This repository contains python code for the purposes of TSC (topological superconductivity) simulations. Essentially, it is the "translated" version of an older FORTRAN codebase into Python.
While FORTRAN is an excellent programming language for scientific computing due to its speed, Python's numerous libraries can help optimize the code to such an extent that it surpasses FORTRAN's amazing benchmarks. This is exactly what we did here: we used NumPy to vectorize everything in the FORTRAN codebase (vectorized operations essentially correspond to optimized C code under the hood). For the operations that could not be vectorized (for example, due to memory limitations), we used the numba.njit module to compile the corresponding functions into C code, bypassing Python's interpreter. Finally, we applied parallelization using joblib to loops that were very time consuming even for the FORTRAN codebase. As a result, these optimizations led to an overall decrease of compute time, even when compared to FORTRAN using ifort with an Intel CPU.
There are directions on how to setup the necessary environment to run the code inside the Documentation notebook manually. The requirements.txt file holds all necessary libraries. To create a new environment called TSC and install the requirements, first run
conda create --name TSC -y
and then activate it using
conda activate TSC
Finally, run the following command to install all dependencies
conda install -c conda-forge --file requirements.txt -y
The main codebase corresponds to the tsc python module. A user can either install the module locally, or simply keep the folder and work at the same or a different directory. The docs folder provides many examples of how to use the code. Especially the Documentation notebook contains a thorough description of all aspects of the codebase, its functions, the Theory behind them, as well as why we made specific choices when writing the code. For topics that are even more specialized (e.g. Parallelization), there are additional notebooks inside docs.
As you may have noticed, almost all experiments are run within .ipynb notebooks, with a config.py file at the same directory and usually with a plotting.py file to handle all plots. We advise you to keep this same structure when running your own experiments or tests.