def test_active_info_not_fixed_size(self):
"""
Raise a ``ValueError`` if the provided network is not fixed sized, and
the ``size`` argument is ``None``
"""
with self.assertRaises(ValueError):
active_information(ECA(30), k=3, timesteps=10, local=False)
active_information(ECA(30), k=3, timesteps=10, size=5, local=False)
def test_entropy_rate_not_fixed_size(self):
"""
Raise a ``ValueError`` if the provided network is not fixed sized, and
the ``size`` argument is ``None``
"""
with self.assertRaises(ValueError):
entropy_rate(ECA(30), k=3, timesteps=10, local=False)
entropy_rate(ECA(30), k=3, timesteps=10, size=5, local=False)
def test_attractors_eca(self):
networks = [(ECA(30), 2, 3), (ECA(30), 3, 1), (ECA(30), 4, 4),
(ECA(30), 5, 2), (ECA(30), 6, 3), (ECA(110), 2, 1),
(ECA(110), 3, 1), (ECA(110), 4, 3), (ECA(110), 5, 1),
(ECA(110), 6, 3)]
for rule, width, size in networks:
landscape = Landscape(rule, size=width)
self.assertEqual(size, len(landscape.attractors))
def test_attractors_eca(self):
"""
test ``attractors`` on ECA
"""
networks = [(ECA(30), 2, 3), (ECA(30), 3, 1), (ECA(30), 4, 4),
(ECA(30), 5, 2), (ECA(30), 6, 3), (ECA(110), 2, 1),
(ECA(110), 3, 1), (ECA(110), 4, 3), (ECA(110), 5, 1),
(ECA(110), 6, 3)]
for rule, width, size in networks:
self.assertEqual(size, len(attractors(rule, width)))
def test_timeseries_eca(self):
rule = ECA(30)
for size in [5, 7, 11]:
landscape = Landscape(rule, size=size)
time = 10
series = landscape.timeseries(time)
self.assertEqual((size, 2**size, time + 1), series.shape)
for index, state in enumerate(rule.state_space(size)):
traj = landscape.trajectory(state, timesteps=time)
for t, expect in enumerate(traj):
got = series[:, index, t]
self.assertTrue(np.array_equal(expect, got))
def test_reproduce_open_ecas(self):
"""
Ensure that RewiredECAs can reproduce open ECAs
"""
from neet.automata import ECA
reca = RewiredECA(30, boundary=(1, 0), size=7)
eca = ECA(30, boundary=(1, 0))
state = [0, 0, 0, 1, 0, 0, 0]
for _ in range(10):
expect = eca.update(np.copy(state))
got = reca.update(state)
self.assertTrue(np.array_equal(expect, got))
def test_basins_eca(self):
"""
test ``basins`` on ECAs
"""
networks = [(ECA(30), 2, [2, 1, 1]), (ECA(30), 3, [8]),
(ECA(30), 4, [2, 12, 1, 1]), (ECA(30), 5, [2, 30]),
(ECA(30), 6, [62, 1, 1]), (ECA(110), 2, [4]),
(ECA(110), 3, [8]), (ECA(110), 4, [4, 6, 6]),
(ECA(110), 5, [32]), (ECA(110), 6, [10, 27, 27])]
for net, width, basin_sizes in networks:
basin_counter = Counter([len(c) for c in basins(net, size=width)])
self.assertEqual(Counter(basin_sizes), basin_counter)
def test_basin_entropy_eca(self):
"""
test ``basin_entropy`` on ECAs
"""
networks = [(ECA(30), 2, 1.5), (ECA(30), 3, 0.),
(ECA(30), 4, 1.186278124459133),
(ECA(30), 5, 0.3372900666170139),
(ECA(30), 6, 0.23187232431271465), (ECA(110), 2, 0.),
(ECA(110), 3, 0.), (ECA(110), 4, 1.561278124459133),
(ECA(110), 5, 0.), (ECA(110), 6, 1.4690124052234232)]
for net, width, entropy in networks:
self.assertAlmostEqual(basin_entropy(net, size=width), entropy)
def test_basins_eca(self):
networks = [(ECA(30), 2, 3), (ECA(30), 3, 1), (ECA(30), 4, 4),
(ECA(30), 5, 2), (ECA(30), 6, 3), (ECA(110), 2, 1),
(ECA(110), 3, 1), (ECA(110), 4, 3), (ECA(110), 5, 1),
(ECA(110), 6, 3)]
for rule, width, size in networks:
landscape = Landscape(rule, size=width)
self.assertEqual(landscape.volume, len(landscape.basins))
self.assertEqual(size, 1 + np.max(landscape.basins))
unique = list(set(landscape.basins))
unique.sort()
self.assertEqual(list(range(size)), unique)
def test_is_state_space(self):
self.assertTrue(issubclass(Landscape, StateSpace))
space = s_pombe.state_space()
landscape = Landscape(s_pombe)
self.assertEqual(list(space), list(landscape))
self.assertEqual([space.encode(state) for state in space],
list(map(landscape.encode, landscape)))
ca = ECA(30)
space = ca.state_space(10)
landscape = Landscape(ca, size=10)
self.assertEqual(list(space), list(landscape))
self.assertEqual([space.encode(state) for state in space],
list(map(landscape.encode, landscape)))
def test_transitions_not_fixed_sized(self):
"""
``transitions`` should raise an error if ``net`` is not fixed sized
and ``size`` is ``None``
"""
with self.assertRaises(ValueError):
transitions(ECA(30), size=None)
def test_transitions_eca(self):
ca = ECA(30)
landscape = Landscape(ca, size=1)
self.assertEqual(ca, landscape.network)
self.assertEqual(1, landscape.size)
self.assertEqual([0, 0], list(landscape.transitions))
landscape = Landscape(ca, size=2)
self.assertEqual(ca, landscape.network)
self.assertEqual(2, landscape.size)
self.assertEqual([0, 1, 2, 0], list(landscape.transitions))
landscape = Landscape(ca, size=3)
self.assertEqual(ca, landscape.network)
self.assertEqual(3, landscape.size)
self.assertEqual([0, 7, 7, 1, 7, 4, 2, 0], list(landscape.transitions))
landscape = Landscape(ca, size=10)
trans = landscape.transitions
self.assertEqual(ca, landscape.network)
self.assertEqual(10, landscape.size)
self.assertEqual(1024, len(trans))
self.assertEqual([0, 515, 7, 517, 14, 525, 11, 521, 28, 543],
list(trans[:10]))
self.assertEqual([18, 16, 13, 14, 10, 8, 7, 4, 2, 0],
list(trans[-10:]))
def test_basin_entropy_variable_sized(self):
"""
``basin_entropy`` should raise an error if ``net`` is a variable
sized network and ``size`` is ``None``
"""
with self.assertRaises(ValueError):
basin_entropy(ECA(30), size=None)
def test_timeseries_too_short(self):
landscape = Landscape(ECA(30), size=3)
with self.assertRaises(ValueError):
landscape.timeseries(-1)
with self.assertRaises(ValueError):
landscape.timeseries(0)
def test_attractors_variable_sized(self):
"""
``attractors`` should raise an error if ``net`` is a variable sized
network and ``size`` is ``None``
"""
with self.assertRaises(ValueError):
attractors(ECA(30), size=None)
def test_transition_graph_eca(self):
"""
test ``transitions_graph`` on ``ECA``
"""
graph = transition_graph(ECA(30), size=8)
self.assertEqual(256, graph.number_of_nodes())
self.assertEqual(256, graph.number_of_edges())
def test_transition_graph_variable_sized(self):
"""
``transitions_graph`` should raise an error if ``net`` is variable
sized and ``size`` is ``None``
"""
with self.assertRaises(ValueError):
transition_graph(ECA(30))
def test_trajectory_too_short(self):
landscape = Landscape(ECA(30), size=3)
with self.assertRaises(ValueError):
landscape.trajectory([0, 0, 0], timesteps=-1)
with self.assertRaises(ValueError):
landscape.trajectory([0, 0, 0], timesteps=0)
def test_timeseries_variable_sized(self):
"""
``timeseries`` should raise an error if ``net`` is variable sized and
``size`` is ``None``
"""
with self.assertRaises(ValueError):
timeseries(ECA(30), size=None, timesteps=5)
def test_trajectory_eca(self):
landscape = Landscape(ECA(30), size=3)
with self.assertRaises(ValueError):
landscape.trajectory([])
with self.assertRaises(ValueError):
landscape.trajectory([0, 1])
xs = [0, 1, 0]
got = landscape.trajectory(xs)
self.assertEqual([0, 1, 0], xs)
self.assertEqual([[0, 1, 0], [1, 1, 1], [0, 0, 0]], got)
got = landscape.trajectory(xs, timesteps=5)
self.assertEqual([0, 1, 0], xs)
self.assertEqual(
[[0, 1, 0], [1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
got)
got = landscape.trajectory(xs, encode=False)
self.assertEqual([0, 1, 0], xs)
self.assertEqual([[0, 1, 0], [1, 1, 1], [0, 0, 0]], got)
got = landscape.trajectory(xs, timesteps=5, encode=False)
self.assertEqual([0, 1, 0], xs)
self.assertEqual(
[[0, 1, 0], [1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
got)
got = landscape.trajectory(xs, encode=True)
self.assertEqual([0, 1, 0], xs)
self.assertEqual([2, 7, 0], list(got))
got = landscape.trajectory(xs, timesteps=5, encode=True)
self.assertEqual([0, 1, 0], xs)
self.assertEqual([2, 7, 0, 0, 0, 0], list(got))
xs = 2
got = landscape.trajectory(xs)
self.assertEqual([2, 7, 0], list(got))
got = landscape.trajectory(xs, timesteps=5)
self.assertEqual([2, 7, 0, 0, 0, 0], list(got))
got = landscape.trajectory(xs, encode=True)
self.assertEqual([2, 7, 0], list(got))
got = landscape.trajectory(xs, timesteps=5, encode=True)
self.assertEqual([2, 7, 0, 0, 0, 0], list(got))
got = landscape.trajectory(xs, encode=False)
self.assertEqual([[0, 1, 0], [1, 1, 1], [0, 0, 0]], got)
got = landscape.trajectory(xs, timesteps=5, encode=False)
self.assertEqual(
[[0, 1, 0], [1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
got)
def test_attractor_lengths(self):
for code in [30, 110, 21, 43]:
for size in range(2, 7):
landscape = Landscape(ECA(code), size=size)
lengths = list(map(len, landscape.attractors))
self.assertEqual(lengths, list(landscape.attractor_lengths))
for net in [s_pombe, s_cerevisiae, c_elegans]:
landscape = Landscape(net)
lengths = list(map(len, landscape.attractors))
self.assertEqual(lengths, list(landscape.attractor_lengths))
def test_basin_entropy_eca(self):
networks = [(ECA(30), 2, 1.500000, 0.451545),
(ECA(30), 3, 0.000000, 0.000000),
(ECA(30), 4, 1.186278, 0.357105),
(ECA(30), 5, 0.337290, 0.101534),
(ECA(30), 6, 0.231872, 0.069801),
(ECA(110), 2, 0.000000, 0.000000),
(ECA(110), 3, 0.000000, 0.000000),
(ECA(110), 4, 1.561278, 0.469992),
(ECA(110), 5, 0.000000, 0.000000),
(ECA(110), 6, 1.469012, 0.442217)]
for net, width, base2, base10 in networks:
landscape = Landscape(net, size=width)
self.assertAlmostEqual(base2, landscape.basin_entropy(), places=6)
self.assertAlmostEqual(base2,
landscape.basin_entropy(base=2),
places=6)
self.assertAlmostEqual(base10,
landscape.basin_entropy(base=10),
places=6)
def test_transitions_eca_encoded(self):
"""
test ``transitions`` on ECAs; encoding the states
"""
rule30 = ECA(30)
got = transitions(rule30, size=1, encode=True)
self.assertEqual([0, 0], got)
got = transitions(rule30, size=2, encode=True)
self.assertEqual([0, 1, 2, 0], got)
got = transitions(rule30, size=3, encode=True)
self.assertEqual([0, 7, 7, 1, 7, 4, 2, 0], got)
def test_transitions_eca(self):
"""
test ``transitions`` on ECAs; encoding the states
"""
rule30 = ECA(30)
got = transitions(rule30, size=1)
self.assertEqual([[0], [0]], got)
got = transitions(rule30, size=2)
self.assertEqual([[0, 0], [1, 0], [0, 1], [0, 0]], got)
got = transitions(rule30, size=3)
self.assertEqual([[0, 0, 0], [1, 1, 1], [1, 1, 1], [1, 0, 0],
[1, 1, 1], [0, 0, 1], [0, 1, 0], [0, 0, 0]], got)
def test_in_degree(self):
for code in [30, 110, 21, 43]:
for size in range(2, 7):
landscape = Landscape(ECA(code), size=size)
in_degrees = np.empty(landscape.volume, dtype=np.int)
for i in range(landscape.volume):
in_degrees[i] = np.count_nonzero(
landscape.transitions == i)
self.assertEqual(list(in_degrees), list(landscape.in_degrees))
for net in [s_pombe, s_cerevisiae, c_elegans]:
landscape = Landscape(net)
in_degrees = np.empty(landscape.volume, dtype=np.int)
for i in range(landscape.volume):
in_degrees[i] = np.count_nonzero(landscape.transitions == i)
self.assertEqual(list(in_degrees), list(landscape.in_degrees))
def test_transitions_eca_values(self):
ca = ECA(30)
landscape = Landscape(ca, size=3, values={0: 1})
self.assertEqual(ca, landscape.network)
self.assertEqual(3, landscape.size)
self.assertEqual([1, 7, 7, 1, 7, 5, 3, 1], list(landscape.transitions))
landscape = Landscape(ca, size=3, values={1: 0})
self.assertEqual(ca, landscape.network)
self.assertEqual(3, landscape.size)
self.assertEqual([0, 5, 5, 1, 5, 4, 0, 0], list(landscape.transitions))
landscape = Landscape(ca, size=3, values={})
self.assertEqual(ca, landscape.network)
self.assertEqual(3, landscape.size)
self.assertEqual([0, 7, 7, 1, 7, 4, 2, 0], list(landscape.transitions))
def test_transitions_eca_pin(self):
ca = ECA(30)
landscape = Landscape(ca, size=3, pin=[1])
self.assertEqual(ca, landscape.network)
self.assertEqual(3, landscape.size)
self.assertEqual([0, 5, 7, 3, 5, 4, 2, 2], list(landscape.transitions))
landscape = Landscape(ca, size=3, pin=[0])
self.assertEqual(ca, landscape.network)
self.assertEqual(3, landscape.size)
self.assertEqual([0, 7, 6, 1, 6, 5, 2, 1], list(landscape.transitions))
landscape = Landscape(ca, size=3, pin=None)
self.assertEqual(ca, landscape.network)
self.assertEqual(3, landscape.size)
self.assertEqual([0, 7, 7, 1, 7, 4, 2, 0], list(landscape.transitions))
def test_transitions_eca_index(self):
ca = ECA(30)
landscape = Landscape(ca, size=3, index=1)
self.assertEqual(ca, landscape.network)
self.assertEqual(3, landscape.size)
self.assertEqual([0, 3, 2, 1, 6, 5, 6, 5], list(landscape.transitions))
landscape = Landscape(ca, size=3, index=0)
self.assertEqual(ca, landscape.network)
self.assertEqual(3, landscape.size)
self.assertEqual([0, 1, 3, 3, 5, 4, 6, 6], list(landscape.transitions))
landscape = Landscape(ca, size=3, index=None)
self.assertEqual(ca, landscape.network)
self.assertEqual(3, landscape.size)
self.assertEqual([0, 7, 7, 1, 7, 4, 2, 0], list(landscape.transitions))
def test_trajectory_eca_encoded(self):
"""
test ``trajectory`` on ECAs; encoding the states
"""
rule30 = ECA(30)
with self.assertRaises(ValueError):
trajectory(rule30, [], encode=True)
state = [0, 1, 0]
got = trajectory(rule30, state, encode=True)
self.assertEqual([0, 1, 0], state)
self.assertEqual([2, 7], got)
got = trajectory(rule30, state, timesteps=2, encode=True)
self.assertEqual([0, 1, 0], state)
self.assertEqual([2, 7, 0], got)
def test_trajectory_eca(self):
"""
test ``trajectory`` on ECAs
"""
rule30 = ECA(30)
with self.assertRaises(ValueError):
trajectory(rule30, [])
xs = [0, 1, 0]
got = trajectory(rule30, xs)
self.assertEqual([0, 1, 0], xs)
self.assertEqual([[0, 1, 0], [1, 1, 1]], got)
got = trajectory(rule30, xs, timesteps=2)
self.assertEqual([0, 1, 0], xs)
self.assertEqual([[0, 1, 0], [1, 1, 1], [0, 0, 0]], got)
