The following code creates two random signals and plots correlation with specified maximal lag and normalized cross-correlation.
import matplotlib.pyplot as plt
import numpy as np
from xcorr import correlate_maxlag, correlate_template, get_lags
np.random.seed(42)
N = 200
maxlag = 30
a = np.random.random(N)
start = N // 4
b = a[start:-start]
cc1 = correlate_maxlag(a, b, maxlag)
cc2 = correlate_template(a, b)
grid = plt.GridSpec(2, 2, wspace=0.4, hspace=0.3)
ax1 = plt.subplot(grid[0, 0:])
ax2 = plt.subplot(grid[1, 0])
ax3 = plt.subplot(grid[1, 1], sharey=ax2)
ax1.plot(np.arange(len(a)), a, label='signal a')
ax1.plot(np.arange(len(b)) + start, b, label='signal b')
ax2.plot(get_lags(cc1), cc1)
ax3.plot(cc2)
ax1.legend(loc=3)
kw = dict(xy=(0.05, 0.95), xycoords='axes fraction', va='top')
ax2.annotate('correlate_maxlag(a, b, {})'.format(maxlag), **kw)
ax3.annotate('correlate_template(a, b)', **kw)
plt.savefig('xcorr_example.png')
plt.show()
