Nion Swift User’s Guide (16.10.0)

Nion Swift is open source scientific image processing software integrating hardware control, data acquisition, visualization, processing, and analysis using Python. Nion Swift is easily extended using Python. It runs on Windows, Linux, and macOS.

The Swift Workspace

Key Features

  • Data handling of 1D plots, 2D images, 1D and 2D collections of plots and images, and sequences.

  • Live computations that can be chained during acquisition or live parameter adjustment.

  • Open source, cross platform (macOS, Windows, Linux), and Python based scientific data processing.

  • Low latency, high throughput data acquisition platform.

Who Uses Nion Swift?

Nion Swift has been primarily developed for the operation of Nion electron microscopes and also as an offline tool to visualize, process, and analyze scientific data from electron microscopes, other instrumentation, and other scientific fields.

Nion Swift is open source. You can find the source code on GitHub Nion Swift.


The quickest and easiest way to get Nion Swift running is to download a pre-built version that includes everything needed to run the basic application.

To install Nion Swift from Python source code, you will need to install a Python environment and then install Python packages required for Nion Swift.

For specific installation details and download links, follow link below. Instructions to install additional packages to extend Nion Swift are also covered.

Release Notes

To see a list of changes for each version of Nion Swift, follow the link below.

Using Nion Swift

Once you have installed Nion Swift and successfully launched it, you can read the introduction to understand the basic ideas, follow through the basic tutorial to try out key concepts, and consult the user guide for more advanced use, Python scripting, and reference.

Python Scripting

Nion Swift offers a great deal of functionality using the user interface. However, sometimes you will want to go beyond its intrinsic capabilities. Fortunately it is easy to extend the functionality using Python.

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