See https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5022S/abstract Stellar streams are sensitive probes of the Galactic potential. The likelihood of a model given stream data can only be assessed using simulations. However, comparison to simulation is challenging in a noisy 6D phase space in which even the stream paths are hard to quantify. Here we present a novel application of Self-Organizing Maps and first-order Kalman filters to reconstruct the stream path, propagating measurement errors and data sparsity into the stream path uncertainty. The technique is Galactic- model independent, non-parametric, and works on phase-wrapped streams. We can uniformly analyze and compare data with simulation.