def test_final_size_calculation():
#Tests the calculation of the final size epidemic against the manually calculated value
simulation = SIR_Selke(200, 0.008, 1, 5)
#Generated the infection periods
npr.seed(1)
T = simulation.inf_periods
res_to_inf = npr.exponential(1, 200)
#There is a 4 here because python starts counting at zero, so this sets the first 5 to 0.
res_to_inf[:4] = 0
res_to_inf = np.sort(res_to_inf)
total_force_of_infection = 0.008 * np.cumsum(T)
#Calcualte the first time that the total force of infection is exceeded by resistance
counter = 0
for i in range(200):
if res_to_inf[i] < total_force_of_infection[i]:
counter = counter + 1
else:
break
npr.seed(1)
test_var = simulation.compute_final_size()
assert test_var == counter
def test_data_gen_simple():
#We do not specify the distribution, we check that the function draws the correct vector from the exponential distribution
#the parameter for the infectious periods is set to 1.5
npr.seed(1)
simulation = SIR_Selke(200, 0.008, 1.5, 5)
test_var = simulation.inf_periods
npr.seed(1)
test_var2 = npr.exponential(1.5, 200)
assert all(test_var2 == test_var)
def test_data_gen_non_markovian_int_parameter():
npr.seed(1)
simulation = SIR_Selke(200,
0.008,
1,
5,
infection_period_distribution=npr.exponential)
test_var = simulation.inf_periods
npr.seed(1)
test_var2 = npr.exponential(1, 200)
assert all(test_var2 == test_var)
def test_data_gen_non_markovian_1_par():
npr.seed(1)
simulation = SIR_Selke(200,
0.008,
0.9,
5,
infection_period_distribution=npr.geometric)
test_var = simulation.inf_periods
npr.seed(1)
test_var2 = npr.geometric(0.9, 200)
assert all(test_var2 == test_var)
def test_final_size_using_hazard_function():
'''
Tests whether the final size of the epidemic is the same when using the
hazard rate integration for exponential rate, as it is when we precaculate the result
'''
npr.seed(1)
def my_hazard(t):
return 1
simulation = SIR_Selke(200, 0.008, 1, 5, hazard_rate=my_hazard)
test_var_1 = simulation.compute_final_size()
npr.seed(1)
simulation = SIR_Selke(200, 0.008, 1, 5)
test_var_2 = simulation.compute_final_size()
assert test_var_1 == test_var_2
def test_repeated_sim():
#Tests that the results from performing repeated iterations of the final size calculation are as expected
npr.seed(1)
simulation = SIR_Selke(200, 0.008, 1, 5)
simulation.sim_final_size(2)
def test_error_unless_pos_distribution():
with raises(ValueError):
simulation = SIR_Selke(200,
0.008, [1, 1],
5,
infection_period_distribution=npr.normal)