Dataguzzler-Python Dataguzzler-Python is a prototype toolkit for managing spatial data for nondestructive evaluation (NDE) modeling, data analysis, etc. It can be downloaded from https://github.com/isuthermography/dataguzzler-python. Dataguzzler-Python is a tool to facilitate data acquisition, leveraging Python for scripting and interaction. A Dataguzzler-Python data acquisition system consists of modules that can control and/or capture data from your measurement hardware, and often additional higher-level modules that integrate functionality provided by the hardware into some sort of virtual instrument. Dataguzzler-Python helps you leverage existing Python knowledge and existing Python libraries to build these modules and systems. It provides a configuration system for constructing your acquisition application. It provides both an interactive interface and a scripting environment to support manual, ad-hoc, and automated processes. It significantly simplifies concurrency management by managing per-module locks and specifying locking rules so that module authors can focus on interfacing with hardware. Dataguzzler-Python is designed to integrate with the SpatialNDE2 library that provides a sophisticated transaction-oriented recording database, math engine, and viewer. The combination makes it easy to build sophisticated data acquisition applications by assembling modules for the individual hardware components. It provides a parallel framework where multiple acquisition tasks can proceed concurrently while maintaining consistent views of the experiment through the versioned and transaction-oriented recording database. The math engine maximizes parallel computation within the versioned recording database and is designed to support dynamic offload to GPU devices when appropriate and available. It is also designed to simplify working with geometric objects (CAD models, etc.) including operations such as raytracing sensor data onto the surface of the CAD model. The viewer provides live interaction and exploration within a consistent view of the most recent recorded data. System requirementsPython (3.8, or greater)Numpy and Matplotlib (recommended)Pint units library (recommended) QT5 (recommended)SpatialNDE2 library (recommended) Installation of prerequisites (except for SpatialNDE2) is usually through a package manager such as Anaconda or Linux OS package managers. See the SpatialNDE2 page and documentation for more information. Documentation User's Guide (html) User's Guide (pdf)