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PyMoDAQ: An open-source Python-based software for modular data acquisition.

¹CEMES-CNRS, Université de Toulouse, 29 rue Jeanne Marvig, 31055 Toulouse, France.

The Review of Scientific Instruments

|July 10, 2021

Summary

PyMoDAQ is an open-source Data Acquisition framework (DAQ-F) offering a flexible, modular, and user-friendly interface for automating diverse experimental setups. It supports live monitoring, data logging, and FAIR data principles, making it a versatile alternative to commercial solutions.

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Area of Science:

Scientific instrumentation
Open-source software development
Data acquisition systems

Background:

Increasing availability of scientific Python packages enables open-source Data Acquisition frameworks (DAQ-Fs).
These DAQ-Fs offer versatile alternatives to custom-made or commercial solutions.
PyMoDAQ is developed to address the need for user-friendly experimental control and automation.

Purpose of the Study:

Introduce PyMoDAQ as a versatile Data Acquisition framework (DAQ-F).
Highlight its modular design, user interface flexibility, and additional functionalities.
Compare PyMoDAQ with existing frameworks based on key criteria to showcase its novelty.

Main Methods:

Development of a modular Python-based Data Acquisition framework (PyMoDAQ).
Implementation of a graphical user interface for experiment control and automation.
Integration of features like live plotting, data saving with metadata, and logging.
Evaluation of PyMoDAQ using seven criteria and comparison with other DAQ-Fs.

Main Results:

PyMoDAQ provides an easy-to-use interface for controlling and automating various experimental setups.
Its modular structure facilitates data acquisition as a function of multiple parameters.
The framework includes functionalities for instrument configuration, live visualization, and data logging.
PyMoDAQ's data saving mechanism is compatible with FAIR data principles.
Comparison highlights PyMoDAQ's novelty and pertinence as a versatile DAQ-F.

Conclusions:

PyMoDAQ offers a flexible and user-friendly solution for experimental data acquisition and automation.
Its modularity and comprehensive features make it adaptable for diverse scientific applications.
PyMoDAQ's compatibility with FAIR data principles enhances data reusability and accessibility.
It represents a significant advancement in open-source DAQ frameworks.