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DelaunayTriangulation

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This is a package for constructing Delaunay triangulations and Voronoi tessellations of planar point sets. Supports unconstrained and constrained triangulations, weighted triangulations, mesh refinement, triangulation of curve bounded domains, Voronoi tessellations, power diagrams, and clipped and centroidal Voronoi tessellations. To install the package, do

julia>] add DelaunayTriangulation

Many features are available, some of these being:

To ensure that the algorithms are robust, by default we make use of AdaptivePredicates.jl to use adaptive arithmetic for all geometric predicates in this package. This choice can be configured, allowing for the additional choices of ExactPredicates.jl or no adaptive or exact arithmetic at all; see the documentation. Much of the work in this package is derived from the book Delaunay Mesh Generation by Cheng, Dey, and Shewchuk (2013). Please see the documentation for much more information.

Some examples are below (and in the documentation), but if you would also like to see how DelaunayTriangulation.jl is used in other packages, see FiniteVolumeMethod.jl (solving 2D PDEs) and NaturalNeighbours.jl (scattered data interpolation).

If you would like to make an issue or contribute, please see the CONTRIBUTING.md file. For feature requests, please see the discussions tab.

Example Usage

Here is a quick example of some ways the package can be used. As mentioned, see the docs for many examples.

# using Pkg; Pkg.add(["DelaunayTriangulation", "CairoMakie", "StableRNGs"])
using DelaunayTriangulation, CairoMakie, StableRNGs

# Unconstrained 
points = rand(2, 50)
tri1 = triangulate(points) # default predicate kernel is AdaptiveKernel()

# Voronoi example 
vorn2 = voronoi(tri1)

# Clipped Voronoi 
vorn3 = voronoi(tri1, clip=true, predicates = ExactKernel())

# Smoothed Voronoi 
vorn4 = centroidal_smooth(vorn3; predicates = FastKernel()) # or do voronoi(tri1, clip = true, smooth = true)

# Constrained example with refinement 
boundary_points = [(0.0, 0.0), (1.0, 0.0), (1.0, 0.3), (0.5, 0.3),
    (0.3, 0.7), (0.1, 1.0), (0.0, 1.0), (0.0, 0.0)]
boundary_nodes, points = convert_boundary_points_to_indices(boundary_points)
tri5 = triangulate(points; boundary_nodes)
refine!(tri5; max_area=1e-2get_area(tri5))

# Disjoint constrained example with refinement 
boundary_points = [
    [[(0.0, 0.0), (1.0, 0.0), (1.0, 1.0), (0.0, 1.0), (0.0, 0.0)]],
    [[(0.3, 0.3), (0.3, 0.7), (0.7, 0.7), (0.7, 0.3), (0.3, 0.3)]],
    [[(1.2, 0.0), (1.4, 0.0), (1.4, 1.2), (0.0, 1.2), (0.0, 1.1),
        (1.1, 1.1), (1.1, 0.0), (1.2, 0.0)]]
]
boundary_nodes, points = convert_boundary_points_to_indices(boundary_points)
tri6 = triangulate(points; boundary_nodes)
refine!(tri6; max_area=1e-2get_area(tri6))

# Curve-bounded example
using DelaunayTriangulation: EllipticalArc
ellipse = EllipticalArc((1.0, 0.0), (1.0, 0.0), (0.0, 0.0), 1.0, 2.0, 0.0)
tri7 = triangulate(NTuple{2,Float64}[]; boundary_nodes=[ellipse])
refine!(tri7; max_area=1e-2get_area(tri7))

# Disjoint curve-bounded example 
ellipse = EllipticalArc((7.0, 3.5), (7.0, 3.5), (0.0, 3.5), 7.0, 10.0, 0.0)
catmull_cp = [(0.0, 0.0), (-2.0, -1.0), (-4.0, 0.0), (-5.0, 2.0), (-1.0, 4.0), (0.0, 3.0),
    (1.0, 4.0), (5.0, 2.0), (4.0, 0.0), (2.0, -1.0), (0.0, 0.0)]
catmull_spl = CatmullRomSpline(catmull_cp)
circle = CircularArc((0.5, 1.5), (0.5, 1.5), (0.0, 1.0))
circle2 = CircularArc((0.1, 1.5), (0.1, 1.5), (0.0, 1.0), positive=false)
points = [(-4.0, -10.0), (4.0, -10.0), (4.0, -7.0), (-4.0, -7.0)]
square = [1, 2, 3, 4, 1]
boundary_nodes = [[square], [[ellipse]], [[catmull_spl]], [[circle]], [[circle2]]]
tri8 = triangulate(points; boundary_nodes)
refine!(tri8; max_area=1e-2get_area(tri8)) # could also use find_polygon to help define a custom refinement function for each shape

# Weighted triangulation example
rng = StableRNG(1) # You can pass rngs for reproducibility. Alternatively, just use Random.seed!(n)
points = tuple.(rand(rng, 20), rand(rng, 20))
weights = 3randn(rng, 20)
tri9 = triangulate(points; weights, rng)

# Power diagram example 
points = tuple.(rand(rng, 100), rand(rng, 100))
weights = rand(rng, 100)
tri10 = triangulate(points; weights, rng)
vorn10 = voronoi(tri10) # can also use clip/smooth here 

# Clipped Voronoi example with a generic convex polygon
points = 10randn(rng, 2, 100)
weights = rand(rng, 100)
circ = CircularArc((0.0, 5.0), (0.0, 5.0), (0.0, 0.0)) # clip to a circle 
clip_points = [circ(t) for t in LinRange(0, 1, 100)]
clip_vertices = [1:(length(clip_points)-1); 1]
tri11 = triangulate(points; weights, rng)
vorn11 = voronoi(tri11, clip=true, clip_polygon=(clip_points, clip_vertices), rng=rng)

# Plotting 
fig = Figure(fontsize = 42, size = (2800, 2200))
wh = (width = 600, height = 600)
ax = Axis(fig[1, 1]; title = "Unconstrained", wh...);            triplot!(ax, tri1)
ax = Axis(fig[1, 2]; title = "Voronoi", wh...);                  voronoiplot!(ax, vorn2)
ax = Axis(fig[1, 3]; title = "Clipped Voronoi", wh...);          voronoiplot!(ax, vorn3)
ax = Axis(fig[1, 4]; title = "Centroidal Voronoi", wh...);       voronoiplot!(ax, vorn4)
ax = Axis(fig[2, 1]; title = "Constrained", wh...);              triplot!(ax, tri5)
ax = Axis(fig[2, 2]; title = "Disjoint Constrained", wh...);     triplot!(ax, tri6)
ax = Axis(fig[2, 3]; title = "Curve-Bounded", wh...);            triplot!(ax, tri7)
ax = Axis(fig[2, 4]; title = "Disjoint Curve-Bounded", wh...);   triplot!(ax, tri8)
ax = Axis(fig[3, 1]; title = "Weighted", wh...);                 triplot!(ax, tri9)
ax = Axis(fig[3, 2]; title = "Power Diagram", wh...);            voronoiplot!(ax, vorn10)
ax = Axis(fig[3, 3]; title = "Generic Clipped Voronoi", wh...);  voronoiplot!(ax, vorn11)

Similar Packages

This is not the only Delaunay triangulation package available. Some others are: