scipy.interpolate.

NdBSpline#

class scipy.interpolate.NdBSpline(t, c, k, *, extrapolate=None)[source]#

Tensor product spline object.

The value at point xp = (x1, x2, ..., xN) is evaluated as a linear combination of products of one-dimensional b-splines in each of the N dimensions:

c[i1, i2, ..., iN] * B(x1; i1, t1) * B(x2; i2, t2) * ... * B(xN; iN, tN)

Here B(x; i, t) is the i-th b-spline defined by the knot vector t evaluated at x.

Parameters:

ttuple of 1D ndarrays: knot vectors in directions 1, 2, … N, len(t[i]) == n[i] + k + 1
cndarray, shape (n1, n2, …, nN, …): b-spline coefficients
kint or length-d tuple of integers: spline degrees. A single integer is interpreted as having this degree for all dimensions.
extrapolatebool, optional: Whether to extrapolate out-of-bounds inputs, or return nan. Default is to extrapolate.

Attributes:

ttuple of ndarrays: Knots vectors.
cndarray: Coefficients of the tensor-product spline.
ktuple of integers: Degrees for each dimension.
extrapolatebool, optional: Whether to extrapolate or return nans for out-of-bounds inputs. Defaults to true.

Methods

`__call__`(xi, *[, nu, extrapolate])	Evaluate the tensor product b-spline at `xi`.
`design_matrix`(xvals, t, k[, extrapolate])	Construct the design matrix as a CSR format sparse array.