  We report on a Python-toolbox for unbiased statistical analysis of
fluorescence intermittency properties of single emitters. Intermittency, i.e.,
step-wise temporal variations in the instantaneous emission intensity and
fluorescence decay rate properties are common to organic fluorophores, II-VI
quantum dots and perovskite quantum dots alike. Unbiased statistical analysis
of intermittency switching time distributions, involved levels and lifetimes is
important to avoid interpretation artefacts. This work provides an
implementation of Bayesian changepoint analysis and level clustering applicable
to time-tagged single-photon detection data of single emitters that can be
applied to real experimental data and as tool to verify the ramifications of
hypothesized mechanistic intermittency models. We provide a detailed Monte
Carlo analysis to illustrate these statistics tools, and to benchmark the
extent to which conclusions can be drawn on the photophysics of highly complex
systems, such as perovskite quantum dots that switch between a plethora of
states instead of just two.
