Module « scipy.signal »

Fonction unit_impulse - module scipy.signal

Signature de la fonction unit_impulse

def unit_impulse(shape, idx=None, dtype=<class 'float'>) 

Description

unit_impulse.__doc__

    Unit impulse signal (discrete delta function) or unit basis vector.

    Parameters
    ----------
    shape : int or tuple of int
        Number of samples in the output (1-D), or a tuple that represents the
        shape of the output (N-D).
    idx : None or int or tuple of int or 'mid', optional
        Index at which the value is 1.  If None, defaults to the 0th element.
        If ``idx='mid'``, the impulse will be centered at ``shape // 2`` in
        all dimensions.  If an int, the impulse will be at `idx` in all
        dimensions.
    dtype : data-type, optional
        The desired data-type for the array, e.g., ``numpy.int8``.  Default is
        ``numpy.float64``.

    Returns
    -------
    y : ndarray
        Output array containing an impulse signal.

    Notes
    -----
    The 1D case is also known as the Kronecker delta.

    .. versionadded:: 0.19.0

    Examples
    --------
    An impulse at the 0th element (:math:`\delta[n]`):

    >>> from scipy import signal
    >>> signal.unit_impulse(8)
    array([ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

    Impulse offset by 2 samples (:math:`\delta[n-2]`):

    >>> signal.unit_impulse(7, 2)
    array([ 0.,  0.,  1.,  0.,  0.,  0.,  0.])

    2-dimensional impulse, centered:

    >>> signal.unit_impulse((3, 3), 'mid')
    array([[ 0.,  0.,  0.],
           [ 0.,  1.,  0.],
           [ 0.,  0.,  0.]])

    Impulse at (2, 2), using broadcasting:

    >>> signal.unit_impulse((4, 4), 2)
    array([[ 0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.],
           [ 0.,  0.,  1.,  0.],
           [ 0.,  0.,  0.,  0.]])

    Plot the impulse response of a 4th-order Butterworth lowpass filter:

    >>> imp = signal.unit_impulse(100, 'mid')
    >>> b, a = signal.butter(4, 0.2)
    >>> response = signal.lfilter(b, a, imp)

    >>> import matplotlib.pyplot as plt
    >>> plt.plot(np.arange(-50, 50), imp)
    >>> plt.plot(np.arange(-50, 50), response)
    >>> plt.margins(0.1, 0.1)
    >>> plt.xlabel('Time [samples]')
    >>> plt.ylabel('Amplitude')
    >>> plt.grid(True)
    >>> plt.show()