def test_derived_outputs_whitelist():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.run()
model.outputs = np.array(
[
[990, 10, 0],
[980, 15, 5],
[970, 20, 10],
[960, 25, 15],
[950, 30, 20],
[940, 35, 25],
]
)
model.request_output_for_compartments("recovered", ["R"])
model.request_output_for_compartments("not_infected", ["S", "R"])
model.request_output_for_compartments("infected", ["I"])
model.request_output_for_compartments("total_population", ["S", "I", "R"])
model.request_cumulative_output(name="accum_infected", source="infected")
model.set_derived_outputs_whitelist(["recovered", "accum_infected"])
dos = model._calculate_derived_outputs()
assert "recovered" in dos # Included coz in whitelist (or dependency of)
assert "infected" in dos # Included coz in whitelist (or dependency of)
assert "accum_infected" in dos # Included coz in whitelist (or dependency of)
assert "not_infected" not in dos # Excluded coz not in whitelist
assert "total_population" not in dos # Excluded coz not in whitelist
assert_array_equal(dos["recovered"], np.array([0, 5, 10, 15, 20, 25]))
assert_array_equal(dos["infected"], np.array([10, 15, 20, 25, 30, 35]))
assert_array_equal(dos["accum_infected"], np.array([10, 25, 45, 70, 100, 135]))
def test_functional_derived_outputs():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.run()
model.outputs = np.array(
[
[990, 10, 0],
[980, 15, 5],
[970, 20, 10],
[960, 25, 15],
[950, 30, 20],
[940, 35, 25],
]
)
def get_non_infected(rec, pop):
return rec / pop
model.request_output_for_compartments("recovered", ["R"])
model.request_output_for_compartments("population", ["S", "I", "R"])
model.request_function_output("recovered_prop", get_non_infected, ["recovered", "population"])
dos = model._calculate_derived_outputs()
assert_array_equal(dos["recovered_prop"], np.array([0, 0.005, 0.01, 0.015, 0.020, 0.025]))
def test_no_derived_outputs():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.run()
# 6 timesteps, 3 compartments.
assert model.outputs.shape == (6, 3)
assert model.derived_outputs == {}
def test_flow_derived_outputs():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 900, "I": 100})
# Constant entry.
model.add_importation_flow("imports", num_imported=2, dest="S")
model.request_output_for_flow(name="importation", flow_name="imports")
model.request_output_for_flow(name="importation_raw", flow_name="imports", raw_results=True)
# Linear entry.
model.add_importation_flow("imports_land", num_imported=lambda t: 3 * t, dest="S")
model.request_output_for_flow(name="importation_land", flow_name="imports_land")
# Quadratic entry.
model.add_importation_flow("imports_air", num_imported=lambda t: t ** 2, dest="S")
model.request_output_for_flow(name="importation_air", flow_name="imports_air")
# Fractional transition flow
model.add_transition_flow("recovery", 0.1, "I", "R")
model.request_output_for_flow(name="recovery_raw", flow_name="recovery", raw_results=True)
model.request_output_for_flow(name="recovery_delta", flow_name="recovery", raw_results=False)
model.run()
dos = model.derived_outputs
# Raw outputs are the instantaneous flow rate at a given time.
assert_allclose(dos["recovery_raw"], model.outputs[:, 1] * 0.1, rtol=0.01)
# Post-processed outputs reflect changes in compartment size.
recovered_count = np.zeros_like(model.outputs[:, 2])
recovered_count[1:] = np.diff(model.outputs[:, 2])
assert_allclose(dos["recovery_delta"][1:], recovered_count[1:], rtol=0.01)
# Good match for constant
assert_array_equal(dos["importation"][1:], np.array([2, 2, 2, 2, 2]))
assert_array_equal(dos["importation_raw"], np.array([2, 2, 2, 2, 2, 2]))
# Good match for linear
assert_allclose(dos["importation_land"][1:], np.array([1.5, 4.5, 7.5, 10.5, 13.5]))
# So-so match for quadratic
assert_allclose(dos["importation_air"][1:], np.array([0.5, 2.5, 6.5, 12.5, 20.5]), rtol=0.1)
def test_derived_outputs_with_no_save_results():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.add_importation_flow("imports", num_imported=2, dest="S")
# Expect np.array([0, 2, 2, 2, 2, 2]))
model.request_output_for_flow(name="importation", flow_name="imports", save_results=False)
# Expect np.array([0, 5, 10, 15, 20, 25]))
model.request_output_for_compartments("recovered", ["R"], save_results=False)
# Expect np.array([0, 5, 15, 30, 50, 75]))
model.request_cumulative_output(
name="recovered_cumulative", source="recovered", save_results=False
)
# Expect np.array([0, 7, 12, 17, 22, 227]))
model.request_aggregate_output(
name="some_aggregate", sources=["recovered", "importation"], save_results=False
)
# Expect np.array([ 0, 12, 27, 47, 72, 102])
model.request_aggregate_output(
name="final_aggregate", sources=["some_aggregate", "recovered_cumulative"]
)
model.run()
# Override outputs so the test is easier to write
model.outputs = np.array(
[
[990, 10, 0],
[980, 15, 5],
[970, 20, 10],
[960, 25, 15],
[950, 30, 20],
[940, 35, 25],
]
)
dos = model._calculate_derived_outputs()
assert_array_equal(dos["final_aggregate"][1:], np.array([12, 27, 47, 72, 102]))
assert "importation" not in dos
assert "recovered" not in dos
assert "recovered_cumulative" not in dos
assert "some_aggregate" not in dos
def test_cumulative_derived_outputs():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.run()
model.outputs = np.array(
[
[990, 10, 0],
[980, 15, 5],
[970, 20, 10],
[960, 25, 15],
[950, 30, 20],
[940, 35, 25],
]
)
model.request_output_for_compartments("recovered", ["R"])
model.request_cumulative_output(name="recoved_cumulative", source="recovered")
model.request_cumulative_output(name="recoved_cumulative_2", source="recovered", start_time=2)
dos = model._calculate_derived_outputs()
assert_array_equal(dos["recoved_cumulative"], np.array([0, 5, 15, 30, 50, 75]))
assert_array_equal(dos["recoved_cumulative_2"], np.array([0, 0, 10, 25, 45, 70]))
def test_aggregate_derived_outputs():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.run()
model.outputs = np.array(
[
[990, 10, 0],
[980, 15, 5],
[970, 20, 10],
[960, 25, 15],
[950, 30, 20],
[940, 35, 25],
]
)
model.request_output_for_compartments("recovered", ["R"])
model.request_output_for_compartments("not_infected", ["S", "R"])
model.request_output_for_compartments("total_population", ["S", "I", "R"])
model.request_aggregate_output(name="my_aggregate", sources=["recovered", "total_population"])
dos = model._calculate_derived_outputs()
assert_array_equal(dos["my_aggregate"], np.array([1000, 1005, 1010, 1015, 1020, 1025]))