`in_hull`#

acoular.grids.in_hull(p, hull, border=True, tol=0)

Test if points in p are in hull, in- or excluding the border.

Parameters:

pnumpy.ndarray of floats, shape (N, K): Coordinates of N points in K dimensions.
hullnumpy.ndarray of floats, shape (M, K), or Delaunay object: Coordinates of M points in K dimensions for which Delaunay triangulation will be computed.
borderbool, optional: Points in p on the border of hull will be kept in the return if True. If False, only points inside hull will be kept. Default is True.
tolfloat, optional: Tolerance allowed in the inside-triangle check. Default is 0.

Returns:

numpy.ndarray of bools: An array of boolean values indicating which points in p are inside the hull, same shape as p. Each entry is True if the corresponding point is inside the hull (or on the border, if border=True), and False otherwise.

Notes

This function uses Delaunay triangulation to determine if a point is inside the convex hull, which is efficient and robust for arbitrary shapes in higher-dimensional spaces.

Examples

>>> from acoular.grids import in_hull
>>> import numpy as np
>>> from scipy.spatial import Delaunay
>>> points = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])
>>> hull = Delaunay(points)
>>> p = np.array([[0.5, 0.5], [2, 2]])
>>> in_hull(p, hull)
array([ True, False])

in_hull#

`in_hull`#