# 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()
net.add_node(name='d')
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 = [
net.add_node(name='a')
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,
net = Network()
for node in nodes:
net.add_node(node)
for edge in edges:
net.add_edge(edge)
net.add_input('a', amplitude=1.0)
for i, edge in enumerate(net.edges):
edge.phase = np.random.uniform(0, 2 * np.pi)
####
# Evaluate Network
####
net.evaluate(amplitude_cutoff=5e-3, max_endpoints=1e6, use_shared_default=True)
waves = [
tuple([w.eval() if hasattr(w, 'eval') else w for w in inner])
for inner in net.get_result('a')
]
####
# 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])
print('10 first waves arriving at a:', waves[:10])
net.print_stats()
#####
# Reset variable
#####
feed = {"Edge_ab": 0.8}
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
}) if hasattr(w, 'eval') else w for w in inner
]) for inner in net.get_result('a')
]
###Define Network
nodes = ['a', 'b', 'c', 'd']
edges = [
Edge(
'a', 'b', phase=0.5, attenuation=1.1, delay=1.0
), # some edges can have gain, but the overall gain of loops must be <1
Edge('b', 'c', phase=1, attenuation=0.9, delay=2.0),
Edge('c', 'd', phase=0.2, attenuation=0.98, delay=0.5),
Edge('d', 'a', phase=-0.5, attenuation=0.8, delay=1.5)
]
net = Network()
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/recurrent', format='svg')
####
#Evaluate Network
####
net.evaluate(amplitude_cutoff=1e-1, max_endpoints=1e6)
####
#Print data
####
print('paths leading to a:', net.get_paths('a'))
print('waves arriving at a:', net.get_result('a'))
net.print_stats()
