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Fonction savgol_coeffs - module scipy.signal

Signature de la fonction savgol_coeffs

def savgol_coeffs(window_length, polyorder, deriv=0, delta=1.0, pos=None, use='conv') 

Description

savgol_coeffs.__doc__

Compute the coefficients for a 1-D Savitzky-Golay FIR filter.

    Parameters
    ----------
    window_length : int
        The length of the filter window (i.e., the number of coefficients).
        `window_length` must be an odd positive integer.
    polyorder : int
        The order of the polynomial used to fit the samples.
        `polyorder` must be less than `window_length`.
    deriv : int, optional
        The order of the derivative to compute. This must be a
        nonnegative integer. The default is 0, which means to filter
        the data without differentiating.
    delta : float, optional
        The spacing of the samples to which the filter will be applied.
        This is only used if deriv > 0.
    pos : int or None, optional
        If pos is not None, it specifies evaluation position within the
        window. The default is the middle of the window.
    use : str, optional
        Either 'conv' or 'dot'. This argument chooses the order of the
        coefficients. The default is 'conv', which means that the
        coefficients are ordered to be used in a convolution. With
        use='dot', the order is reversed, so the filter is applied by
        dotting the coefficients with the data set.

    Returns
    -------
    coeffs : 1-D ndarray
        The filter coefficients.

    References
    ----------
    A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by
    Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8),
    pp 1627-1639.

    See Also
    --------
    savgol_filter

    Notes
    -----

    .. versionadded:: 0.14.0

    Examples
    --------
    >>> from scipy.signal import savgol_coeffs
    >>> savgol_coeffs(5, 2)
    array([-0.08571429,  0.34285714,  0.48571429,  0.34285714, -0.08571429])
    >>> savgol_coeffs(5, 2, deriv=1)
    array([ 2.00000000e-01,  1.00000000e-01,  2.07548111e-16, -1.00000000e-01,
           -2.00000000e-01])

    Note that use='dot' simply reverses the coefficients.

    >>> savgol_coeffs(5, 2, pos=3)
    array([ 0.25714286,  0.37142857,  0.34285714,  0.17142857, -0.14285714])
    >>> savgol_coeffs(5, 2, pos=3, use='dot')
    array([-0.14285714,  0.17142857,  0.34285714,  0.37142857,  0.25714286])

    `x` contains data from the parabola x = t**2, sampled at
    t = -1, 0, 1, 2, 3.  `c` holds the coefficients that will compute the
    derivative at the last position.  When dotted with `x` the result should
    be 6.

    >>> x = np.array([1, 0, 1, 4, 9])
    >>> c = savgol_coeffs(5, 2, pos=4, deriv=1, use='dot')
    >>> c.dot(x)
    6.0