bundles / scipy 1.17.1 / scipy / interpolate / _ndbspline / NdBSpline
class
scipy.interpolate._ndbspline:NdBSpline
Signature
class NdBSpline ( t , c , k , * , extrapolate = None ) Members
Summary
Tensor product spline object.
Extended Summary
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 ndarraysknot vectors in directions 1, 2, ... N,
len(t[i]) == n[i] + k + 1c: ndarray, shape (n1, n2, ..., nN, ...)b-spline coefficients
k: int or length-d tuple of integersspline degrees. A single integer is interpreted as having this degree for all dimensions.
extrapolate: bool, optionalWhether to extrapolate out-of-bounds inputs, or return
nan. Default is to extrapolate.
Attributes
t: tuple of ndarraysKnots vectors.
c: ndarrayCoefficients of the tensor-product spline.
k: tuple of integersDegrees for each dimension.
extrapolate: bool, optionalWhether to extrapolate or return nans for out-of-bounds inputs. Defaults to true.
Methods
__call__derivativedesign_matrix
Notes
Array API Standard Support
NdBSpline has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.
==================== ==================== ==================== Library CPU GPU ==================== ==================== ==================== NumPy ✅ n/a CuPy n/a ⛔ PyTorch ✅ ⛔ JAX ⚠️ no JIT ⛔ Dask ⛔ n/a ==================== ==================== ====================
See
dev-arrayapifor more information.
See also
Aliases
-
scipy.interpolate.NdBSpline