ired data-type for the array, e.g., ``numpy.int8``.  Default is
    ``numpy.float64``.

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

Notes
-----
In digital signal processing literature the unit impulse signal is often
represented by the Kronecker delta. [1]_ I.e., a signal :math:`u_k[n]`,
which is zero everywhere except being one at the :math:`k`-th sample,
can be expressed as

.. math::

    u_k[n] = \delta[n-k] \equiv \delta_{n,k}\ .

Furthermore, the unit impulse is frequently interpreted as the discrete-time
version of the continuous-time Dirac distribution. [2]_

References
----------
.. [1] "Kronecker delta", *Wikipedia*,
       https://en.wikipedia.org/wiki/Kronecker_delta#Digital_signal_processing
.. [2] "Dirac delta function" *Wikipedia*,
       https://en.wikipedia.org/wiki/Dirac_delta_function#Relationship_to_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 numpy as np
>>> 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()

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