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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
spike_trains = spk.load_spike_trains_from_txt("SPIKY_testdata.txt",
time_interval=(0,4000))
# 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])
x, y = f.get_plottable_data()
plt.figure()
plt.plot(x, np.abs(y), '--k')
print("Average: %.8f" % f.avrg())
print("Absolute average: %.8f" % f.abs_avrg())
f = spk.spike_distance(spike_trains[0], spike_trains[1])
x, y = f.get_plottable_data()
print(x)
print(y)
#plt.figure()
plt.plot(x, y, '-b')
plt.show()
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