# scipy.interpolate.RectBivariateSpline¶

class scipy.interpolate.RectBivariateSpline(x, y, z, bbox=[None, None, None, None], kx=3, ky=3, s=0)

Bivariate spline approximation over a rectangular mesh.

Can be used for both smoothing and interpolating data.

Parameters : x,y : array_like 1-D arrays of coordinates in strictly ascending order. z : array_like 2-D array of data with shape (x.size,y.size). 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 is s=0, which is for interpolation.

SmoothBivariateSpline
a smoothing bivariate spline for scattered data
UnivariateSpline
a similar class for univariate spline interpolation

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].

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