scimba_torch.geometry.monte_carlo_hypersurface

A module for sampling hypersurfaces.

Classes

HyperSurfaceSampler([points_file, ...])

Sampler for HyperSurfaces.
class HyperSurfaceSampler(points_file=None, parametric_hyper_surface=None, bounding_domain=None, **kwargs)[source]

Bases: DomainSampler

Sampler for HyperSurfaces.

It is constructed either from a .txt files containing points on the hypersurface, or from a ParametricHyperSurface.

Parameters:

  • points_file (str | None) – A .txt file of points on the curve, default to None.

  • parametric_hyper_surface (ParametricHyperSurface | None) – a parametric HyperSurface, default to None. One among points_file, parametric_hyper_surface must be provided.

  • bounding_domain (VolumetricDomain | list[tuple[float, float]] | Tensor | None) – a bounding domain for the surface. If None whereas parametric_hyper_surface is given, estimated by sampling.

  • **kwargs – arbitrary keyword arguments

Keyword Arguments:

  • nb_points_for_estimation – in case where bounding box is estimated, number of points for estimation; default in 10 000.

  • inflation_for_estimation – in case where bounding box is estimated, inflation factor used after estimation by sampling.

Raises:

ValueError – Arguments are not correct.

bc_sample(n)[source]

Samples n points on the hypersurface.

Parameters:

n (int | list[int]) – Number of points to sample.

Return type:

tuple[LabelTensor, LabelTensor]

Returns:

A tuple of tensors of sampled points and normals.

Raises:

NotImplementedError – when the first argument is a list