» Articles » PMID: 38308146

Thebeat: A Python Package for Working with Rhythms and Other Temporal Sequences

Overview
Publisher Springer
Specialty Social Sciences
Date 2024 Feb 2
PMID 38308146
Authors
Affiliations
Soon will be listed here.
Abstract

thebeat is a Python package for working with temporal sequences and rhythms in the behavioral and cognitive sciences, as well as in bioacoustics. It provides functionality for creating experimental stimuli, and for visualizing and analyzing temporal data. Sequences, sounds, and experimental trials can be generated using single lines of code. thebeat contains functions for calculating common rhythmic measures, such as interval ratios, and for producing plots, such as circular histograms. thebeat saves researchers time when creating experiments, and provides the first steps in collecting widely accepted methods for use in timing research. thebeat is an open-source, on-going, and collaborative project, and can be extended for use in specialized subfields. thebeat integrates easily with the existing Python ecosystem, allowing one to combine our tested code with custom-made scripts. The package was specifically designed to be useful for both skilled and novice programmers. thebeat provides a foundation for working with temporal sequences onto which additional functionality can be built. This combination of specificity and plasticity should facilitate research in multiple research contexts and fields of study.

Citing Articles

PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments.

Jadoul Y, Duengen D, Ravignani A BMC Res Notes. 2023; 16(1):135.

PMID: 37403146 PMC: 10320995. DOI: 10.1186/s13104-023-06396-x.

References
1.
Bouwer F, Werner C, Knetemann M, Honing H . Disentangling beat perception from sequential learning and examining the influence of attention and musical abilities on ERP responses to rhythm. Neuropsychologia. 2016; 85:80-90. DOI: 10.1016/j.neuropsychologia.2016.02.018. View

2.
Burchardt L, Briefer E, Knornschild M . Novel ideas to further expand the applicability of rhythm analysis. Ecol Evol. 2022; 11(24):18229-18237. PMC: 8717299. DOI: 10.1002/ece3.8417. View

3.
Burchardt L, Picciulin M, Parmentier E, Bolgan M . A primer on rhythm quantification for fish sounds: a Mediterranean case study. R Soc Open Sci. 2021; 8(9):210494. PMC: 8456132. DOI: 10.1098/rsos.210494. View

4.
Celma-Miralles A, Toro J . Discrimination of temporal regularity in rats (Rattus norvegicus) and humans (Homo sapiens). J Comp Psychol. 2019; 134(1):3-10. DOI: 10.1037/com0000202. View

5.
Gonzalez-Hoelling S, Reig-Garcia G, Bertran-Noguer C, Suner-Soler R . The Effect of Music-Based Rhythmic Auditory Stimulation on Balance and Functional Outcomes after Stroke. Healthcare (Basel). 2022; 10(5). PMC: 9140539. DOI: 10.3390/healthcare10050899. View