def test_get_latex_result(self):
edge_1 = Edge('b', 'a', phase=0.5, attenuation=0.5, delay=2)
edge_2 = Edge('c', 'a', phase=-0.5, attenuation=1.5, delay=-1)
split_net = self.net
split_net.add_node('c')
split_net.add_edge(edge_1)
split_net.add_edge(edge_2)
split_net.add_input('b', amplitude=1)
split_net.add_input('c', amplitude=1)
split_net.evaluate()
self.assertEqual(split_net.get_latex_result('b'),'1\cdot\exp(j (0.0))\cdot b_{in}(t-0.0)')
edge_1 = Edge('a', 'b', phase=1, attenuation=0.4, delay=2)
edge_2 = Edge('b', 'c', phase=2, attenuation=0.3, delay=1.2)
edge_3 = Edge('c', 'a', phase=3, attenuation=0.2, delay=0)
loop_net = Network()
loop_net.add_node('a')
loop_net.add_node('b')
loop_net.add_node('c')
loop_net.add_edge(edge_1)
loop_net.add_edge(edge_2)
loop_net.add_edge(edge_3)
loop_net.add_input('a', amplitude=1)
loop_net.evaluate(amplitude_cutoff=1e-4)
self.assertEqual(loop_net.get_latex_result('b',precision=2),'0.4\cdot\exp(j (1))\cdot a_{in}(t-2)+0.0096\cdot\exp(j (7))\cdot a_{in}(t-5.2)+0.00023\cdot\exp(j (13))\cdot a_{in}(t-8.4)')
# Add nodes
net.add_node(name='a')
net.add_node(name='b')
net.add_node(name='c')
net.add_node(name='d')
# Add edges
net.add_edge(Edge(start='a', end='b', phase=1, attenuation=0.8, delay=1))
net.add_edge(Edge(start='b', end='c', phase=2, attenuation=0.6, delay=2))
net.add_edge(Edge(start='b', end='d', phase=3, attenuation=0.4, delay=3))
# Add input
net.add_input(name='a', amplitude=1.0, phase=0)
# Visualize the network
net.visualize(path='./visualizations/feedforward', format='svg')
# Evaluate the network
net.evaluate(amplitude_cutoff=1e-3)
# Compute output and show results
print('paths leading to c:', net.get_paths('c'))
print('paths leading to d:', net.get_paths('d'))
print('waves arriving at c:', net.get_result('c'))
print('waves arriving at d:', net.get_result('d'))
print('latex string for waves arriving at c:', net.get_latex_result('c'))
# render output in a html file
net.get_html_result(['c', 'd'],
precision=2,
path='./visualizations/feedforward.html')
net.add_node(name='b')
net.add_node(name='c')
net.add_edge(Edge(start='a', end='b', phase=2, attenuation=amp1, delay=1))
net.add_edge(Edge(start='b', end='c', phase=1, attenuation=amp2, delay=2))
net.add_edge(
Edge(start='c', end='a', phase=phi3, attenuation=0.5 * amp3, delay=3))
net.add_input('a')
net.add_input('b')
net.evaluate(amplitude_cutoff=0.001)
net.visualize(path='./visualizations/docdemo', format='png')
net.visualize(path='./visualizations/docdemo', format='svg')
print(net.get_result('b'))
print(net.get_latex_result('b', linebreak_limit=1))
net.get_html_result(['c', 'b'], path='./visualizations/docdemo_latex.html')
### Create a testbench with a feed dictionary
tb = Testbench(network=net,
timestep=0.05,
feed_dict={
'v1': 0.8,
'v2': 0.8,
'v3': 0.9,
'v4': 3
})
x_in_a = np.sin(np.linspace(0, 2 * np.pi,
400)) # create the input signal (Dimensino N)
t_in = np.linspace(0, 20,
num=401) # create the input time vector (Dimension N+1)
