 The study explores the occurrence of repetitive spatiotemporal propagation patterns in various fields, such as climatology, social communication, network science, and neuroscience. In neuroscience, sinfire patterns are defined by perfectly consistent repetitions of global propagation patterns. An algorithm is introduced to determine how closely a given recording of sequences of discrete events resembles a sinfire pattern and which spike trains lead to follow. The algorithm uses two new indicators, spike order and spike train order, to calculate the sinfire indicator value, allowing for leader follower sorting and quantification of temporal relationships. The study demonstrates the effectiveness of the new approach using artificially generated datasets, before applying it to real datasets from neuroscience, giant depolarized potentials in mice slices, and climatology, El Nino C surface temperature recordings. The algorithm is characterized by its conceptual and practical simplicity, low computational cost, flexibility, and universality. This article was authored by Thomas Coyt's Aerosat of Worry, Martin Pofol, and others. We are article.tv, links in the description below.