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.


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.

See also

a smoothing bivariate spline for scattered data
an older wrapping of FITPACK
an older wrapping of FITPACK
a similar class for univariate spline interpolation


__call__(x, y[, mth, dx, dy, grid]) Evaluate the spline or its derivatives at given positions.
ev(xi, yi[, dx, dy]) Evaluate the spline at points Returns the interpolated value at (xi[i], yi[i]), i=0,...,len(xi)-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].