  Scientific visualization tools are currently not optimized to create
cinematic, production-quality representations of numerical data for the purpose
of science communication. In our pipeline \texttt{Estra}, we outline a
step-by-step process from a raw simulation into a finished render as a way to
teach non-experts in the field of visualization how to achieve
production-quality outputs on their own. We demonstrate feasibility of using
the visual effects software Houdini for cinematic astrophysical data
visualization, informed by machine learning clustering algorithms. To
demonstrate the capabilities of this pipeline, we used a post-impact,
thermally-equilibrated Moon-forming synestia from \cite{Lock18}. Our approach
aims to identify "physically interpretable" clusters, where clusters identified
in an appropriate phase space (e.g. here we use a temperature-entropy
phase-space) correspond to physically meaningful structures within the
simulation data. Clustering results can then be used to highlight these
structures by informing the color-mapping process in a simplified Houdini
software shading network, where dissimilar phase-space clusters are mapped to
different color values for easier visual identification. Cluster information
can also be used in 3D position space, via Houdini's Scene View, to aid in
physical cluster finding, simulation prototyping, and data exploration. Our
clustering-based renders are compared to those created by the Advanced
Visualization Lab (AVL) team for the full dome show "Imagine the Moon" as proof
of concept. With \texttt{Estra}, scientists have a tool to create their own
production-quality, data-driven visualizations.
