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# scipy.interpolate.InterpolatedUnivariateSpline

class `scipy.interpolate.``InterpolatedUnivariateSpline`(x, y, w=None, bbox=[None, None], k=3)

  One-dimensional interpolating spline for a given set of data points.

  Fits a spline y=s(x) of degree k to the provided x, y data. Spline function passes through all provided points. Equivalent to `UnivariateSpline` with s=0.

  Parameters : | x : (N,) array_like Input dimension of data points – must be increasing y : (N,) array_like input dimension of data points w : (N,) array_like, optional Weights for spline fitting. Must be positive. If None (default), weights are all equal. bbox : (2,) array_like, optional 2-sequence specifying the boundary of the approximation interval. If None (default), bbox=[x[0],x[-1]]. k : int, optional Degree of the smoothing spline. Must be 1 <= k <= 5.

  See also

  `UnivariateSpline`
    Superclass – allows knots to be selected by a smoothing condition
  `LSQUnivariateSpline`
    spline for which knots are user-selected
  `splrep`
    An older, non object-oriented wrapping of FITPACK

  `splev`, `sproot`, `splint`, `spalde`

  `BivariateSpline`
    A similar class for two-dimensional spline interpolation

  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 InterpolatedUnivariateSpline
      >>> x = linspace(-3, 3, 100)
      >>> y = exp(-x**2) + randn(100)/10
      >>> s = InterpolatedUnivariateSpline(x, y)
      >>> xs = linspace(-3, 3, 1000)
      >>> ys = s(xs)

  xs,ys is now a smoothed, super-sampled version of the noisy gaussian x,y

  Methods

  `__call__`(x[, nu]) | Evaluate spline (or its nu-th derivative) at positions x.
  `derivatives`(x) | Return all derivatives of the spline at the point x.
  `get_coeffs`() | Return spline coefficients.
  `get_knots`() | Return positions of (boundary and interior) knots of the spline.
  `get_residual`() | Return weighted sum of squared residuals of the spline
  `integral`(a, b) | Return definite integral of the spline between two given points.
  `roots`() | Return the zeros of the spline.
  `set_smoothing_factor`(s) | Continue spline computation with the given smoothing

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