scipy.interpolate.BivariateSpline

class scipy.interpolate.BivariateSpline

Base class for bivariate splines.

This describes a spline s(x, y) of degrees kx and ky on the rectangle [xb, xe] * [yb, ye] calculated from a given set of data points (x, y, z).

This class is meant to be subclassed, not instantiated directly. To construct these splines, call either SmoothBivariateSpline or LSQBivariateSpline or RectBivariateSpline.

See also

UnivariateSpline

a smooth univariate spline to fit a given set of data points.

SmoothBivariateSpline

a smoothing bivariate spline through the given points

LSQBivariateSpline

a bivariate spline using weighted least-squares fitting

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

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].
partial_derivative(dx, dy)	Construct a new spline representing a partial derivative of this spline.