perform_full_analysis

autoeis.core.perform_full_analysis(freq: ndarray[float], Z: ndarray[complex], iters: int = 100, parallel: bool = True, tol_linKK: float = 0.05, tol: float = 0.01, num_warmup: int = 2500, num_samples: int = 1000) → DataFrame

Performs automated EIS analysis by generating plausible ECMs that fit the impedance data, followed by Bayesian inference on components.

Parameters:

  • freq (np.ndarray[float]) – Frequencies corresponding to the impedance data.

  • Z (np.ndarray[complex]) – Impedance data as a complex array.

  • iters (int, optional) – Number of iterations for ECM generation. Default is 100.

  • parallel (bool, optional) – If True, the ECM generation will be done in parallel. Default is True.

  • tol_linKK (float, optional) – Tolerance for acceptable measurements based on linKK residuals.

  • tol (float, optional) – Convergence threshold for the ECM generation. Default is 1e-2.

  • num_warmup (int, optional) – Number of warmup samples for the MCMC. Default is 2500.

  • num_samples (int, optional) – Number of samples for the MCMC. Default is 1000.

Returns:

results – Dataframe containing circuits, parameters, and MCMC results.

Return type:

pd.DataFrame