scipy.spatial.ConvexHull

class scipy.spatial.ConvexHull(points, incremental=False, qhull_options=None)

Convex hulls in N dimensions.

New in version 0.12.0.

Parameters:

points : ndarray of floats, shape (npoints, ndim)

Coordinates of points to construct a convex hull from

incremental : bool, optional

Allow adding new points incrementally. This takes up some additional resources.

qhull_options : str, optional

Additional options to pass to Qhull. See Qhull manual for details. (Default: “Qx” for ndim > 4 and “” otherwise) Option “Qt” is always enabled.

Raises:

QhullError :

Raised when Qhull encounters an error condition, such as geometrical degeneracy when options to resolve are not enabled.

ValueError :

Raised if an incompatible array is given as input.

Notes

The convex hull is computed using the Qhull library.

Do not call the add_points method from a __del__ destructor.

References

[Qhull]

http://www.qhull.org/

Examples

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)

Plot it:

>>> 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-')

We could also have directly used the vertices of the hull, which for 2-D are guaranteed to be in counterclockwise order:

>>> plt.plot(points[hull.vertices,0], points[hull.vertices,1], 'r--', lw=2)
>>> plt.plot(points[hull.vertices[0],0], points[hull.vertices[0],1], 'ro')
>>> plt.show()

(Source code)

Attributes