scipy.interpolate.LSQBivariateSpline¶

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

Weighted least-squares bivariate spline approximation.

Parameters

x, y, zarray_like: 1-D sequences of data points (order is not important).
tx, tyarray_like: Strictly ordered 1-D sequences of knots coordinates.
warray_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, kyints, optional: Degrees of the bivariate spline. Default is 3.
epsfloat, 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.

See also

BivariateSpline: a base class for bivariate splines.
UnivariateSpline: a smooth univariate spline to fit a given set of data points.
SmoothBivariateSpline: a smoothing bivariate spline through the given points
RectSphereBivariateSpline: a bivariate spline over a rectangular mesh on a sphere
SmoothSphereBivariateSpline: a smoothing bivariate spline in spherical coordinates
LSQSphereBivariateSpline: a bivariate spline in spherical coordinates using weighted least-squares fitting
RectBivariateSpline: a bivariate spline over a rectangular mesh.
bisplrep: a function to find a bivariate B-spline representation of a surface
bisplev: a function to evaluate a bivariate B-spline and its derivatives

Notes

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

Methods

`__call__`(x, y[, dx, dy, grid])	Evaluate the spline or its derivatives at given positions.
`ev`(xi, yi[, dx, dy])	Evaluate the spline at points
`get_coeffs`()	Return spline coefficients.
`get_knots`()	Return a tuple (tx,ty) where tx,ty contain knots positions of the spline with respect to x-, y-variable, respectively.
`get_residual`()	Return weighted sum of squared residuals of the spline approximation: sum ((w[i](z[i]-s(x[i],y[i])))*2,axis=0)
`integral`(xa, xb, ya, yb)	Evaluate the integral of the spline over area [xa,xb] x [ya,yb].