# 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(node)
for edge in edges:
net.add_edge(edge)
net.add_input('a', amplitude=1.0)
for edge in net.edges:
edge.attenuation = 0.75
edge.phase = np.random.uniform(0, 2 * np.pi)
net.visualize(path='./visualizations/mediumexample')
####
# Evaluate Network
####
net.evaluate(amplitude_cutoff=1e-3, max_endpoints=1e6)
####
# Print and plot
####
for node in net.nodes:
print('number of paths to ' + node + ':', len(net.get_paths(node)))
print('final path to a added:', net.get_paths('a')[-1])
net.print_stats()
phases = np.asarray([val[1] for val in net.get_result('a')])
phases = phases % 2 * np.pi
amplitudes = np.asarray([val[0] for val in net.get_result('a')])
plt.hist(phases, weights=amplitudes, bins=30)
plt.title("amplitude weighted, binned phase contributions to a")
plt.ylabel('amplitude')
plt.xlabel('phase')
plt.show()
for node in nodes:
net.add_node(node)
for edge in edges:
net.add_edge(edge)
net.add_input('a', amplitude=1.0)
net.visualize(path='./visualizations/symbolicrecurrent')
####
# Evaluate Network
####
net.evaluate(amplitude_cutoff=1e-2,
max_endpoints=1e6,
use_shared_default=False)
print('paths leading to a:', net.get_paths('a'))
waves = [
tuple([w.eval() if hasattr(w, 'eval') else w for w in inner])
for inner in net.get_result('a')
]
print('waves arriving at a:', waves, '\n')
net.print_stats()
####
# Inserting variable values
###
waves = [
tuple([
w.eval(feed_dict={
'amp1': .5,
'ph1': .2