API

Creation routines

Routines for creating NDSpline objects.

make_lsq_spline(x, y, knots, degrees[, w, ...])	Construct a least squares regression B-spline.
make_interp_spline(x, y[, degrees, bcs])	Construct an interpolating B-spline.
make_interp_spline_from_tidy(tidy_data, ...)	Construct an interpolating B-spline from a tidy data source.
from_file(file)	Create a NDSpline object from a NumPy archive containing the necessary attributes.

Class methods

Methods of the NDSpline class.

NDSpline(knots, coefficients, degrees[, ...])	Multivariate tensor-product spline in the B-spline basis.
NDSpline.__call__(x[, nus])	Evaluate the N-dimensional B-spline.
NDSpline.derivative(dim[, nu])	Return NDSpline representing the nu-th derivative in the dim-th dimension.
NDSpline.antiderivative(dim[, nu])	Return NDSpline representing the nu-th antiderivative in the dim-th dimension.
NDSpline.to_file(file[, compress])	Save attributes of NDSpline object to binary file in NumPy .npz format so that the object can be re-created.
NDSpline.copy()	Return a deep copy of this NDSpline object.
NDSpline.__eq__(other)	Check equality with another spline.
NDSpline.allocate_workspace_arrays(num_points)	Allocate workspace arrays for the N-dimensional B-spline evaluation.
NDSpline.compute_basis_coefficient_selector(x)	Evaluate the N-dimensional B-spline basis functions and coefficient selectors.

Knots

Utility function for constructing knot arrays.

_not_a_knot(x, k[, left, right])

Utility function to perform the knot portion of the not-a-knot procedure.

Implementations

Selection and usage of the Cython or NumPy implementations for B-Spline evaluation.

set_impl(name)	Set bspl implementation to either cython or numpy.
get_impl()	Get the current bspl implementation as a string.
_bspl	Cython implementation for evaluating B-splines.
_npy_bspl	NumPy implementation for evaluating B-splines.