# scipy.interpolate.RectBivariateSpline¶

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

Bivariate spline approximation over a rectangular mesh.

Can be used for both smoothing and interpolating data.

Parameters
x,yarray_like

1-D arrays of coordinates in strictly ascending order.

zarray_like

2-D array of data with shape (x.size,y.size).

bboxarray_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.

sfloat, 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

bisplrep

an older wrapping of FITPACK

bisplev

an older wrapping of FITPACK

UnivariateSpline

a similar class for univariate spline interpolation

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 Return spline coefficients. Return a tuple (tx,ty) where tx,ty contain knots positions of the spline with respect to x-, y-variable, respectively. 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].

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