Classe « InterpolatedUnivariateSpline »

Informations générales

Héritage

builtins.object
    UnivariateSpline
        InterpolatedUnivariateSpline

Définition

class InterpolatedUnivariateSpline(UnivariateSpline):

help(InterpolatedUnivariateSpline)

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

.. legacy:: class

    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]]``.
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.

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
--------
>>> 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

Constructeur(s)

Signature du constructeur	Description
__init__(self, x, y, w=None, bbox=[None, None], k=3, ext=0, check_finite=False)

Liste des opérateurs

Opérateurs hérités de la classe object

__eq__, __ge__, __gt__, __le__, __lt__, __ne__

Liste des méthodes

Toutes les méthodes Méthodes d'instance Méthodes statiques

Signature de la méthode	Description

Méthodes héritées de la classe UnivariateSpline

__call__, __init_subclass__, __subclasshook__, antiderivative, derivative, derivatives, get_coeffs, get_knots, get_residual, integral, roots, set_smoothing_factor, validate_input