def test_main():
text = """
A = True
B = Random
C = Random
D = Random
B* = A or C
C* = A and not D
D* = B and C
"""
repeat = 5000
coll = util.Collector()
for i in range(repeat):
update_progress(i, repeat)
model = boolean2.Model(text, mode='async')
model.initialize()
model.iterate(steps=15)
# in this case we take all nodes
# one could just list a few nodes such as [ 'A', 'B', 'C' ]
nodes = model.nodes
# this collects states for each run
coll.collect(states=model.states, nodes=nodes)
# this step averages the values for each node
# returns a dictionary keyed by nodes and a list of values
# with the average state for in each timestep
avgs = coll.get_averages(normalize=True)
# make some shortcut to data to for easier plotting
valueB = avgs["B"]
valueC = avgs["C"]
valueD = avgs["D"]
#
# plot the state of the nodes
#
p1, = plt.plot(valueB, 'ob-')
p2, = plt.plot(valueC, 'sr-')
p3, = plt.plot(valueD, '^g-')
plt.legend([p1, p2, p3], ["B", "C", "D"])
plt.show()
plt.savefig('t-05_figure.png')
def run(text, nodes, repeat, steps):
"""
Runs the simulation and collects the nodes into a collector,
a convenience class that can average the values that it collects.
"""
coll = util.Collector()
for i in xrange(repeat):
engine = Model(mode='async', text=text)
engine.RULE_GETVALUE = new_getvalue
# minimalist initial conditions, missing nodes set to false
engine.initialize(missing=util.false)
engine.iterate(steps=steps)
coll.collect(states=engine.states, nodes=nodes)
print '- completed'
avgs = coll.get_averages(normalize=True)
return avgs