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Published in:
2024 | OriginalPaper | Chapter
LDAQ: An Open-Source Python Package for Data Acquisition and Signal Generation
Authors : Tilen Košir, Klemen Zaletelj, Janko Slavič
Published in: Special Topics in Structural Dynamics & Experimental Techniques, Vol. 5
Publisher: Springer Nature Switzerland
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Abstract
In the field of structural dynamics, tasks related to data acquisition and processing are commonplace. Despite the growing popularity of Python as a tool for data manipulation, its application in data acquisition has been either limited or difficult, requiring significant programming effort from individuals. The lack of a comprehensive Python-based system for data collection is an obstacle to the effectiveness and simplicity of data collection and preservation processes. To address this problem, LDAQ, a pure Python-based data acquisition package, was developed.
LDAQ provides a user-friendly platform for data acquisition and signal generation from a variety of sources, including but not limited to National Instruments, serial communication hardware such as Arduino and ESP, and camera devices such as FLIR thermal imaging cameras. It also facilitates easy integration of new data acquisition and signal generation sources that are not currently supported. LDAQ enables live visualization of both measured and processed signals, including FFT and arbitrary functions, and provides further customization capabilities of these real-time graphs. As an open-source package, LDAQ is freely available and open for collaboration on improvements.
LDAQ offers users the opportunity to integrate this package into their data acquisition processes and contribute to its further development. It paves the way to combine different data collection systems, organize data collection processes, and create a foundation for further refinement and personalization. The advantages of LDAQ are its ease of use, its Python compatibility for additional signal processing, and its potential to simplify data collection systems, increasing efficiency and productivity in data collection.