playNano Documentation¶

Welcome to playNano, a Python toolkit for loading, processing, analysing, and exporting high-speed AFM (HS-AFM) time-series data.

playNano provides a reproducible, provenance-aware workflow for handling AFM videos from raw instrument files to flattened, filtered, and analysed outputs. The toolkit includes a command-line interface (CLI), an interactive GUI for viewing data, and a modular analysis system that supports both built-in and custom extensions.

Welcome to playNano - a Python toolkit for loading, processing, analysing and exporting high-speed AFM (HS-AFM) time-series data (.h5-jpk, .jpk, .spm, .asd). This documentation covers installation, command-line usage, the PySide6 GUI, processing filters, data export, analysis pipelines, and the API reference.

Quick links¶

Introduction - overview of playNano’s motivation, design, and core workflow
Installation - how to install playNano (pip / conda)
Quickstart - 1-minute example: open a file, apply a filter, export GIF
Command Line Interface (CLI) - full command-line reference and examples
GUI: Interactive Playback - GUI walkthrough, keyboard shortcuts and export workflow
Processing - filters, masks and pipeline behaviour
- Processing Operations Reference - reference of all built-in processing operations
Exporting Data - exporting data and GIFs from CLI, GUI, or programmatically
Analysis - running analysis modules and provenance
- Custom Analysis Modules - creating and registering custom analysis modules
What’s New in playNano 0.3.0 - highlights of the latest release
Changelog - release notes and history

Quickstart (example)¶

# show a file in the interactive GUI
playnano play ./test/resources/sample_0.h5-jpk

Note

See Quickstart for step-by-step examples.

User Guide Overview¶

The User Guide is divided into two parts:

Getting started¶

Introduction - overview of playNano’s motivation, design, and core workflow.
Quickstart - a standalone, five-minute overview showing how to load, process, and export AFM data.
Notebooks - interactive Jupyter notebooks demonstrating typical workflows and parameter exploration.

Practical Guides¶

Installation - detailed installation and environment setup.
Command Line Interface (CLI) - running batch processing, exports, and automation from the command line.
GUI: Interactive Playback - exploring AFM stacks interactively and exporting results.
Processing - applying filters, masks, and flattening operations with provenance tracking.
Analysis - running feature detection and tracking pipelines.
Custom Analysis Modules - extending the analysis system with your own modules or research methods.
Exporting Data - saving processed data and analysis results in open formats.