scipy.interpolate.UnivariateSpline.__init__

UnivariateSpline.__init__(x, y, w=None, bbox=[, None, None], k=3, s=None)

Input:

x,y - 1-d sequences of data points (x must be

in strictly ascending order)

Optional input:

w - positive 1-d sequence of weights bbox - 2-sequence specifying the boundary of

the approximation interval. By default, bbox=[x[0],x[-1]]

k=3 - degree of the univariate spline. s - positive smoothing factor defined for

estimation condition:

sum((w[i]*(y[i]-s(x[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 y[i].