This repository contains a Python implementation of a compressed sensing algorithm to reconstruct comprehensive grid maps from sparse samples, specifically for data from Scanning Tunneling Microscopy (STM). The primary aim is to exploit the sparse nature of STM samples to facilitate more efficient reconstructions.
- Python 3.x
- stmpy
- h5py
- scipy
- spgl1
- matplotlib
For any further queries or feedback, please contact mjohnsonnoya@college.harvard.edu.
This implementation was developed as a part of Richard Liu, Jenny Hoffman and Marcos Johnson-Noya's research work. The primary focus was on the application of compressed sensing techniques in the realm of Scanning Tunneling Microscopy (STM).