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scipy.interpolate.

# NdBSpline

class scipy.interpolate.NdBSpline(t, c, k, *, extrapolate=None)

  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:
    t tuple of 1D ndarrays

      knot vectors in directions 1, 2, … N, `len(t[i]) == n[i] + k + 1`

    c ndarray, shape (n1, n2, …, nN, …)

      b-spline coefficients

    k int or length-d tuple of integers

      spline degrees. A single integer is interpreted as having this degree for all dimensions.

    extrapolate bool, optional

      Whether to extrapolate out-of-bounds inputs, or return nan. Default is to extrapolate.

  See also

  `BSpline`

    a one-dimensional B-spline object

  `NdPPoly`

    an N-dimensional piecewise tensor product polynomial

  Attributes:
    t tuple of ndarrays

      Knots vectors.

    c ndarray

      Coefficients of the tensor-product spline.

    k tuple of integers

      Degrees for each dimension.

    extrapolate bool, 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.