Simple Python-based methods for analysis and drift-correction of STM images

A successful scanning tunnelling microscopy (STM) experiment relies on both delicate sample preparation and measurement, and careful image filtering and analysis to provide clear and solid results. Processing and analysis of STM images may result in a tricky task, due to the complexity and specificity of the probed systems. In this paper, we introduce our recently developed, simple Python-based methods for filtering and analysing STM images, with the aim of providing a semi-quantitative treatment of the input data. Case studies will be presented using images obtained through electrochemical STM. Additionally, we propose a straightforward yet effective universal drift-correction tool for SPM image sequences.