Convex hulls in N dimensions.
New in version 0.12.0.
points : ndarray of floats, shape (npoints, ndim)
incremental : bool, optional
qhull_options : str, optional
The convex hull is computed using the Qhull libary [Qhull].
|[Qhull]||(1, 2, 3) http://www.qhull.org/|
Convex hull of a random set of points:
>>> from scipy.spatial import ConvexHull >>> points = np.random.rand(30, 2) # 30 random points in 2-D >>> hull = ConvexHull(points)
>>> import matplotlib.pyplot as plt >>> plt.plot(points[:,0], points[:,1], 'o') >>> for simplex in hull.simplices: >>> plt.plot(points[simplex,0], points[simplex,1], 'k-') >>> plt.show()
|points||(ndarray of double, shape (npoints, ndim)) Points in the convex hull.|
|simplices||(ndarray of ints, shape (nfacet, ndim)) Indices of points forming the simplical facets of the convex hull.|
|neighbors||(ndarray of ints, shape (nfacet, ndim)) Indices of neighbor facets for each facet. The kth neighbor is opposite to the kth vertex. -1 denotes no neighbor.|
|equations||(ndarray of double, shape (nfacet, ndim+1)) [normal, offset] forming the hyperplane equation of the facet (see [Qhull] documentation for more).|
|coplanar||(ndarray of int, shape (ncoplanar, 3)) Indices of coplanar points and the corresponding indices of the nearest facets and nearest vertex indices. Coplanar points are input points which were not included in the triangulation due to numerical precision issues. If option “Qc” is not specified, this list is not computed.|
|add_points(points[, restart])||Process a set of additional new points.|
|close()||Finish incremental processing.|