Visualization

The cgeom.visualization module provides Matplotlib-based plotting helpers, one per algorithm. They share a polished, minimalist navy/blue palette and sensible defaults, so a single call produces a publication-ready figure.

from cgeom.visualization import (
    plot_convex_hull,
    plot_delaunay,
    plot_intersections,
    plot_min_circle,
    plot_min_circle_random,
    plot_triangulation,
    plot_voronoi,
)

Plotting helpers

Each helper takes the corresponding algorithm object (already constructed) and draws its result.

from cgeom.algorithms import ConvexHull, DelaunayTriangulation, SegmentIntersection

# Convex hull
plot_convex_hull(ConvexHull(points))

# Delaunay triangulation (optionally with circumcircles)
dt = DelaunayTriangulation(points)
dt.triangulate()
plot_delaunay(dt, title="Delaunay with Circumcircles", show_circumcircles=True)

# Segment intersections
plot_intersections(SegmentIntersection(segments))

Voronoi diagrams

plot_voronoi takes the diagram object together with the cells returned by build_voronoi_diagram:

voronoi = VoronoiDiagram(points)
cells = voronoi.build_voronoi_diagram()
plot_voronoi(voronoi, cells)

Minimum enclosing circle

plot_min_circle(min_circle)        # plot a computed minimum circle
plot_min_circle_random(n=20)       # generate random points and plot

See the API reference for full signatures.