# 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 nparray1 = np.array([0, 1, 2])array2 = np.array([3, 4, 5])  # Original array1print(array1)  # Original array2print(array2)  # ross-correlation of the arraysprint("\nCross-correlation:\n",      np.correlate(array1, array2))

Output:

[0 1 2]
[3 4 5]

Cross-correlation:
[14]

Example 2:

## Python3

 import numpy as nparray1 = np.array([1,2])array2 = np.array([1,2])  # Original array1print(array1)  # Original array2print(array2)# Cross-correlation of the arraysprint("\nCross-correlation:\n",      np.correlate(array1, array2))

Output:

[1 2]
[1 2]

Cross-correlation:
[5]

My Personal Notes arrow_drop_up