def test_base_bmodel_no_hold():
"""Test the no hold option"""
n_nodes = 12
n_runs_pert = 16
bmodel = Bmodel(J=random_interaction_matrix(N=n_nodes))
bmodel.runs(16)
hold = False
for config in bmodel.steady_states.values:
for node_to_switch in bmodel.node_labels:
for switch_to in ["ON", "OFF"]:
bmodel.perturbe(initial_condition=config,
node_to_switch=node_to_switch,
switch_to=switch_to,
n_runs=n_runs_pert,
hold=hold)
# if node is not holded, then final state can or cannot be what we asked for
# this is difficult to test
checks = []
for switched_node in bmodel.node_labels:
for switch_to in ["ON", "OFF"]:
perturbations = bmodel.get_perturbations(
switched_node=switched_node, switch_to=switch_to, hold=hold)
checks.append((perturbations[switched_node] == {
"ON": -1,
"OFF": 1
}[switch_to]).values)
checks = list(np.concatenate(checks))
checks = sorted(list(set(checks)))
assert checks == [False, True]
def test_base_bmodel_get_perturbations():
"""Test that we retrieve the right number of perturbations"""
n_nodes = 16
n_runs_pert = 5
bmodel = Bmodel(J=random_interaction_matrix(N=n_nodes), maxT=4)
bmodel.runs(20)
# do the perturbations
for config in bmodel.steady_states.values:
for hold in [True, False]:
for node_to_switch in bmodel.node_labels:
for switch_to in ["ON", "OFF"]:
bmodel.perturbe(initial_condition=config,
node_to_switch=node_to_switch,
switch_to=switch_to,
n_runs=n_runs_pert,
hold=hold)
# count the perturbations
for hold in [True, False]:
for switched_node in bmodel.node_labels:
for switch_to in ["ON", "OFF"]:
perturbations = bmodel.get_perturbations(
switched_node=switched_node,
switch_to=switch_to,
hold=hold)
n_ics = (bmodel.steady_states[switched_node] == {
"ON": -1,
"OFF": 1
}[switch_to]).sum()
assert perturbations.shape == (n_ics * n_runs_pert, n_nodes)
def test_base_bmodel_hold():
"""Test the hold option"""
n_nodes = 12
n_runs_pert = 4
bmodel = Bmodel(J=random_interaction_matrix(N=n_nodes))
bmodel.runs(16)
hold = True
for config in bmodel.steady_states.values:
for node_to_switch in bmodel.node_labels:
for switch_to in ["ON", "OFF"]:
bmodel.perturbe(initial_condition=config,
node_to_switch=node_to_switch,
switch_to=switch_to,
n_runs=n_runs_pert,
hold=hold)
# If node is holded, its final state must be what we asked for
for switched_node in bmodel.node_labels:
for switch_to in ["ON", "OFF"]:
perturbations = bmodel.get_perturbations(
switched_node=switched_node, switch_to=switch_to, hold=hold)
assert np.all(perturbations[switched_node] == {
"ON": 1,
"OFF": -1
}[switch_to])
def test_base_bmodel_indicator_can_fail():
n_nodes = 16
J = random_interaction_matrix(N=n_nodes)
node_labels = ["node_%d" % i for i in range(n_nodes)]
indicator_nodes = ["node_a", "node_b"]
with pytest.raises(IndicatorError):
_ = Bmodel(J=J,
node_labels=node_labels,
indicator_nodes=indicator_nodes)
def test_base_bmodel_indicator_unique():
"""Test that we cannot pass non-unique indicators"""
n_nodes = 16
J = random_interaction_matrix(N=n_nodes)
node_labels = ["node_%d" % i for i in range(n_nodes)]
indicator_nodes = ["node_0", "node_1", "node_0"]
with pytest.raises(IndicatorError):
_ = Bmodel(J=J,
node_labels=node_labels,
indicator_nodes=indicator_nodes)
def test_generate_random_interaction_matrix_avk5_N100():
"""Test that the generate matrix has right avk."""
N = 100
avk = 5
avks = [
np.abs(random_interaction_matrix(N=N, avk=avk)).mean() * N
for _ in range(1000)
]
rel_err = np.abs(np.mean(avks) / avk - 1)
assert rel_err < 0.05
def test_generate_random_interaction_matrix_mu04():
"""Test that the generate matrix has right mu."""
N = 40
avk = 3
mu = 0.4
mus = []
for _ in range(1000):
J = random_interaction_matrix(N=N, avk=avk, mu=mu)
mus.append(np.sum(J == 1) / np.sum(J != 0))
rel_err = np.abs(np.mean(mus) / mu - 1)
assert rel_err < 0.01
def test_base_bmodel_indicator_is_set():
n_nodes = 16
node_labels = ["node_%d" % i for i in range(n_nodes)]
# let us get at least two indicator nodes
num_indicators = 0
while num_indicators <= 1:
J = random_interaction_matrix(N=n_nodes)
indicator_nodes = [
node for i, node in enumerate(node_labels) if np.all(J[:, i] == 0)
]
num_indicators = len(indicator_nodes)
bmodel = Bmodel(J=J,
node_labels=node_labels,
indicator_nodes=indicator_nodes)
assert bmodel.indicator_nodes == list(indicator_nodes)
def test_generate_random_interaction_matrix_connected_N100():
"""Test that the generate matrix is connected."""
J = random_interaction_matrix(N=100, avk=3, mu=0.8, connected=True)
n, _ = connected_components(np.abs(J))
assert n == 1
def test_generate_random_interaction_matrix_N():
"""Test that the generate matrix has right N."""
J = random_interaction_matrix(N=100, avk=3, mu=0.8, connected=True)
assert J.shape == (100, 100)
def random_small():
N = 10
J = random_interaction_matrix(N=N).astype(float)
J_pseudo = np.identity(N) + 2 * J
return N, J, J_pseudo
