pyFDRT : An open‐source Python tool for Fourier‐transform analysis of the distribution of relaxation times in electrochemical impedance spectra

Abstract Electrochemical impedance spectroscopy (EIS) is widely used to characterize electrochemical systems. However, interpreting overlapping relaxation processes typically requires prior knowledge of the system under investigation. Although distribution of relaxation times (DRT) analysis deconvolutes these processes, results remain sensitive to user‐defined choices including the discretization basis, regularization strength, and peak identification. We present pyFDRT, an open‐source Python package for Fourier‐transform DRT analysis that fully automates these parameter selections. The workflow first estimates the DRT using Fourier‐transform deconvolution without requiring discretization or regularization parameters. Subsequently, this initial estimate is refined using non‐negative least squares with automated Tikhonov regularization. The number of relaxation processes is then determined using analytic ZARC kernels (resistor in parallel with a constant phase element). Benchmark evaluations against existing Python‐based DRT tools demonstrate that pyFDRT consistently recovers the correct number of relaxation processes from theoretical spectra. Additionally, Warburg open behavior arising from diffusion processes can be identified automatically or specified by the user during DRT calculations.