scipy.interpolate.

InterpolatedUnivariateSpline

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

1-D interpolating spline for a given set of data points.

Legacy

This class is considered legacy and will no longer receive updates. While we currently have no plans to remove it, we recommend that new code uses more modern alternatives instead. Specifically, we recommend using make_interp_spline instead.

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 strictly 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 1.

bbox(2,) array_like, optional

2-sequence specifying the boundary of the approximation interval. If None (default), bbox=[x[0], x[-1]].

kint, optional

Degree of the smoothing spline. Must be 1 <= k <= 5. Default is k = 3, a cubic spline.

extint 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.

check_finitebool, optional

Whether to check that the input arrays contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination or non-sensical results) if the inputs do contain infinities or NaNs. Default is False.

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 interior knots of the spline.
get_residual()	Return weighted sum of squared residuals of the spline approximation.
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.

validate_input

See also

UnivariateSpline

a smooth univariate spline to fit a given set of data points.

LSQUnivariateSpline

a spline for which knots are user-selected

SmoothBivariateSpline

a smoothing bivariate spline through the given points

LSQBivariateSpline

a bivariate spline using weighted least-squares fitting

splrep

a function to find the B-spline representation of a 1-D curve

splev

a function to evaluate a B-spline or its derivatives

sproot

a function to find the roots of a cubic B-spline

splint

a function to evaluate the definite integral of a B-spline between two given points

spalde

a function to evaluate all derivatives of a B-spline

Notes

The number of data points must be larger than the spline degree k.

Examples

Try it in your browser!

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.interpolate import InterpolatedUnivariateSpline
>>> rng = np.random.default_rng()
>>> x = np.linspace(-3, 3, 50)
>>> y = np.exp(-x**2) + 0.1 * rng.standard_normal(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()

Notice that the spl(x) interpolates y:

>>> spl.get_residual()
0.0

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