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 : array_like

input dimension of data points – must be increasing

y : array_like

input dimension of data points

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.

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 UnivariateSpline
>>> x = linspace(-3, 3, 100)
>>> y = exp(-x**2) + randn(100)/10
>>> s = UnivariateSpline(x, y, s=1)
>>> xs = linspace(-3, 3, 1000)
>>> ys = s(xs)

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

Methods

derivatives
get_coeffs
get_knots
get_residual
integral
roots
set_smoothing_factor