__init__(self, x, y, z, w = None, bbox = ([ None ] * 4), kx = 3, ky = 3, s = None, eps = None)
Input:
x,y,z - 1-d sequences of data points (order is not
important)
Optional input:
w - positive 1-d sequence of weights
bbox - 4-sequence 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=3,3 - degrees of the bivariate spline.
s - positive smoothing factor defined for
estimation condition:
sum((w[i]*(z[i]-s(x[i],y[i])))**2) <= s
Default s=len(w) which should be a good value
if 1/w[i] is an estimate of the standard
deviation of z[i].
eps - a threshold for determining the effective rank
of an over-determined linear system of
equations. 0 < eps < 1, default is 1e-16.
