tspart.voronoi

Module Contents

Functions

rasterize(V)	Polygon rasterization (scanlines).
rasterize_outline(V)	Polygon outline rasterization (scanlines).
weighted_centroid_outline(V, P, Q)	Given an ordered set of vertices V describing a polygon,
uniform_centroid(V)	Given an ordered set of vertices V describing a polygon,
weighted_centroid(V, D)	Given an ordered set of vertices V describing a polygon,
in_box(points, bbox)
voronoi(points, bbox)
centroids(points, density[, density_P, density_Q])	Given a set of point and a density array, return the set of weighted
normalize(D)
initialization(n, D)	Return n points distributed over [xmin, xmax] x [ymin, ymax]
stipple_image(grayscale_array[, points, iterations, ...])
stipple_image_multi(grayscale_arrays[, points, ...])

tspart.voronoi.rasterize(V)

Polygon rasterization (scanlines).

Given an ordered set of vertices V describing a polygon, return all the (integer) points inside the polygon. See http://alienryderflex.com/polygon_fill/

Parameters:

V(n,2) shaped numpy array

Polygon vertices

tspart.voronoi.rasterize_outline(V)

Polygon outline rasterization (scanlines).

Given an ordered set of vertices V describing a polygon, return all the (integer) points for the polygon outline. See http://alienryderflex.com/polygon_fill/

Parameters:

V(n,2) shaped numpy array

Polygon vertices

tspart.voronoi.weighted_centroid_outline(V, P, Q)

Given an ordered set of vertices V describing a polygon, return the surface weighted centroid according to density P & Q.

P & Q are computed relatively to density: density_P = density.cumsum(axis=1) density_Q = density_P.cumsum(axis=1)

This works by first rasterizing the polygon and then finding the center of mass over all the rasterized points.

tspart.voronoi.uniform_centroid(V)

Given an ordered set of vertices V describing a polygon, returns the uniform surface centroid.

See http://paulbourke.net/geometry/polygonmesh/

tspart.voronoi.weighted_centroid(V, D)

Given an ordered set of vertices V describing a polygon, return the surface weighted centroid according to density D.

This works by first rasterizing the polygon and then finding the center of mass over all the rasterized points.

tspart.voronoi.in_box(points, bbox)

tspart.voronoi.voronoi(points, bbox)

tspart.voronoi.centroids(points, density, density_P=None, density_Q=None)

Given a set of point and a density array, return the set of weighted centroids.

tspart.voronoi.normalize(D)

tspart.voronoi.initialization(n, D)

Return n points distributed over [xmin, xmax] x [ymin, ymax] according to (normalized) density distribution.

with xmin, xmax = 0, density.shape[1]

ymin, ymax = 0, density.shape[0]

The algorithm here is a simple rejection sampling.

tspart.voronoi.stipple_image(grayscale_array, points=5000, iterations=50, logging=True)

tspart.voronoi.stipple_image_multi(grayscale_arrays, points=5000, iterations=50, logging=True)