  Stellar streams are sensitive probes of the Galactic potential. The
likelihood of a stream model given stream data is often assessed using
simulations. However, comparing to simulations is challenging when even the
stream paths can be hard to quantify. Here we present a novel application of
Self-Organizing Maps and first-order Kalman Filters to reconstruct a stream's
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. With this technique, we can uniformly analyze
and compare data with simulations, enabling both comparison of simulation
techniques and ensemble analysis with stream tracks of many stellar streams.
Our method is implemented in the public Python package TrackStream, available
at https://github.com/nstarman/trackstream.
