I'm sorry, but this is not good code for teaching anyone about anything to do with physics. It manages to be both verbose, non-idiomatic, slow and wrong. Feynman would not touch this with a ten foot pole.
First ditch all the object orientation and encapsulation and stuff. Your data is a 2xN Numpy array. Your visualization is a scatter plot in Matplotlib. Voila, 80% of the code is gone.
For the position updates, you either use a repulsive potential to approximate the hard spheres and do molecular dynamics, showing how to integrate Newton's second law and the Verlet scheme and ergodicity and the whole shebang. Or you do Monte Carlo for the positional updates and keep the exact hard spheres. You discuss statistical mechanics concepts like ensembles and thermostats and stuff.
Then you produce results like the pair correlation function and compare it with the Carnahan-Starling equation, dig into the really cool stuff. Compute velocity autocorrelation functions, test what happens when you change density and temperature, talk about phase diagrams, etc.
This is actually an amazingly deep subject, yet very accessible and intuitive, that sits on the border between physics and chemistry. Sad to see it treated like this. Would suggest that people have a look at the book by Allen and Tildesley which is much much better. They have both Python and Fortran example code on Github.
It uses python and had a mindblowing moment when I realized how the simulation I wrote connected to the world, making it one of the few I actually managed to finish. As you alluded to, the lecturers definitely hint that simpler code is easier to work with as the later stuff becomes impossibly complex with anything more.
Definitely a personal favorite for me to. Excelled introduction to MCMC methods, lots and lots of python programs and nicely filmed against a green screen.
I agree with your comments, but it's still good to recognise that although his modelling might not be correct or imprecise, the results do show diffusion occurring.
It might not be the realistic simulation of what's going on but gets the intuition across very well nonetheless.
TFA is a decent first cut. It may be inefficient (the author already mentioned and discarded a N^2 approach), but it’s easily understandable. The author also mentioned that he was planning a NumPy version, so maybe some of the topics you addressed will be covered.
First ditch all the object orientation and encapsulation and stuff. Your data is a 2xN Numpy array. Your visualization is a scatter plot in Matplotlib. Voila, 80% of the code is gone.
For the position updates, you either use a repulsive potential to approximate the hard spheres and do molecular dynamics, showing how to integrate Newton's second law and the Verlet scheme and ergodicity and the whole shebang. Or you do Monte Carlo for the positional updates and keep the exact hard spheres. You discuss statistical mechanics concepts like ensembles and thermostats and stuff.
Then you produce results like the pair correlation function and compare it with the Carnahan-Starling equation, dig into the really cool stuff. Compute velocity autocorrelation functions, test what happens when you change density and temperature, talk about phase diagrams, etc.
This is actually an amazingly deep subject, yet very accessible and intuitive, that sits on the border between physics and chemistry. Sad to see it treated like this. Would suggest that people have a look at the book by Allen and Tildesley which is much much better. They have both Python and Fortran example code on Github.