trimesh.nsphere

nsphere.py

Functions for fitting and minimizing nspheres: circles, spheres, hyperspheres, etc.

Functions:

fit_nsphere(points[, prior])

Fit an n-sphere to a set of points using least squares.

is_nsphere(points)

Check if a list of points is an nsphere.

leastsq(func, x0[, args, Dfun, full_output, …])

Minimize the sum of squares of a set of equations.

minimum_nsphere(obj)

Compute the minimum n- sphere for a mesh or a set of points.
trimesh.nsphere.fit_nsphere(points, prior=None)

Fit an n-sphere to a set of points using least squares.

Parameters

  • points ((n, d) float) – Points in space

  • prior ((d,) float) – Best guess for center of nsphere

Returns

  • center ((d,) float) – Location of center

  • radius (float) – Mean radius across circle

  • error (float) – Peak to peak value of deviation from mean radius

trimesh.nsphere.is_nsphere(points)

Check if a list of points is an nsphere.

Parameters

points ((n, dimension) float) – Points in space

Returns

check – True if input points are on an nsphere

Return type

bool

trimesh.nsphere.minimum_nsphere(obj)

Compute the minimum n- sphere for a mesh or a set of points.

Uses the fact that the minimum n- sphere will be centered at one of the vertices of the furthest site voronoi diagram, which is n*log(n) but should be pretty fast due to using the scipy/qhull implementations of convex hulls and voronoi diagrams.

Parameters

obj ((n, d) float or trimesh.Trimesh) – Points or mesh to find minimum bounidng nsphere

Returns

  • center ((d,) float) – Center of fitted n- sphere

  • radius (float) – Radius of fitted n-sphere