tsearch(tri, xi)
Find simplices containing the given points. This function does the same thing as Delaunay.find_simplex
.
>>> import numpy as np
... import matplotlib.pyplot as plt
... from scipy.spatial import Delaunay, delaunay_plot_2d, tsearch
... rng = np.random.default_rng()
The Delaunay triangulation of a set of random points:
>>> pts = rng.random((20, 2))
... tri = Delaunay(pts)
... _ = delaunay_plot_2d(tri)
Find the simplices containing a given set of points:
>>> loc = rng.uniform(0.2, 0.8, (5, 2))
... s = tsearch(tri, loc)
... plt.triplot(pts[:, 0], pts[:, 1], tri.simplices[s], 'b-', mask=s==-1)
... plt.scatter(loc[:, 0], loc[:, 1], c='r', marker='x')
... plt.show()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.spatial._qhull.tsearch
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them