LoopStructural.api.LoopInterpolator

class LoopStructural.api.LoopInterpolator(bounding_box: BoundingBox, dimensions: int = 3, type=InterpolatorType.FINITE_DIFFERENCE, nelements: int = 1000)

Bases: object

Scikitlearn like interface for LoopStructural interpolators useful for quickly building an interpolator to apply to a dataset build a generic interpolation object speficying the bounding box and then fit to constraints and evaluate the interpolator

Parameters:

• bounding_box (BoundingBox) – Area where the interpolation will work

• dimensions (int, optional) – number of dimensions e.g. 3d or 2d, by default 3

• type (str, optional) –

  type of interpolation algorithm FDI- finite difference, PLI - linear finite elements,

  by default “FDI”

• nelements (int, optional) – degrees of freedom for interpolator, by default 1000

__init__(bounding_box: BoundingBox, dimensions: int = 3, type=InterpolatorType.FINITE_DIFFERENCE, nelements: int = 1000)

Scikitlearn like interface for LoopStructural interpolators useful for quickly building an interpolator to apply to a dataset build a generic interpolation object speficying the bounding box and then fit to constraints and evaluate the interpolator

Parameters:

• bounding_box (BoundingBox) – Area where the interpolation will work

• dimensions (int, optional) – number of dimensions e.g. 3d or 2d, by default 3

• type (str, optional) –

  type of interpolation algorithm FDI- finite difference, PLI - linear finite elements,

  by default “FDI”

• nelements (int, optional) – degrees of freedom for interpolator, by default 1000

Methods

__init__(bounding_box[, dimensions, type, ...])	Scikitlearn like interface for LoopStructural interpolators useful for quickly building an interpolator to apply to a dataset build a generic interpolation object speficying the bounding box and then fit to constraints and evaluate the interpolator
evaluate_gradient(locations)	Evaluate the gradient of the interpolator at locations
evaluate_scalar_value(locations)	Evaluate the value of the interpolator at locations
fit([values, tangent_vectors, ...])	_summary_
fit_and_evaluate_gradient([values, ...])
fit_and_evaluate_value([values, ...])

evaluate_gradient(locations: ndarray) → ndarray

Evaluate the gradient of the interpolator at locations

Parameters:

locations (np.ndarray) – Nx3 locations

Returns:

np.ndarray – Nx3 gradient of implicit function

evaluate_scalar_value(locations: ndarray) → ndarray

Evaluate the value of the interpolator at locations

Parameters:

locations (np.ndarray) – Nx3 array of locations to evaluate the interpolator at

Returns:

np.ndarray – value of implicit function at locations

fit(values: ndarray | None = None, tangent_vectors: ndarray | None = None, normal_vectors: ndarray | None = None, inequality_constraints: ndarray | None = None)

_summary_

Parameters:

• values (Optional[np.ndarray], optional) – Value constraints for implicit function, by default None

• tangent_vectors (Optional[np.ndarray], optional) – tangent constraints for implicit function, by default None

• normal_vectors (Optional[np.ndarray], optional) – gradient norm constraints for implicit function, by default None

• inequality_constraints (Optional[np.ndarray], optional) – _description_, by default None