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CircadiPy: An open-source toolkit for analyzing chronobiology time series
João Pedro Carvalho-Moreira1, Leonardo de Oliveira Guarnieri1, Matheus Costa Passos2
1Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas - Universidade Federal de Minas Gerais, Belo Horizonte, Brasil; Centro de Tecnologia e Pesquisa em Magneto Ressonância, Programa de Pós-Graduação em Engenharia Elétrica - Universidade Federal de Minas Gerais, Belo Horizonte, Brasil.
This study introduces CircadiPy, an open-access Python library for analyzing biological rhythms. It provides a flexible toolkit for researchers to extract key parameters of circadian rhythmicity from time-series data.
Area of Science:
- Chronobiology
- Biological Rhythms
- Mammalian Physiology
Background:
- Biological processes, including metabolism, body temperature, activity, and sleep in mammals, exhibit circadian rhythmicity.
- Statistical evaluation of biological rhythmicity involves analyzing time series data to extract parameters like period, amplitude, phase, and variability.
Purpose of the Study:
- To introduce CircadiPy, an open-access Python library for chronobiological data analysis.
- To provide researchers with a flexible, comprehensive, and scalable toolkit for analyzing rhythmicity-influenced processes.
Main Methods:
- Development of the CircadiPy Python library, encapsulating methods for chronobiological analysis and data inspection.
- Utilizing a simplified computational method for time series data analysis within a single Python object.
Main Results:
- CircadiPy effectively guides users in analyzing chronobiological data from diverse sources.
- The toolkit precisely extracts parameters related to circadian rhythmicity.
Conclusions:
- CircadiPy offers a straightforward, cost-free alternative to commercial tools for chronobiological analysis.
- As an open-source tool, CircadiPy fosters community contributions for enhanced functionality and visualization.
