def test_apply_many_flows():
    """
    Expect multiple flows to operate independently and produce the correct final flow rate.
    """
    model = CompartmentalModel(times=[0, 5],
                               compartments=["S", "I", "R"],
                               infectious_compartments=["I"])
    model.set_initial_population(distribution={"S": 900, "I": 100})
    model.add_death_flow("infection_death", 0.1, "I")
    model.add_universal_death_flows("universal_death", 0.1)
    model.add_infection_frequency_flow("infection", 0.2, "S", "I")
    model.add_sojourn_flow("recovery", 10, "I", "R")
    model.add_transition_flow("vaccination", 0.1, "S", "R")
    model.add_crude_birth_flow("births", 0.1, "S")
    model._prepare_to_run()
    actual_flow_rates = model._get_compartment_rates(model.initial_population,
                                                     0)

    # Expect the effects of all these flows to be linearly superimposed.
    infect_death_flows = np.array([0, -10, 0])
    universal_death_flows = np.array([-90, -10, 0])
    infected = 900 * 0.2 * (100 / 1000)
    infection_flows = np.array([-infected, infected, 0])
    recovery_flows = np.array([0, -10, 10])
    vaccination_flows = np.array([-90, 0, 90])
    birth_flows = np.array([100, 0, 0])
    expected_flow_rates = (infect_death_flows + universal_death_flows +
                           infection_flows + recovery_flows +
                           vaccination_flows + birth_flows)
    assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_apply_infect_death_flows(inf_pop, exp_flow):
    model = CompartmentalModel(times=[0, 5],
                               compartments=["S", "I"],
                               infectious_compartments=["I"])
    model.set_initial_population(distribution={"I": inf_pop})
    model.add_death_flow("infection_death", 0.1, "I")
    model._prepare_to_run()
    actual_flow_rates = model._get_compartment_rates(model.initial_population,
                                                     0)
    # Expect 0.1 * inf_pop = exp_flow
    expected_flow_rates = np.array([0, -exp_flow])
    assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_apply_replace_death_birth_flow():
    model = CompartmentalModel(times=[0, 5],
                               compartments=["S", "I"],
                               infectious_compartments=["I"])
    model.set_initial_population(distribution={"S": 900, "I": 100})
    model.add_death_flow("infection_death", 0.1, "I")
    model.add_universal_death_flows("universal_death", 0.05)
    model.add_replacement_birth_flow("births", "S")
    model._prepare_to_run()
    actual_flow_rates = model._get_compartment_rates(model.initial_population,
                                                     0)
    # Expect 10 people to die and 10 to be born
    exp_i_flow_rate = -0.1 * 100 - 0.05 * 100
    exp_s_flow_rate = -exp_i_flow_rate  # N.B births + deaths in the S compartment should balance.
    expected_flow_rates = np.array([exp_s_flow_rate, exp_i_flow_rate])
    assert_array_equal(actual_flow_rates, expected_flow_rates)