def test_vector_input_constant(self):
# Adjust these values to change the matrix dimensions
# Matrix A is D1xD2
# Matrix B is D2xD3
# result is D1xD3
D1 = 1
D2 = 2
seed = 123
N = 50
net=Network('Matrix Multiplication', seed=seed,
Simulator=self.Simulator)
# make 2 matrices to store the input
print "make_array: input matrices A and B"
net.make_array('A', neurons=N, array_size=D1*D2,
neuron_type='lif')
# connect inputs to them so we can set their value
net.make_input('input A', value=[.5, -.5])
net.connect('input A', 'A')
inprobe = net.make_probe('input A', dt_sample=0.01, pstc=0.1)
sprobe = net._probe_signals(
net.ensembles['A'].input_signals, dt_sample=0.01, pstc=0.01)
Aprobe = net.make_probe('A', dt_sample=0.01, pstc=0.1)
net.run(1)
in_data = inprobe.get_data()
s_data = sprobe.get_data()
A_data = Aprobe.get_data()
plt.subplot(311); plt.plot(in_data)
plt.subplot(312); plt.plot(s_data)
plt.subplot(313); plt.plot(A_data)
if self.show:
plt.show()
assert np.allclose(in_data[-10:], [.5, -.5], atol=.01, rtol=.01)
assert np.allclose(s_data[-10:], [.5, -.5], atol=.01, rtol=.01)
assert np.allclose(A_data[-10:], [.5, -.5], atol=.01, rtol=.01)
