voronoi

Builtin index · Statistics & ML

result = voronoi(points)

Input: points · Output: result

What it is

Computes the Voronoi diagram of an (N, 2) point matrix points, built on a real Delaunay triangulation.

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`result = voronoi(points)`	`points` matrixnot documented yet	`points` matrixthe input `(N, 2)` matrix, unchanged `cells` —a length-`N` list, one entry per input point, each a list of `(x, y)` vertex coordinates outlining that point's Voronoi cell `bounded` vectora length-`N` vector of 0/1 flags, one per point	Returns a Record with fields `points` (the input `(N, 2)` matrix, unchanged), `cells` (a length-`N` list, one entry per input point, each a list of `(x, y)` vertex coordinates outlining that point's Voronoi cell), and `bounded` (a length-`N` vector of 0/1 flags, one per point).

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

pts = [0, 2, 0, 2, 0, 0, 2, 2] as matrix(4, 2)   # the corners of a square

v = voronoi(pts)
print("bounded: {v.bounded}")      # every corner cell is unbounded

print("cell 0:  {v.cells[0]}")     # the one finite vertex: the centre

output

bounded: [0, 0, 0, 0]
cell 0:  [1, 1]

from statistics-ml

How it works

voronoi(points) — the Voronoi diagram of an (n,2) point set, built on spade's Delaunay triangulation (see this crate's Cargo.toml for why spade, not a hand-rolled Fortune's sweep). See voronoi_diagram's own doc comment for the exact return shape and how unbounded cells are handled.

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.