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
- Year: 2025
- Location: Canberra, Australia
- Track: Paper
- Pages: 63–72
- Article Number: 9
- DOI: 10.5281/zenodo.15698790 (Link to paper and supplementary files)
- PDF Link
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} }