ccf.py

import numpy as np
import matplotlib.pyplot as plt

def cross_correlation(x, y):
    # Calculate cross-correlation using convolution
    ccf_values = np.convolve(x, y[::-1], mode='full') / np.sum(y**2)

    # Adjust indices to represent lags
    lags = np.arange(-(len(y) - 1), len(x))

    return lags, ccf_values

# Generate two sample signals
np.random.seed(42)
signal_x = np.random.randn(100)
signal_y = np.random.randn(50)

# Calculate cross-correlation
lags, ccf_values = cross_correlation(signal_x, signal_y)

# Plot cross-correlation values
plt.stem(lags, ccf_values)
plt.title('Cross-Correlation Function (CCF)')
plt.xlabel('Lag')
plt.ylabel('CCF Value')
plt.show()