SciPy

scipy.interpolate.InterpolatedUnivariateSpline

class scipy.interpolate.InterpolatedUnivariateSpline(x, y, w=None, bbox=[None, None], k=3, ext=0)[source]

One-dimensional interpolating spline for a given set of data points.

Fits a spline y = spl(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 : (N,) array_like

Input dimension of data points – must be increasing

y : (N,) array_like

input dimension of data points

w : (N,) array_like, optional

Weights for spline fitting. Must be positive. If None (default), weights are all equal.

bbox : (2,) 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 1 <= k <= 5.

ext : int or str, optional

Controls the extrapolation mode for elements not in the interval defined by the knot sequence.

  • if ext=0 or ‘extrapolate’, return the extrapolated value.
  • if ext=1 or ‘zeros’, return 0
  • if ext=2 or ‘raise’, raise a ValueError
  • if ext=3 of ‘const’, return the boundary value.

The default value is 0.

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

>>> import matplotlib.pyplot as plt
>>> from scipy.interpolate import InterpolatedUnivariateSpline
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * np.random.randn(50)
>>> spl = InterpolatedUnivariateSpline(x, y)
>>> plt.plot(x, y, 'ro', ms=5)
>>> xs = np.linspace(-3, 3, 1000)
>>> plt.plot(xs, spl(xs), 'g', lw=3, alpha=0.7)
>>> plt.show()

(Source code)

../_images/scipy-interpolate-InterpolatedUnivariateSpline-1_00_00.png

Notice that the spl(x) interpolates y:

>>> spl.get_residual()
0.0

Methods

__call__(x[, nu, ext]) 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 approximation: sum((w[i] * (y[i]-spl(x[i])))**2, axis=0).
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 factor s and with the knots found at the last call.