How to compute cross-correlation of two given NumPy arrays?
Last Updated :
08 Dec, 2020
In the Numpy program, we can compute cross-correlation of two given arrays with the help of correlate(). In this first parameter and second parameter pass the given arrays it will return the cross-correlation of two given arrays.
Syntax : numpy.correlate(a, v, mode = ‘valid’)
Parameters :
a, v : [array_like] Input sequences.
mode : [{‘valid’, ‘same’, ‘full’}, optional] Refer to the convolve docstring. Default is ‘valid’.
Return : [ndarray] Discrete cross-correlation of a and v.
Example 1:
In this example, we will create two NumPy arrays and the task is to compute cross-correlation using correlate().
Python3
import numpy as np
array1 = np.array([ 0 , 1 , 2 ])
array2 = np.array([ 3 , 4 , 5 ])
print (array1)
print (array2)
print ( "\nCross-correlation:\n" ,
np.correlate(array1, array2))
|
Output:
[0 1 2]
[3 4 5]
Cross-correlation:
[14]
Example 2:
Python3
import numpy as np
array1 = np.array([ 1 , 2 ])
array2 = np.array([ 1 , 2 ])
print (array1)
print (array2)
print ( "\nCross-correlation:\n" ,
np.correlate(array1, array2))
|
Output:
[1 2]
[1 2]
Cross-correlation:
[5]
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