scipy.interpolate.SmoothBivariateSpline.__init__

SmoothBivariateSpline.__init__(x, y, z, w=None, bbox=[, None, None, None, None], 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,axis=0) <= 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.