id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-103781-bycskjtr	Mönke, Gregor	Optimal time frequency analysis for biological data - pyBOAT	2020-06-04		.txt	text/plain	7386	451	54	With this challenge in mind, we have developed pyBOAT, a Python-based fully automatic stand-alone software that integrates multiple steps of non-stationary oscillatory time series analysis into an easy-to-use graphical user interface. Our approach integrates data-visualization, optimized sinc-filter detrending, amplitude envelope removal and a subsequent continuous-wavelet based time-frequency analysis. Computational methods that enable analysis of periods, amplitudes and phases of rhythmic time series data have been essential to unravel function and design principles of biological clocks (Lauschke et al. This allows to use a straightforward numerical method to estimate a lter response | w(ω)| 2 , i.e. applying the smoothing operation to simulated white noise and time averaging the Wavelet spectra. Continuous wavelet analysis allows to reveal non-stationary period, amplitude and phase dynamics and to identify multiple frequency components across dierent scales within a single oscillatory signal (Leise [2013] , Leise et al.	./cache/cord-103781-bycskjtr.txt	./txt/cord-103781-bycskjtr.txt