# 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]

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
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

 __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 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

#### Previous topic

scipy.interpolate.InterpolatedUnivariateSpline.set_smoothing_factor

#### Next topic

scipy.interpolate.LSQUnivariateSpline.__call__