18  Correlograms

18.1 Autocorrelograms

import pynapple as nap
import numpy as np
dt = 0.01
times = np.arange(0, 4, dt)
rate = 40 * (1 + np.sin(2*np.pi*times)) / 2
units = nap.TsGroup({
    1: nap.Ts(times[np.random.rand(len(times)) < rate * dt]),
    2: nap.Ts(times[np.random.rand(len(times)) < rate*2 * dt]),
})
autocorrelogram = nap.compute_autocorrelogram(
    units, binsize=0.1, windowsize=1.0, norm=False
)
autocorrelogram

	1	2
-0.9	16.666667	42.980132
-0.8	16.111111	35.364238
-0.7	12.777778	27.218543
-0.6	10.694444	17.748344
-0.5	11.805556	14.172185
-0.4	11.944444	18.079470
-0.3	15.000000	29.337748
-0.2	19.444444	41.192053
-0.1	22.222222	52.715232
0.0	0.000000	0.000000
0.1	22.083333	52.715232
0.2	20.277778	41.258278
0.3	13.888889	28.807947
0.4	12.500000	18.410596
0.5	10.833333	14.039735
0.6	10.833333	17.350993
0.7	12.638889	26.887417
0.8	15.972222	34.900662
0.9	16.805556	42.913907

---

18.2 Crosscorrelograms

crosscorrelogram = nap.compute_crosscorrelogram(
    units, binsize=0.1, windowsize=1.0, norm=False
)
crosscorrelogram

	1
	2
-0.9	39.722222
-0.8	36.388889
-0.7	30.277778
-0.6	19.444444
-0.5	16.944444
-0.4	19.166667
-0.3	28.750000
-0.2	39.166667
-0.1	48.611111
0.0	54.305556
0.1	50.833333
0.2	43.888889
0.3	34.166667
0.4	25.416667
0.5	20.138889
0.6	19.861111
0.7	25.416667
0.8	32.361111
0.9	38.611111