scipy.spatial.Delaunay.find_simplex¶

Delaunay.find_simplex(self, xi, bruteforce=False, tol=None)¶

Find the simplices containing the given points.

Parameters:

Parameters:	tri : DelaunayInfo Delaunay triangulation xi : ndarray of double, shape (…, ndim) Points to locate bruteforce : bool, optional Whether to only perform a brute-force search tol : float, optional Tolerance allowed in the inside-triangle check. Default is `100*eps`.
Returns:	i : ndarray of int, same shape as xi Indices of simplices containing each point. Points outside the triangulation get the value -1.

tri : DelaunayInfo

Delaunay triangulation

xi : ndarray of double, shape (…, ndim)

Points to locate

bruteforce : bool, optional

Whether to only perform a brute-force search

tol : float, optional

Tolerance allowed in the inside-triangle check. Default is 100*eps.

Returns:

i : ndarray of int, same shape as xi

Indices of simplices containing each point. Points outside the triangulation get the value -1.

Notes

This uses an algorithm adapted from Qhull’s qh_findbestfacet, which makes use of the connection between a convex hull and a Delaunay triangulation. After finding the simplex closest to the point in N+1 dimensions, the algorithm falls back to directed search in N dimensions.