unit_impulse(shape, idx=None, dtype=<class 'float'>)
The 1D case is also known as the Kronecker delta.
Number of samples in the output (1-D), or a tuple that represents the shape of the output (N-D).
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.
The desired data-type for the array, e.g., numpy.int8
. Default is numpy.float64
.
Output array containing an impulse signal.
Unit impulse signal (discrete delta function) or unit basis vector.
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()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._signaltools.sosfilt
scipy.signal._waveforms.unit_impulse
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