# scipy.interpolate.LSQUnivariateSpline¶

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

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 : (N,) array_like Input dimension of data points – must be increasing y : (N,) array_like Input dimension of data points t : (M,) array_like interior knots of the spline. Must be in ascending order and bbox[0]

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
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
>>> import matplotlib.pyplot as plt
>>> 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)
>>> plt.plot(x, y, '.-')
>>> plt.plot(xs, ys)
>>> plt.show()
```

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. antiderivative([n]) Construct a new spline representing the antiderivative of this spline. derivative([n]) Construct a new spline representing the derivative of this spline. 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