qmsolve VS PythonCompphys

Very nice. I just stumbled upon this repo in my free time that I was going to take a look at https://github.com/quantum-visualizations/qmsolve. Also does visualization of Schrodinger’s eqn

In the visualization, the color hue shows the phase of the wave function of the electron ψ(x,y, t), while the opacity shows the amplitude. The transmittance spectrum, computed by taking the Fourier transform of the incident and transmitted wavefunction, can be found in this plot.

The simulation was done with qmsolve, a python open-source page we made for visualizing and solving the Schrödinger equation.

As mentioned in another comment, here's the link to the source code. For the split-step method I found this resource very helpful. For the Crank-Nicholson method I don't have any free resources to share, except for the wikipedia article. There is another method from this article which is easier to implement than split-step or Crank-Nicholson since it doesn't require taking Fourier transforms or solving a system of equations. You may find the stability conditions to be too limiting when it comes to performance, at least when compared to the other methods.

The simulator used can be found here.

The Schrödinger equation was solved using a Split Fourier method, which is one of the most efficient and accurate methods to solve the time-dependent version. The simulator used is also OC and is posted here.

Also, the script that returns this visualization can be found here.

I have a repository targeted at computational physics with python: https://github.com/aromanro/PythonCompphys

aromanro/PythonCompphys: Some python workbooks with various topics from Computational Physics (github.com)