Software & Open Hardware
Open tools for reproducible engineering research
We develop software and open hardware for instrument control, process automation, computational modeling, image analysis, and research data processing. These tools support the lab’s broader goal of making experimental and computational workflows more transparent, adaptable, and reusable.
Instrument control
Automation and process control
Tools for controlling experimental systems, automating process steps, coordinating measurements, and improving repeatability in custom laboratory workflows.
Examples include pyinfuse, pymov, and pyhotty.
Modeling + mechanics
Computational mechanics tools
Software for mechanics modeling, design exploration, numerical analysis, and interpretation of experimental behavior in engineered structures and interfaces.
Examples include PeriFEATO and cantilever/misalignment analysis tools.
Data + images
Experimental analysis workflows
Tools for image analysis, data processing, creep and nanoindentation analysis, and extracting engineering insight from experimental measurements.
Examples include PyGapeVision and analysis workflows for lab data.
Why open tools?
Research software as scholarly infrastructure
Open tools make research workflows easier to inspect, reproduce, adapt, and extend. In the Nanosystems Lab, software and hardware development are not side products; they are part of how we design experiments, train students, and build more capable engineering systems.
Students who contribute to these projects gain experience in coding, automation, documentation, experimental design, and the practical engineering judgment needed to make tools useful to other researchers.
Explore our public repositories
Visit the Nanosystems Lab GitHub organization to see current and past software, automation, modeling, and lab-infrastructure projects.