`scipy.interpolate._fitpack2:LSQBivariateSpline`

source: /scipy/interpolate/_fitpack2.py :1459

Signature

   class     LSQBivariateSpline (    x    ,    y    ,    z    ,    tx    ,    ty    ,    w    =  None   ,    bbox    =  [None, None, None, None]   ,    kx    =  3   ,    ky    =  3   ,    eps    =  None     )

Members

__init__

Summary

Weighted least-squares bivariate spline approximation.

Parameters

x, y, z : array_like: 1-D sequences of data points (order is not important).
tx, ty : array_like: Strictly ordered 1-D sequences of knots coordinates.
w : array_like, optional: Positive 1-D array of weights, of the same length as x, y and z.
bbox : (4,) array_like, optional: Sequence of length 4 specifying the boundary of the rectangular approximation domain. By default, bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)].
kx, ky : ints, optional: Degrees of the bivariate spline. Default is 3.
eps : float, optional: A threshold for determining the effective rank of an over-determined linear system of equations. eps should have a value within the open interval (0, 1), the default is 1e-16.

Notes

The length of x, y and z should be at least (kx+1) * (ky+1).

If the input data is such that input dimensions have incommensurate units and differ by many orders of magnitude, the interpolant may have numerical artifacts. Consider rescaling the data before interpolating.

Array API Standard Support

LSQBivariateSpline is not in-scope for support of Python Array API Standard compatible backends other than NumPy.

See dev-arrayapi for more information.

Aliases

scipy.interpolate.LSQBivariateSpline