Writing a reminder on https://pylima.readthedocs.io/en/latest/source/NotesOnFits.html that changing to "fancy parameters" as defined in pyLIMA can support the DE fit considerably. In particular changing s, q, tE and rho to log values. Accordingly, new examples highlighting the convergence rate would be helpful. An output of the fit parameters (as a reminder) could be added at the end of each fit. Similarly, the respective boundaries might not be sufficient for a given run, e.g. because rho is larger than 0.05 or piE is larger 1. A gentle reminder before the fit could help :)
Writing a reminder on https://pylima.readthedocs.io/en/latest/source/NotesOnFits.html that changing to "fancy parameters" as defined in pyLIMA can support the DE fit considerably. In particular changing s, q, tE and rho to log values. Accordingly, new examples highlighting the convergence rate would be helpful. An output of the fit parameters (as a reminder) could be added at the end of each fit. Similarly, the respective boundaries might not be sufficient for a given run, e.g. because rho is larger than 0.05 or piE is larger 1. A gentle reminder before the fit could help :)