def test_class_Chain():
"""tests to ensure the behaviour class Chain"""
test_model = Model('test_model')
from_pop = Population('from_pop', 0.)
to_pop = Population('to_pop', 0.)
pop_a = Population('pop_a', 0.)
pop_b = Population('pop_b', 0.)
start = 100000
from_pop.future = [start]
mean = 10.
sigma = 2.
delay_pars = {'mean': Parameter('mean', mean, parameter_min=0.1, parameter_max=100.),
'sigma': Parameter('sigma', sigma, parameter_min=0.1, parameter_max=1000.)}
delay = Delay('delay', 'norm', delay_pars, test_model)
frac = 0.8
fraction = Parameter('frac', frac)
chain = []
chain.append(Propagator('prop_0', from_pop, pop_a, fraction, delay))
chain.append(Propagator('prop_1', pop_a, pop_b, fraction, delay))
test_chain = Chain('test_chain', from_pop, to_pop, chain, fraction, delay, test_model)
test_model.add_connector(test_chain)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
for func in [test_chain.update_expectation, test_chain.update_data]:
EPS = 0.02
if func == test_chain.update_data:
EPS = 0.2
to_pop.reset()
pop_a.reset()
pop_b.reset()
func()
for pop in [to_pop, pop_b]:
distribution = pop.future
total = start * (1. - frac ** 2) * frac
ave = mean
std_dev = sigma
if pop == pop_b:
total = start * frac ** 2
ave = 2. * mean
std_dev = np.sqrt(2.) * sigma
sum_p = 0.
sum_tp = 0.
sum_ttp = 0.
for i in range(len(distribution)):
sum_p += distribution[i]
sum_tp += i * distribution[i] * time_step
sum_ttp += i * i * distribution[i] * time_step ** 2
assert np.abs(sum_p - total) < EPS * total
est_mean = sum_tp / total
assert np.abs(est_mean - ave) < EPS * ave
est_sigma = np.sqrt(sum_ttp / total - est_mean ** 2)
assert np.abs(est_sigma - std_dev) < EPS * std_dev
def test_class_Splitter():
"""tests to ensure the behaviour class Splitter"""
test_model = Model('test_model')
start = 100000
from_pop = Population('from_pop', 0)
from_pop.future = [start]
to_pops = [Population('to_pop1', 0.), Population('to_pop2', 0.)]
fracs = [0.4, 0.6]
fraction = Parameter('frac', fracs[0])
mean = 10.
std_dev = 4.
delay_pars = {
'mean': Parameter('mean', mean, parameter_min=-100., parameter_max=100.),
'sigma': Parameter('sigma', std_dev, parameter_min=-100., parameter_max=100.)
}
test_delay = Delay('test_delay', 'norm', delay_parameters=delay_pars, model=test_model)
test_split = Splitter('test_prop', from_pop, to_pops, [fraction], test_delay)
test_model.add_connector(test_split)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
for func in [test_split.update_expectation, test_split.update_data]:
EPS = 0.01
if func == test_split.update_data:
EPS = 0.1
to_pops[0].reset()
to_pops[1].reset()
func()
total = 0
for i in range(2):
distribution = to_pops[i].future
frac = fracs[i]
sum_p = 0.
sum_tp = 0.
sum_ttp = 0.
for i in range(len(distribution)):
sum_p += distribution[i]
sum_tp += i * distribution[i] * time_step
sum_ttp += i * i * distribution[i] * time_step ** 2
total += sum_p
assert np.abs(sum_p - start * frac) < EPS * start * frac
est_mean = sum_tp / (start * frac)
assert np.abs(est_mean - mean) < EPS * mean
est_sigma = np.sqrt(sum_ttp / (start * frac) - est_mean ** 2)
assert np.abs(est_sigma - std_dev) < EPS * std_dev
assert np.abs(total - start) < 0.1
def test_class_Multiplier():
"""tests to ensure the behaviour class Multiplier"""
test_model = Model('test_model')
EPS = 1.
n1 = 50.
n2 = 20.
n3 = 2.
scale = 0.1
f_pops = [Population('f1_pop', n1), Population('f2_pop', n2), Population('f3_pop', n3)]
to_pop = Population('to_pop', 0.)
scale_par = Parameter('alpha', scale)
delay = Delay('fast', 'fast')
test_multiplier = Multiplier('test_multiplier', f_pops, to_pop, scale_par, delay, model=test_model)
test_model.add_connector(test_multiplier)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
# expectation:
expected = n1 * n2 / n3 * scale * time_step
to_pop.reset()
test_multiplier.set_distribution('poisson', None)
test_multiplier.update_expectation()
assert to_pop.future[0] == expected
# Poisson
n_rep = 1000
n_list = []
for i in range(n_rep):
to_pop.reset()
test_multiplier.update_data()
n_list.append(to_pop.future[0])
assert np.abs(np.mean(n_list) - expected) < EPS
assert np.abs(np.std(n_list) - np.sqrt(expected)) < EPS
# Negative binomial
p_nb = 0.2
nbinom_par = Parameter('nb', p_nb)
test_multiplier.set_distribution('nbinom', nbinom_par)
n_rep = 1000
n_list = []
for i in range(n_rep):
to_pop.reset()
test_multiplier.update_data()
n_list.append(to_pop.future[0])
assert np.abs(np.mean(n_list) - expected) < EPS
assert np.abs(np.std(n_list) - np.sqrt(expected / p_nb)) < EPS
def test_class_Propagator():
"""tests to ensure the behaviour class Propagator"""
test_model = Model('test_model')
start = 100000
from_pop = Population('from_pop', 0)
from_pop.future = [start]
to_pop = Population('to_pop', 0.)
frac = 0.4
fraction = Parameter('frac', frac)
mean = 10.
std_dev = 4.
delay_pars = {
'mean': Parameter('mean', mean, parameter_min=-100., parameter_max=100.),
'sigma': Parameter('sigma', std_dev, parameter_min=-100., parameter_max=100.)
}
test_delay = Delay('test_delay', 'norm', delay_parameters=delay_pars, model=test_model)
test_prop = Propagator('test_prop', from_pop, to_pop, fraction, test_delay)
test_model.add_connector(test_prop)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
for func in [test_prop.update_expectation, test_prop.update_data]:
EPS = 0.01
if func == test_prop.update_data:
EPS = 0.1
to_pop.reset()
func()
distribution = to_pop.future
sum_p = 0.
sum_tp = 0.
sum_ttp = 0.
for i in range(len(distribution)):
sum_p += distribution[i]
sum_tp += i * distribution[i] * time_step
sum_ttp += i * i * distribution[i] * time_step ** 2
assert np.abs(sum_p - start * frac) < EPS * start * frac
est_mean = sum_tp / (start * frac)
assert np.abs(est_mean - mean) < EPS * mean
est_sigma = np.sqrt(sum_ttp / (start * frac) - est_mean ** 2)
assert np.abs(est_sigma - std_dev) < EPS * std_dev
def test_class_Delay():
"""tests to ensure the behaviour class Delay"""
test_model = Model('test_model')
EPS = 0.1
mean = 10.
std_dev = 4.
half_width = float(std_dev * np.sqrt(12.) / 2.)
k_vals = [1, 2, 3]
for delay_type in ['norm', 'uniform', 'erlang', 'gamma']:
k_s = [1]
if delay_type == 'erlang':
k_s = k_vals
for k in k_s:
delay_pars = {
'mean': Parameter('mean', mean, parameter_min=-100., parameter_max=100.),
'sigma': Parameter('sigma', std_dev, parameter_min=-100., parameter_max=100.),
'half_width': Parameter('hw', half_width, parameter_min=-100., parameter_max=100.),
'k': Parameter('k', k, parameter_type='int', parameter_min=1, parameter_max=100)
}
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
# The delay is created after the model: since it is not associated with a connector, the model
# does not know to call its update method. (The model does not have stand alone delays.)
test_delay = Delay('test_delay', delay_type, delay_parameters=delay_pars, model=test_model)
distribution = test_delay.future_expectations
sum_p = 0.
sum_tp = 0.
sum_ttp = 0.
for i in range(len(distribution)):
sum_p += distribution[i]
sum_tp += i * distribution[i] * time_step
sum_ttp += i * i * distribution[i] * time_step ** 2
assert np.abs(sum_p - 1.) < EPS
est_mean = sum_tp
assert np.abs(est_mean - mean) < EPS
est_sigma = np.sqrt(sum_ttp - sum_tp * sum_tp)
if delay_type != 'erlang':
assert np.abs(est_sigma - std_dev) < EPS
else:
assert np.abs(est_sigma - mean / np.sqrt(1. * k)) < EPS
# Define the infection cycle
# oooooooooooooooooooooooooooo
initial_contagious_par = Parameter('cont_0', 10., 0., 5000.,
'Number of contagious people at t0',
hidden=False)
contagious_pop = Population('contagious', initial_contagious_par,
'number of people that can cause someone to become infected',
hidden=False, color='red')
# this value is only used if the transition is removed
trans_rate = Parameter('alpha', 0.4, 0., 2.,
'mean number of people that a contagious person infects ' +
'per day', hidden=True)
fast_delay = Delay('fast', 'fast', model=bc_model)
neg_binom_par = Parameter('neg_binom_p', 0.5, 0.001, 0.999,
'Dispersion parameter p for neg binom')
bc_model.add_connector(
Multiplier('infection cycle', [susceptible_pop, contagious_pop, total_pop],
infected_pop, trans_rate, fast_delay, bc_model,
distribution='nbinom', nbinom_par=neg_binom_par))
contagious_frac = Parameter('cont_frac', 0.9, 0., 1.,
'fraction of infected people that become contagious',
hidden=False)
contagious_delay_pars = {
'mean': Parameter('cont_delay_mean', 5., 0., 50.,
'mean time from being infected to becoming contagious'),
# Define the infection cycle
# oooooooooooooooooooooooooooo
initial_contagious_par = Parameter('cont_0', 55., 0., 5000.,
'Number of contagious people at t0',
hidden=False)
contagious_pop = Population('contagious', initial_contagious_par,
'number of people that can cause someone to become infected',
hidden=False, color='red')
# this value is only used if the transition is removed
trans_rate = Parameter('alpha', 0.390, 0., 2.,
'mean number of people that a contagious person infects ' +
'per day', hidden=True)
infection_delay = Delay('fast', 'fast', model=bc_model)
bc_model.add_connector(
Multiplier('infection cycle', [susceptible_pop, contagious_pop, total_pop],
infected_pop, trans_rate, infection_delay, bc_model))
contagious_frac = Parameter('cont_frac', 0.9, 0., 1.,
'fraction of infected people that become contagious',
hidden=False)
contagious_delay_pars = {
'mean': Parameter('cont_delay_mean', 2., 0., 50.,
'mean time from being infected to becoming contagious'),
'sigma': Parameter('cont_delay_sigma', 1., 0.01, 20.,
'standard deviation of times from being infected to becoming contagious')
}
contagious_pop = Population(
'contagious',
initial_contagious_par,
'number of people that can cause someone to become infected',
hidden=False,
color='red')
# this value is only used if the transition is removed
trans_rate = Parameter(
'alpha',
0.390,
0.,
2.,
'mean number of people that a contagious person infects ' + 'per day',
hidden=True)
infection_delay = Delay('fast', 'fast', model=bc_model)
bc_model.add_connector(
Multiplier('infection cycle', [susceptible_pop, contagious_pop, total_pop],
infected_pop, trans_rate, infection_delay, bc_model))
contagious_frac = Parameter(
'cont_frac',
0.9,
0.,
1.,
'fraction of infected people that become contagious',
hidden=False)
contagious_delay_pars = {
'mean':
Parameter('cont_delay_mean', 2., 0., 50.,