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