bundles / scipy 1.17.1 / scipy / interpolate / _fitpack2 / InterpolatedUnivariateSpline

class

`scipy.interpolate._fitpack2:InterpolatedUnivariateSpline`

source: /scipy/interpolate/_fitpack2.py :660

Signature

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

Members

__init__

Summary

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

Extended Summary

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]].

k : int, optional

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

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.

check_finite : bool, 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.

Notes

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

Array API Standard Support

InterpolatedUnivariateSpline is not in-scope for support of Python Array API Standard compatible backends other than NumPy.

See dev-arrayapi for more information.

Examples

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()

✓

Aliases

scipy.interpolate.InterpolatedUnivariateSpline