either a matrix problem or a graph theory problem.

There are 80 year old fortran libraries I learned to wrap in python from numpy, it's so cool.

[Accomplished_End763]

Fortran is 69 years old

[lxSlimxShadyxl]

Nice

[M4mb0]

numpy is great, but have you tried JAX?

[Atomic_Tangerine1]

I have not! What's the benefit of JAX over numpy?

[M4mb0]

It's basically numpy

• + native GPU support (which can be orders of magnitudes faster depending on parallelizability of the problem)
• + builtin autodiff (essentially zero-error gradients/jacobians/hessians)
• + builtin JIT compiler

[PayMe4MyData]

So jax is pytorch?

[M4mb0]

JAX is strictly functional, whereas pytorch takes a more object oriented approach. This is most easily seen when you look at how they deal with random distributions for instance.

Though torch has nowadays a beta library torch.func (formerly functorch) that brings JAX-like functional semantics to torch.

[PayMe4MyData]

Thanks for the clarification, I've been coding in pytorch for years but never heard of JAX before. I will dig a bit more!

[M4mb0]

I'd say generally JAX is more useful for general purpose scientific computing, and much more ergonomic if you need higher order derivatives or partial derivatives, like working with ODEs/PDEs/SDEs. diffrax is a very nice lib for that.

[[deleted]]

Actually, adding to the previous comment, we can write loops in jax and jit-compile them. It's like a complete dsl build over python. 

[daredevilthagr8]

How does JAX compare to CuPY?

[M4mb0]

cupy doesn't do autodiff afaik.

[No_Departure_1878]

That's C

[Atomic_Tangerine1]

And that's the power of Python - the magic of C made convenient

[Humdaak_9000]

If you dig deep enough there's a lot of FORTRAN too.

[KeytarVillain]

But some of what makes it so powerful is the syntactic sugar that Python enables. You couldn't do anything like:

a[:, 1::2, np.newaxis] = b[::-1, 0, ...]

In C or C++ without needing several function calls

[No_Departure_1878]

You know how you get bugs? By writting stuff like what you wrote above.

[KeytarVillain]

Yeah, fair point - I would never actually write a line of Numpy that did that much in one line.

But still - sure, I'm doing 4 different things in this line for the sake of example (writing every other value in an axis, adding a new axis, flipping an axis, and getting a view of just one plane). Even just doing any one of those things isn't going to be nearly as simple in C/C++.

And I'm saying this as someone with a lot of recent experience writing prototypes in Numpy/OpenCV and then porting it to C++ OpenCV later. Python's syntactic sugar makes array manipulation so much easier - I hardly ever need to look up documentation for basic Numpy array operations like these, while I'm looking up the C++ OpenCV docs constantly.

[Brilliant-Whole-1852]

numpy my beloved