def test_save_load():
file_name = os.path.join(tempfile.mkdtemp(prefix='pyspike_'),
"save_load.txt")
N = 10
# generate some spike trains
spike_trains = []
for n in range(N):
spike_trains.append(spk.generate_poisson_spikes(1.0, [0, 100]))
# save them into txt file
spk.save_spike_trains_to_txt(spike_trains, file_name, precision=17)
# load again
spike_trains_loaded = spk.load_spike_trains_from_txt(file_name, [0, 100])
for n in range(N):
assert_array_equal(spike_trains[n].spikes,
spike_trains_loaded[n].spikes)
plt.plot(x, y, '--k')
print("ISI distance: %.8f" % isi_profile.avrg())
plt.show()
spike_profile = spk.spike_profile(spike_trains[0], spike_trains[1])
x, y = spike_profile.get_plottable_data()
plt.plot(x, y, '--k')
print("SPIKE distance: %.8f" % spike_profile.avrg())
plt.show()
import numpy as np
from matplotlib import pyplot as plt
import pyspike as spk
st1 = spk.generate_poisson_spikes(1.0, [0, 20])
st2 = spk.generate_poisson_spikes(1.0, [0, 20])
d = spk.spike_directionality(st1, st2)
#print "Spike Directionality of two Poissonian spike trains:", d
E = spk.spike_train_order_profile(st1, st2)
plt.figure()
x, y = E.get_plottable_data()
plt.plot(x, y, '-ob')
plt.ylim(-1.1, 1.1)
plt.xlabel("t")
plt.ylabel("E")
plt.title("Spike Train Order Profile")
import pstats
# in case you dont have the cython backends, disable the warnings as follows:
# spk.disable_backend_warning = True
M = 100 # number of spike trains
r = 1.0 # rate of Poisson spike times
T = 1E3 # length of spike trains
print("%d spike trains with %d spikes" % (M, int(r*T)))
spike_trains = []
t_start = datetime.now()
for i in range(M):
spike_trains.append(spk.generate_poisson_spikes(r, T))
t_end = datetime.now()
runtime = (t_end-t_start).total_seconds()
sort_by = 'tottime'
# sort_by = 'cumtime'
print("Spike generation runtime: %.3fs" % runtime)
print()
print("================ ISI COMPUTATIONS ================")
print(" MULTIVARIATE DISTANCE")
cProfile.run('spk.isi_distance(spike_trains)', 'performance.stat')
p = pstats.Stats('performance.stat')
p.strip_dirs().sort_stats(sort_by).print_stats(5)
import pstats
# in case you dont have the cython backends, disable the warnings as follows:
# spk.disable_backend_warning = True
M = 100 # number of spike trains
r = 1.0 # rate of Poisson spike times
T = 1E3 # length of spike trains
print("%d spike trains with %d spikes" % (M, int(r*T)))
spike_trains = []
t_start = datetime.now()
for i in range(M):
spike_trains.append(spk.generate_poisson_spikes(r, T))
t_end = datetime.now()
runtime = (t_end-t_start).total_seconds()
sort_by = 'tottime'
# sort_by = 'cumtime'
print("Spike generation runtime: %.3fs" % runtime)
print()
print("================ ISI COMPUTATIONS ================")
print(" MULTIVARIATE DISTANCE")
cProfile.run('spk.isi_distance(spike_trains)', 'performance.stat')
p = pstats.Stats('performance.stat')
p.strip_dirs().sort_stats(sort_by).print_stats(5)
import numpy as np
from matplotlib import pyplot as plt
import pyspike as spk
st1 = spk.generate_poisson_spikes(1.0, [0, 20])
st2 = spk.generate_poisson_spikes(1.0, [0, 20])
d = spk.spike_directionality(st1, st2)
print "Spike Directionality of two Poissonian spike trains:", d
E = spk.spike_train_order_profile(st1, st2)
plt.figure()
x, y = E.get_plottable_data()
plt.plot(x, y, '-ob')
plt.ylim(-1.1, 1.1)
plt.xlabel("t")
plt.ylabel("E")
plt.title("Spike Train Order Profile")
###### Optimize spike train order of 20 Random spike trains #######
M = 20
spike_trains = [spk.generate_poisson_spikes(1.0, [0, 100]) for m in xrange(M)]
F_init = spk.spike_train_order(spike_trains)