find_simplex(self, xi, bruteforce=False, tol=None)

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.

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.

Find the simplices containing the given points.

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.spatial._qhull.tsearch scipy.spatial._qhull.Delaunay

Local connectivity graph

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

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