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# scipy.signal.unit_impulse

`scipy.signal.``unit_impulse`(shape, idx=None, dtype=<type 'float'>)

  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.

  New in version 0.19.0.

  Examples

  An impulse at the 0th element (\(\delta[n]\)):

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

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

      >>> 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()

  (Source code)