net.add_edge(Edge(start='a', end='b', phase=phi1, attenuation=amp1, delay=1))
net.add_edge(Edge(start='b', end='c', phase=phi2, attenuation=amp2, delay=2))
net.add_edge(Edge(start='b', end='d', phase=3, attenuation=0.4, delay=3))
net.add_input(name='a', amplitude=1.0, phase=0)
net.visualize(path='./visualizations/symbolicfeedforward', format='svg')
net.evaluate(use_shared_default=False, feed_dict=None)
# 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'))
net.get_html_result(['c', 'd'],
path='./visualizations/symbolicfeedforward.html')
# Evaluation without feed dictionary, using the default value of each SymNum
waves = [
tuple([
w.eval(feed_dict=None, use_shared_default=False)
if hasattr(w, 'eval') else w for w in inner
]) for inner in net.get_result('c')
]
print('Waves arriving at c:', waves, '\n')
print(net.get_eval_result(name='c', feed_dict=None, use_shared_default=False))
# Evaluation without feed dictionary, with global defaults
waves = [
tuple([
w.eval(feed_dict=None, use_shared_default=True)
if hasattr(w, 'eval') else w for w in inner
# 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='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)
tb.add_input_sequence(node_name='a', x=x_in_a, t=t_in)
