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# 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].

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