NumPy: Compute cross-correlation of two given arrays

Last update on March 25 2025 12:21:14 (UTC/GMT +8 hours)

9. Cross-Correlation of Two Arrays

Write a NumPy program to compute cross-correlation of two given arrays.

Sample Solution:

Python Code:

# Importing the NumPy library
import numpy as np

# Creating an array 'x' containing elements [0, 1, 3]
x = np.array([0, 1, 3])

# Creating an array 'y' containing elements [2, 4, 5]
y = np.array([2, 4, 5])

# Displaying the original array 'x'
print("\nOriginal array1:")
print(x)

# Displaying the original array 'y'
print("\nOriginal array1:")
print(y)

# Calculating the cross-correlation of arrays 'x' and 'y' using np.correlate()
print("\nCross-correlation of the said arrays:\n", np.correlate(x, y)) 

Sample Output:

Original array1:
[0 1 3]

Original array1:
[2 4 5]

Cross-correlation of the said arrays:
 [19]

Explanation:

In the above exercise –

x = np.array([0, 1, 3]): Create a NumPy array x with the values [0, 1, 3].

y = np.array([2, 4, 5]): Create a NumPy array y with the values [2, 4, 5].

print(np.correlate(x, y)): Compute and print the cross-correlation of the sequences x and y using np.correlate.

For more Practice: Solve these Related Problems:

• Write a function that computes the cross-correlation of two 1D arrays using np.correlate and returns the correlation vector.
• Create a program that calculates cross-correlation at different lags and identifies the lag with the maximum correlation.
• Implement a solution that normalizes the cross-correlation result to obtain correlation coefficients between -1 and 1.
• Develop a method that handles arrays of different lengths by zero-padding the shorter array before computing cross-correlation.

Python-Numpy Code Editor:

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