scipy.interpolate.UnivariateSpline¶

class scipy.interpolate.UnivariateSpline(x, y, w=None, bbox=[, None, None], k=3, s=None)

One-dimensional smoothing spline fit to a given set of data points.

Fits a spline y=s(x) of degree k to the provided x, y data. s specifies the number of knots by specifying a smoothing condition.

Parameters : x : array_like 1-D array of independent input data. Must be increasing. y : array_like 1-D array of dependent input data, of the same length as x. 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. s : float or None, optional Positive smoothing factor used to choose the number of knots. Number of knots will be increased until the smoothing condition is satisfied: sum((w[i]*(y[i]-s(x[i])))**2,axis=0) <= s If None (default), s=len(w) which should be a good value if 1/w[i] is an estimate of the standard deviation of y[i]. If 0, spline will interpolate through all data points.

InterpolatedUnivariateSpline
Subclass with smoothing forced to 0
LSQUnivariateSpline
Subclass in which knots are user-selected instead of being set by smoothing condition
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 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

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