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])
def test_weighted_control_phenotype_pop(bowtie_database):
pop = bowtie_database.population
t = bowtie_database.targets[0]
tset = t.calc_distinct_outputs()
anz = OutputControlAnalyzer(pop.factory.world, tset)
cy_info = numpy.asarray(anz.analyse_collection(pop))
for i, net in enumerate(pop):
wc = get_weighted_control_phenotype(net, tset)
assert (cy_info[i][:, 0] >= cy_info[i][:, 2]).all()
numpy.testing.assert_allclose(wc, cy_info[i][:, 2])
# # Let's just do 50.
if i > 50:
break
def test_average_control_phenotype_pop(bowtie_database):
pop = bowtie_database.population
anz = OutputControlAnalyzer(pop.factory.world)
cy_info = numpy.asarray(anz.analyse_collection(pop))
for i, net in enumerate(pop):
probs, ents = get_average_control_phenotype(net)
py_info = numpy.asarray([[p, e] for (p, e) in zip(probs, ents)])
numpy.testing.assert_allclose(py_info, cy_info[i][:, :2])
assert not numpy.any(numpy.isnan(cy_info[1]))
# # Let's just do 50.
if i > 50:
break