def test_basic_1(self, N=1000):
"""
Create a network with sin(t) being represented by
a population of spiking neurons. Assert that the
decoded value from the population is close to the
true value (which is input to the population).
Expected duration of test: about .7 seconds
"""
net = Network('Runtime Test', dt=0.001, seed=123,
Simulator=self.Simulator)
print 'make_input'
net.make_input('in', value=np.sin)
print 'make A'
net.make('A', N, 1)
print 'connecting in -> A'
net.connect('in', 'A')
A_fast_probe = net.make_probe('A', dt_sample=0.01, pstc=0.001)
A_med_probe = net.make_probe('A', dt_sample=0.01, pstc=0.01)
A_slow_probe = net.make_probe('A', dt_sample=0.01, pstc=0.1)
in_probe = net.make_probe('in', dt_sample=0.01, pstc=0.0)
net.run(1.0)
target = np.sin(np.arange(0, 1000, 10) / 1000.)
target.shape = (100, 1)
for speed in 'fast', 'med', 'slow':
probe = locals()['A_%s_probe' % speed]
data = np.asarray(probe.get_data()).flatten()
plt.plot(data, label=speed)
in_data = np.asarray(in_probe.get_data()).flatten()
plt.plot(in_data, label='in')
plt.legend(loc='upper left')
#print in_probe.get_data()
#print net.sim.sim_step
if self.show:
plt.show()
# target is off-by-one at the sampling frequency of dt=0.001
print rmse(target, in_probe.get_data())
assert rmse(target, in_probe.get_data()) < .001
print rmse(target, A_fast_probe.get_data())
assert rmse(target, A_fast_probe.get_data()) < .1, (
rmse(target, A_fast_probe.get_data()))
print rmse(target, A_med_probe.get_data())
assert rmse(target, A_med_probe.get_data()) < .01
print rmse(target, A_slow_probe.get_data())
assert rmse(target, A_slow_probe.get_data()) < 0.1
