DataLab: Bridging Scientific and Industrial Worlds for Advanced Signal and Image Processing
09-26, 11:05–11:35 (Europe/Paris), Louis Armand 2

This talk introduces DataLab, a unique open-source platform for signal and image processing, seamlessly integrating scientific and industrial applications.

The main objective of this talk is to show how DataLab may be used as a complementary tool alongside with Jupyter notebooks or an IDE (e.g., Spyder), and how it can be extended with custom Python scripts or applications.


DataLab offers a practical approach to signal and image processing, tailored for the scientific community. This platform, grounded in Python and leveraging the rich ecosystem of scientific libraries (e.g. NumPy, SciPy, scikit-image, OpenCV, PlotPyStack), supports a range of research activities.

This talk will explore the main use cases of DataLab:
- Processing and visualizing your signals and images (use DataLab as generic processing software)
- Prototyping your processing pipeline (direct communication with your IDE or notebook)
- Debugging your processing application (direct communication with DataLab from your application)
- Enhancing your application with processing features (use DataLab as a library, or as a companion to your application)

Real-World Scientific Applications

This segment will present how DataLab is used in various scientific fields. We will explore examples like spectral data analysis in chemistry, measuring laser beam sizes in physics, or identifying biological structures in medical imaging. These instances will demonstrate DataLab's adaptability and utility in real research scenarios, as a rapid prototyping tool for signal and image processing as well as a customizable platform for data visualization.

Extensibility and Automation in Scientific Research

DataLab's extensible nature allows researchers to tailor it to their specific needs. This part of the talk will illustrate how custom plugins and macros can enhance research capabilities, while its automation features streamline repetitive tasks, improving efficiency in scientific workflows.

Bridging Scientific Research with Industrial Processes

A case study will be presented, illustrating DataLab's application in a scientific setting, where it also interfaces with industrial control systems. This example will showcase DataLab's versatility and its potential to facilitate data processing in cross-disciplinary projects.

Pierre Raybaut is a long-term advocate of Python in a scientific context, renowned as the creator of Spyder, the Scientific Python IDE, and other pivotal projects like Python(x,y) and WinPython. These tools have been instrumental in making Python a leading language for scientific computing.

Pierre's academic journey began with an engineer's degree from the Institut d’Optique Graduate School, specializing in laser physics. He further advanced his expertise by earning a PhD in optics and photonics from Université Paris-Saclay, where he developed software for simulating regenerative amplification in ultra-short pulse lasers.

Professionally, Pierre has held diverse and impactful roles. He served as a research engineer at THALES Avionics, a principal software developer at CEA (the French Alternative Energies and Atomic Energy Commission), and managed the Laser MégaJoule timing and fiducial system project. Eventually, he became the Head of a Research Laboratory at CEA before transitioning to Codra, an industrial software company, where he currently holds the position of Chief Technology Officer (CTO).

Beyond his work on Spyder, Pierre is deeply involved in the open-source software community. He has created tools such as guidata, PlotPy, PlotPyStack and DataLab, and has contributed to numerous other projects.