Module « scipy.signal »

Fonction cspline1d - module scipy.signal

Signature de la fonction cspline1d

def cspline1d(signal, lamb=0.0) 

Description

cspline1d.__doc__

    Compute cubic spline coefficients for rank-1 array.

    Find the cubic spline coefficients for a 1-D signal assuming
    mirror-symmetric boundary conditions. To obtain the signal back from the
    spline representation mirror-symmetric-convolve these coefficients with a
    length 3 FIR window [1.0, 4.0, 1.0]/ 6.0 .

    Parameters
    ----------
    signal : ndarray
        A rank-1 array representing samples of a signal.
    lamb : float, optional
        Smoothing coefficient, default is 0.0.

    Returns
    -------
    c : ndarray
        Cubic spline coefficients.

    See Also
    --------
    cspline1d_eval : Evaluate a cubic spline at the new set of points.

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

    >>> 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()