pyBOAT

pyBOAT performs continuous-wavelet–based analysis of non-stationary biological time series to quantify rhythmic biological signals and their temporal dynamics.

Key Features:

• Continuous Wavelet Analysis: Employs continuous wavelet analysis to reveal time-dependent features in non-stationary oscillatory data.
• Optimized sinc-filter detrending: Applies optimized sinc-filter detrending as a preprocessing step to remove trends from time series.
• Amplitude Envelope Removal: Removes amplitude envelopes to normalize oscillation amplitudes prior to spectral analysis.
• Time-Frequency Analysis: Performs continuous-wavelet–based time-frequency analysis to quantify rhythmic patterns and their temporal evolution.
• Data Visualization: Generates time-frequency representations for inspection of oscillatory structure and dynamics.

Scientific Applications:

• Live measurements of biological oscillations: Quantifies non-stationary and noisy oscillations from high-resolution live measurement data.
• Mechanistic studies of rhythmic phenomena: Supports analysis aimed at discovering functional mechanisms underlying biological rhythms.
• Synthetic biological oscillator design: Assists evaluation and design efforts for synthetic biological oscillators by characterizing their temporal behavior.

Methodology:

Computational methods explicitly include continuous wavelet analysis, optimized sinc-filter detrending, amplitude envelope removal, and continuous-wavelet–based time-frequency analysis.