(2.5, 10), (5, 10), (10, 5), (10, 2.5), (5, 0), (2.5, 0), (0, 2.5), (0, 5)
])
# Initialize the algorithm
v = Voronoi(polygon)
# Attach a Voronoi observer that visualizes the Voronoi diagram every step
v.attach_observer(
VoronoiObserver(
# Settings to pass into the visualizer's plot_all() method.
# - By default, the observer uses a set of minimalistic presets that are useful for visualizing during
# construction, clipping and the final result. Have a look at Presets.construction, Presets.clipping and
# Presets.final.
# - These settings below will update the default presets used by the observer. For example, by default,
# the arc_labels are not shown, but below we can enable the arc labels. Other parameters can be found in
# the visualizer's plot_all() method.
settings=dict(arc_labels=True, site_labels=True),
# Callback that saves the figure every step
# If no callback is provided, it will simply display the figure in a matplotlib window
callback=lambda observer, figure: figure.savefig(f"output/voronoi/{observer.n_messages:02d}.png")
)
)
# Attach observer that visualizes the tree every step. This is a binary tree data structure that keeps track of where
# the arcs and the breakpoints between the arcs are going.
v.attach_observer(
TreeObserver(
# Callback that saves the figure every step
# If no callback is provided, it will render the figure in a window
# ]
# Initialize the algorithm
v = Voronoi(triangle)
# Optional: attach observer that visualizes Voronoi diagram every step
v.attach_observer(
VoronoiObserver(
# Settings to put into the visualizer
settings=dict(polygon=True,
edges=True,
vertices=True,
sites=True,
outgoing_edges=False,
border_to_site=False,
scale=1,
edge_labels=False,
site_labels=False,
triangles=False,
arcs=False),
# Callback that saves the figure every step
callback=lambda observer, figure: figure.savefig(
f"output/{observer.n_messages:02d}.png")))
# Make the output directory
if not os.path.exists("output"):
os.mkdir("output")
# Start the procedure