def test_max_infected_index(self):
"""
Tests index of maximum infected during simulation.
"""
G = nx.barabasi_albert_graph(100, 5)
network = contagion.ContactNetwork(G, fraction_infected=0.25)
sim = contagion.Contagion(network, save_history=True)
self.assertGreater(sim.run_simulation_get_max_infected_index(), -1)
def test_init_histories(self):
"""
Tests initiation of simulation history.
"""
G = nx.barabasi_albert_graph(100, 5)
network = contagion.ContactNetwork(G, fraction_infected=0.25)
sim = contagion.Contagion(network, save_history=True)
self.assertEqual(len(sim.Su_hist), 1)
def test_run_simulation(self):
"""
Tests that the simulation runs for an appropriate number of steps.
"""
G = nx.barabasi_albert_graph(100, 5)
network = contagion.ContactNetwork(G, fraction_infected=0.25)
sim = contagion.Contagion(network, save_history=True)
sim.run_simulation()
self.assertGreater(len(sim.In_hist), 4)
def test_init_Re(self):
"""
Tests initialization of Recovered compartment.
"""
G = nx.barabasi_albert_graph(100, 5)
network = contagion.ContactNetwork(G,
fraction_infected=0.5,
fraction_recovered=0.35)
self.assertEqual(np.sum(network.Re), 35)
def test_single_omega(self):
"""
Tests running of a simulation with a Re-to-Su probability.
"""
G = nx.barabasi_albert_graph(100, 5)
network = contagion.ContactNetwork(G, fraction_infected=0.25)
sim = contagion.Contagion(network, beta=0.5, omega=0.05)
sim.run_simulation()
self.assertGreater(len(sim.In_hist), 4)
def test_time_varying_beta(self):
"""
Tests that the simulation correctly initializes time-varying
transmission rates as a list.
"""
G = nx.barabasi_albert_graph(100, 5)
network = contagion.ContactNetwork(G, fraction_infected=0.25)
sim = contagion.Contagion(network, beta=[1., 0., 0.5, 1., 0., 0.5])
self.assertIsInstance(sim.beta_queue, list)
def test_generate_random_walk_degrees_length(self):
"""
Tests the length of the generated random degree sequence.
"""
G = nx.barabasi_albert_graph(100, 5)
network = contagion.ContactNetwork(G,
fraction_infected=0.5,
fraction_recovered=0.35)
walk = network.generate_random_walk_degree_sequence(10)
self.assertEqual(len(walk), 10)
def test_reset_Su_In_Re(self):
"""
Tests reset of compartmental histories.
"""
G = nx.barabasi_albert_graph(100, 5)
network = contagion.ContactNetwork(G,
fraction_infected=0.5,
fraction_recovered=0.35)
network.In -= network.In
network.reset_Su_In_Re()
self.assertEqual(np.sum(network.In), 50)
def test_immunize_network_vaccinate(self):
"""
Tests that network immunization is working correctly.
"""
G = nx.barabasi_albert_graph(100, 5)
network = contagion.ContactNetwork(G,
fraction_infected=0.5,
fraction_recovered=0.35)
Im = copy.deepcopy(network.In)
np.random.shuffle(Im)
network.immunize_network(Im, efficacy=0.7)
self.assertEqual(np.sum(network.Im), np.sum(Im))
def test_multiple_omega(self):
"""
Tests running of a simulation with Re-to-Su and Im-to_Su probabilities.
"""
G = nx.barabasi_albert_graph(100, 5)
network = contagion.ContactNetwork(G, fraction_infected=0.25)
Im = copy.deepcopy(network.In)
np.random.shuffle(Im)
network.immunize_network(Im, efficacy=0.7)
sim = contagion.Contagion(network, beta=0.5, omega=(0.1, 0.05))
sim.run_simulation()
self.assertGreater(len(sim.In_hist), 4)
