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Module « scipy.signal »

Fonction cspline1d_eval - module scipy.signal

Signature de la fonction cspline1d_eval

def cspline1d_eval(cj, newx, dx=1.0, x0=0) 

Description

help(scipy.signal.cspline1d_eval)

Evaluate a cubic spline at the new set of points.

`dx` is the old sample-spacing while `x0` was the old origin. In
other-words the old-sample points (knot-points) for which the `cj`
represent spline coefficients were at equally-spaced points of:

  oldx = x0 + j*dx  j=0...N-1, with N=len(cj)

Edges are handled using mirror-symmetric boundary conditions.

Parameters
----------
cj : ndarray
    cublic spline coefficients
newx : ndarray
    New set of points.
dx : float, optional
    Old sample-spacing, the default value is 1.0.
x0 : int, optional
    Old origin, the default value is 0.

Returns
-------
res : ndarray
    Evaluated a cubic spline points.

See Also
--------
cspline1d : Compute cubic spline coefficients for rank-1 array.

Examples
--------
We can filter a signal to reduce and smooth out high-frequency noise with
a cubic spline:

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy.signal import cspline1d, cspline1d_eval
>>> rng = np.random.default_rng()
>>> sig = np.repeat([0., 1., 0.], 100)
>>> sig += rng.standard_normal(len(sig))*0.05  # add noise
>>> time = np.linspace(0, len(sig))
>>> filtered = cspline1d_eval(cspline1d(sig), time)
>>> plt.plot(sig, label="signal")
>>> plt.plot(time, filtered, label="filtered")
>>> plt.legend()
>>> plt.show()

Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les compléments Voir le programme détaillé