Onedimensional smoothing spline fit to a given set of data points.
Fits a spline y=s(x) of degree k to the provided x, y data. s specifies the number of knots by specifying a smoothing condition.
Parameters :  x : array_like
y : array_like
w : array_like, optional
bbox : array_like, optional
k : int, optional
s : float or None, optional


See also
Notes
The number of data points must be larger than the spline degree k.
Examples
>>> from numpy import linspace,exp
>>> from numpy.random import randn
>>> from scipy.interpolate import UnivariateSpline
>>> x = linspace(3, 3, 100)
>>> y = exp(x**2) + randn(100)/10
>>> s = UnivariateSpline(x, y, s=1)
>>> xs = linspace(3, 3, 1000)
>>> ys = s(xs)
xs,ys is now a smoothed, supersampled version of the noisy gaussian x,y.
Methods
__call__(x[, nu])  Evaluate spline (or its nuth derivative) at positions x. 
derivatives(x)  Return all derivatives of the spline at the point x. 
get_coeffs()  Return spline coefficients. 
get_knots()  Return the positions of (boundary and interior) 
get_residual()  Return weighted sum of squared residuals of the spline 
integral(a, b)  Return definite integral of the spline between two 
roots()  Return the zeros of the spline. 
set_smoothing_factor(s)  Continue spline computation with the given smoothing 