How to compute cross-correlation of two given NumPy arrays?

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().

import numpy as np
array1 = np.array([0, 1, 2])
array2 = np.array([3, 4, 5])

# Original array1
print(array1)

# Original array2
print(array2)

# ross-correlation of the arrays
print("\nCross-correlation:\n",
      np.correlate(array1, array2))

Output:

[0 1 2]
[3 4 5]

Cross-correlation:
 [14]

Example 2:

import numpy as np
array1 = np.array([1,2])
array2 = np.array([1,2])

# Original array1
print(array1)

# Original array2
print(array2)
# Cross-correlation of the arrays
print("\nCross-correlation:\n",
      np.correlate(array1, array2))

Output:

[1 2]
[1 2]

Cross-correlation:
 [5]