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:

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.

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.