SplineCurve

class SplineCurve(positions, order=1, **kwargs)

Bases: Parametrization

Parametrize a point cloud as a spline curve.

Parameters:

positionsnp.ndarray

Control points defining the spline curve

Methods

SplineCurve.compute_normal(points)	Compute the normal vector at a given point on the surface.
SplineCurve.fit(positions, *[, order])	Fit a spline curve through a point cloud.
SplineCurve.points_per_sampling(sampling_density)	Computes the approximate number of random samples required to achieve a given spatial sampling_density.
SplineCurve.sample(n_samples[, normal_offset])	Samples points from the surface of the parametrization.

compute_normal(points)

Compute the normal vector at a given point on the surface.

Parameters:

pointsnp.ndarray

Points on the surface with shape n x d

Returns:

np.ndarray

Normal vectors at the given points

classmethod fit(positions, *, order=1, **kwargs)

Fit a spline curve through a point cloud.

Parameters:

positionsnp.ndarray

Control points with shape (n, 3). Order along the curve is inferred from cumulative chord lengths.

orderint

Spline degree. 1 = linear, 3 = cubic spline.

Returns:

SplineCurve

Fitted spline curve.

points_per_sampling(sampling_density, normal_offset=None)

Computes the approximate number of random samples required to achieve a given spatial sampling_density.

Parameters:

sampling_densityfloat

Average distance between points.

normal_offsetfloat, optional

Compute number of samples on offset parametrization, instead of current.

Returns:

int

Number of required random samples.

sample(n_samples, normal_offset=0.0, **kwargs)

Samples points from the surface of the parametrization.

Parameters:

n_samplesint

Number of samples to draw

normal_offsetfloat, optional

Offset points by normal_offset times their normal vector.

*argsList

Additional arguments

**kwargsDict

Additional keywoard arguments.

Returns:

np.ndarray

Point coordinates (n, 3).