def test_average_control_phenotype_net(bowtie_network):
net = bowtie_network
probs, ents = get_average_control_phenotype(net)
for p, e in zip(probs, ents):
# We should never have more information than there is to explain!
assert p <= e
onz = OutputControlAnalyzer(net.factory.world)
cy_info = onz.numpy_info_from_network(net)[0][:,:2]
py_info = numpy.asarray([[p, e] for (p, e) in zip(probs, ents)])
numpy.testing.assert_allclose(cy_info, py_info)
def test_weighted_control_phenotype(bowtie_database, bowtie_network):
net = bowtie_network
# net = bowtie_database.population[50]
t = bowtie_database.targets[0]
tset = t.calc_distinct_outputs()
onz = OutputControlAnalyzer(net.factory.world, tset)
cy_info = onz.numpy_info_from_network(net)[0]
probs, cats = get_causal_specs_phenotype(net, tset)
wc = get_weighted_control_phenotype(net, tset)
ac, ent = get_average_control_phenotype(net)
# Should always DECREASE
assert (wc <= ac).all()
numpy.testing.assert_allclose(wc, cy_info[:, 2])
