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- Ndimensions:- 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- tevaluated 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.