Simulations inform all aspects of modern astrophysical research, ranging
in scale from 1D and 2D test problems that can run in seconds on an
astronomer's laptop all the way to large-scale 3D calculations that run
on the largest supercomputers, with a spectrum
of data sizes and shapes filling the landscape between these two
extremes. In this talk I will review the diversity of astrophysics
simulation data formats commonly in use by researchers, providing an
overview of the most common simulation techniques, including
pure N-body dynamics, smoothed particle hydrodynamics (SPH), adaptive
mesh refinement (AMR), spectral methods, and unstructured meshes.
Additionally, I will highlight methods for incorporating physical
phenomena that are important for astrophysics, including
chemistry, magnetic fields, radiative transport, and "subgrid" recipes
for important physics that cannot be directly resolved in a simulation.
In addition to the numerical techniques, I will also discuss the
communities that have developed around these simulation
codes and argue that increasing use and availability of open community
codes has dramatically lowered the barrier to entry for novice
simulators. Extracting scientific results from astrophysical simulation
data requires detailed knowledge of the underlying
data structures and data formats, as well as the semantic meaning of
the data in relation to the physics problem posed by the simulation. As a
solution to this problem, I will present yt, a community-developed
python library for analyzing and visualizing simulation
data. With support for most of the common astrophysics simulation
research data formats, yt endeavors to provide a universal language for
asking physically motivated questions of simulation data, regardless of
the underlying data format. I will highlight the
community of yt contributors and users, showcase scientific results
where yt was used to facilitate the analysis.
Funding
National Science Foundation; Gordon and Betty Moore Foundation