pybela: a Python library to interface scientific and physical computing

Teresa Pelinski, Giulio Moro, and Andrew McPherson

Proceedings of the International Conference on New Interfaces for Musical Expression

Abstract

Workflows to obtain, examine and prototype with sensor data often involve a back and forth between environments, platforms and programming languages. Usually, sensors are connected to physical computing platforms, and solutions to transmit data to the computer often rely on low-bandwidth communicating channels. It is not obvious how to interface physical computing platforms with data science environments, which also operate under distinct constraints and programming styles. We introduce pybela, a Python library that facilitates real-time, high-bandwidth, bidirectional data streaming between the Bela embedded computing platform and Python, bridging the gap between physical computing environments and data-driven workflows. In this paper, we outline its design, implementation and applications, including deep learning examples.

Citation

Teresa Pelinski, Giulio Moro, and Andrew McPherson. 2025. pybela: a Python library to interface scientific and physical computing. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.15698790 [PDF]

BibTeX Entry

@article{nime2025_9,
 abstract = {Workflows to obtain, examine and prototype with sensor data often involve a back and forth between environments, platforms and programming languages. Usually, sensors are connected to physical computing platforms, and solutions to transmit data to the computer often rely on low-bandwidth communicating channels. It is not obvious how to interface physical computing platforms with data science environments, which also operate under distinct constraints and programming styles. We introduce pybela, a Python library that facilitates real-time, high-bandwidth, bidirectional data streaming between the Bela embedded computing platform and Python, bridging the gap between physical computing environments and data-driven workflows. In this paper, we outline its design, implementation and applications, including deep learning examples.},
 address = {Canberra, Australia},
 articleno = {9},
 author = {Teresa Pelinski and Giulio Moro and Andrew McPherson},
 booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
 doi = {10.5281/zenodo.15698790},
 editor = {Doga Cavdir and Florent Berthaut},
 issn = {2220-4806},
 month = {June},
 numpages = {10},
 pages = {63--72},
 title = {pybela: a Python library to interface scientific and physical computing},
 track = {Paper},
 url = {http://nime.org/proceedings/2025/nime2025_9.pdf},
 year = {2025}
}