NdBSpline — SciPy v1.15.3 Manual (original) (raw)
scipy.interpolate.
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 vectort
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.
See also
a one-dimensional B-spline object
an N-dimensional piecewise tensor product polynomial
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. |