This is documentation for an old release of SciPy (version 0.10.1). Read this page Search for this page in the documentation of the latest stable release (version 1.15.1).

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)

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_lie, optional

Positive 1-D sequence of weights.

bbox : 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.

s : float, optional

Positive smoothing factor defined for estimation condition: sum((w[i]*(z[i]-s(x[i],y[i])))**2,axis=0) <= s Default s=len(w) which should be a good value if 1/w[i] is an estimate of the standard deviation of z[i].

eps : float, optional

A threshold for determining the effective rank of an over-determined linear system of equations. eps should have a value between 0 and 1, the default is 1e-16.

See also

bisplrep, bisplev

UnivariateSpline

a similar class for univariate spline interpolation

SmoothUnivariateSpline

To create a BivariateSpline through the given points

Notes

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

Methods

__call__(x, y[, mth])	Evaluate spline at positions x,y.
ev(xi, yi)	Evaluate spline at points (x[i], y[i]), i=0,...,len(x)-1
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
integral(xa, xb, ya, yb)	Evaluate the integral of the spline over area [xa,xb] x [ya,yb].