Quantification of effectiveness of peak-finding methods

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Objective: The primary goal of this issue is to systematically quantify and compare the effectiveness of various peak-finding methods currently in use or proposed for TimeTrace. This comparison aims to evaluate these methods based on accuracy, computational efficiency, and overall reliability in different scenarios.

Background: TimeTrace employs signal processing techniques to accurately detect and analyze the peaks in watch timing data. Recent discussions have highlighted several potential methods, including standard peak detection and its derivatives, autocorrelation, and sub-sample precision methods like parabolic fitting. However, there's a need for a systematic evaluation to determine which method or combination of methods provides the best balance between accuracy and computational efficiency, particularly in a Python environment.