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# scipy.interpolate.LSQUnivariateSpline

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

  One-dimensional spline with explicit internal knots.

  Fits a spline y=s(x) of degree k to the provided x, y data. t specifies the internal knots of the spline

  Parameters : | x : array_like input dimension of data points – must be increasing y : array_like input dimension of data points t: array_like : interior knots of the spline. Must be in ascending order and bbox[0]<t[0]<...<t[-1]<bbox[-1] w : array_like, optional weights for spline fitting. Must be positive. If None (default), weights are all equal. bbox : 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 <= 5.
  Raises : | ValueError : If the interior knots do not satisfy the Schoenberg-Whitney conditions

  See also

  `UnivariateSpline`
    Superclass – knots are specified by setting a smoothing condition
  `InterpolatedUnivariateSpline`
    spline passing through all points
  `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 LSQUnivariateSpline
      >>> x = linspace(-3,3,100)
      >>> y = exp(-x**2) + randn(100)/10
      >>> t = [-1,0,1]
      >>> s = LSQUnivariateSpline(x,y,t)
      >>> xs = linspace(-3,3,1000)
      >>> ys = s(xs)

  xs,ys is now a smoothed, super-sampled version of the noisy gaussian x,y with knots [-3,-1,0,1,3]

  Methods

  `derivatives` |
  `get_coeffs` |
  `get_knots` |
  `get_residual` |
  `integral` |
  `roots` |
  `set_smoothing_factor` |

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