Introduction

Overview

playNano is an open-source Python toolkit for time-aware processing and analysis of atomic force microscopy (AFM) data. It is designed to handle complete AFM time series—such as high-speed AFM (HS-AFM) videos—as unified datasets rather than isolated frames. The software provides a reproducible, provenance-tracked workflow for preparing, visualising, and analysing AFM data within a modular Python environment.

Motivation and Design

Conventional AFM image-processing tools often treat each frame independently, making it difficult to perform consistent operations or analyses across time-series data. playNano was developed to address this gap by introducing a pipeline-based, time-aware, and FAIR-compliant approach built using Python tools and libraries.

The design philosophy is to keep the architecture transparent and simple enough for experimental scientists to use and extend, while ensuring every processing and analysis step is fully recorded for reproducibility. Each pipeline—whether for image processing or analysis—stores its operations and parameters directly in the dataset’s provenance, allowing complete reconstruction of results at any stage.

Core Components

playNano is organised around four main stages:

  1. Loading - Imports a sequence of AFM frames into an AFMImageStack, preserving timestamps and metadata. The following AFM file formats are supported: .jpk, .asd, .h5-jpk, and .spm.

  2. Processing - Applies flattening, filtering, and frame-alignment operations to prepare data for analysis and export. Pipelines are defined as ordered lists of steps that run serially on the stack. See more: Processing.

  3. Analysis - Executes configurable pipelines of analysis modules, such as particle detection, clustering, and tracking. Modules can be combined to create custom workflows. See more: Analysis.

  4. Export - Saves processed data and analysis results in multiple open formats for reuse, sharing, and publication. See more: Exporting Data.

Next Steps

→ Continue to Quickstart to load your first AFM file and run a basic processing workflow.