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# compute the isi distance of some test data
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
import pyspike as spk
# first load the data
spike_trains = []
spike_file = open("SPIKY_testdata.txt", 'r')
for line in spike_file:
spike_trains.append(spk.spike_train_from_string(line))
# plot the spike time
for (i,spikes) in enumerate(spike_trains):
plt.plot(spikes, i*np.ones_like(spikes), 'o')
f = spk.isi_distance(spike_trains[0], spike_trains[1], 4000)
x, y = f.get_plottable_data()
plt.figure()
plt.plot(x, y, '-k')
print("Average: %.8f" % f.avrg())
print("Absolute average: %.8f" % f.abs_avrg())
plt.show()
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