  Given a countable set of points in a continuous space, Voronoi tessellation
is an intuitive way of partitioning the space according to the distance to the
individual points. As a powerful approach to obtain structural information, it
has a long history and widespread applications in diverse disciplines, from
astronomy to urban planning. For particulate systems in real life, such as a
pile of sand or a crowd of pedestrians, the realization of Voronoi tessellation
needs to be modified to accommodate the fact that the particles cannot be
simply treated as points. Here, we elucidate the use of Set Voronoi
tessellation (i.e., considering for a non-spherical particle a set of points on
its surface) to extract meaningful local information in a quasi-two-dimensional
system of granular rods. In addition, we illustrate how it can be applied to
arbitrarily shaped particles such as an assembly of honey bees or pedestrians
for obtaining structural information. Details on the implementation of this
algorithm with the strategy of balancing computational cost and accuracy are
discussed. Furthermore, we provide our python code as open source in order to
facilitate Set Voronoi calculations in two dimensions for arbitrarily shaped
objects.
