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_Parameter():
"""tests to ensure the behaviour class Parameter"""
test_parameter = Parameter('test_par', 10., parameter_min=5., parameter_max=100.)
for value in [50]:
error_caught = False
try:
test_parameter.set_value(value)
except ValueError:
error_caught = True
except TypeError:
error_caught = True
assert error_caught
def test_class_Operator():
r1 = 10.
r2 = 11.3
i1 = 5
i2 = 7
rp1 = Parameter('r1', r1, parameter_min=5., parameter_max=100.)
rp2 = Parameter('r2', r2, parameter_min=5., parameter_max=100.)
ip1 = Parameter('i1', i1, parameter_type='int', parameter_min=5, parameter_max=100)
ip2 = Parameter('r1', i2, parameter_type='int', parameter_min=5, parameter_max=100)
assert Operator([rp1, rp2], '*').get_value() == r1 * r2
assert Operator([rp1, ip1], '*').get_value() == r1 * i1
assert Operator([ip1, ip2], '*').get_value() == i1 * i2
assert Operator([rp1, rp2, rp1, ip2], '*+/').get_value() == r1 * r2 + r1 / i2
assert Operator([ip1, ip2, ip1, ip2], '*/+').get_value() == i1 * i2 / i1 + i2
def test_Ensemble_data():
"""tests to check data from an ensemble"""
# Test that the ensemble of two identical models
# behalves like twice a single model.
# Only for the independent (ie. diagonal) case
# Note: this test would fail if a fixed contact_matrix
# is passed which happens to be the identity matrix
# The difference is that when specified as independent
# each model is booted independently
# If a contact matrix is specified, then the boot goal
# is the combined total of all models. So there will be a different starting point.
test_a = Model.open_file(path_model_2)
test_a.name = 'test_a'
test_b = Model.open_file(path_model_2)
test_b.name = 'test_b'
test_b.parameters['alpha_0'].set_value(0.7)
reference = Model.open_file(path_model_2)
reference.name = 'reference'
off_diag = Parameter('off_diagonal',
0.1,
parameter_min=0.,
parameter_max=1.,
description='off diagonal element of contact matrix')
off_diags = [off_diag]
test_ensemble = Ensemble('test_ensemble', reference)
test_ensemble.upload_models([test_a, test_b])
test_ensemble.define_cross_transmission('infection cycle',
'infected',
'susceptible',
'total',
'contagious',
'alpha',
contact_type='simple',
contact=off_diags)
n_days = 100
norm_day = 50
test_ensemble.reset()
test_ensemble.evolve_expectations(norm_day)
for key in test_ensemble.populations:
pop = test_ensemble.populations[key]
nu = pop.history[norm_day]
pop.history[norm_day] = int(round(nu))
pop.scale_future(1., expectations=False)
for model_name in test_ensemble.models:
model = test_ensemble.models[model_name]
for pop_name in model.populations:
pop = model.populations[pop_name]
nu = pop.history[norm_day]
pop.history[norm_day] = int(round(nu))
pop.scale_future(1., expectations=False)
test_ensemble.generate_data(n_days, norm_day)
for pop_name in test_ensemble.populations:
pop = test_ensemble.populations[pop_name]
if pop.show_sim:
ens_hist = test_ensemble.populations[pop_name].history
def test_class_Injector():
"""tests to ensure the behaviour class Injector"""
test_model = Model('test_model')
number = 50.
inject = Parameter('inject', number, parameter_min=0., parameter_max=1000.)
time = 5
trans_time = Parameter('time', time, parameter_type='int', parameter_min=0, parameter_max=1000)
to_pop = Population('to_pop', 0)
test_injector = Injector('injector', 'rel_days', trans_time, to_pop, inject, model=test_model)
test_model.add_transition(test_injector)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
to_pop.reset()
test_injector.take_action()
assert to_pop.future[0] == number
assert np.abs(test_injector.trigger_step - time / time_step) < 0.1
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
def test_class_Modifier():
"""tests to ensure the behaviour class Modifier"""
test_model = Model('test_model')
mod_time = Parameter('time', 5, parameter_type='int', parameter_min=0, parameter_max=1000)
par_val = 0.3
par_0_val = 0.5
par_1_val = 0.7
parameter = Parameter('par', par_val)
parameter_0 = Parameter('par_0', par_0_val)
parameter_1 = Parameter('par_1', par_1_val)
test_modifier = Modifier('test_modifier', 'rel_days', mod_time, parameter, parameter_0, parameter_1,
model=test_model)
test_model.add_transition(test_modifier)
for time_step in [1., 1. / 4.]:
test_model.set_time_step(time_step)
parameter.reset()
assert parameter.get_value() == par_val
test_modifier.take_action()
assert parameter.get_value() == par_1_val
test_modifier.reset()
assert parameter.get_value() == par_0_val
assert np.abs(test_modifier.trigger_step - 5 / time_step) < 0.1
def test_class_Adder():
"""tests to ensure the behaviour class Adder"""
for itest in range(6):
from_init = 5
to_init = 10
f_over_t = 1.*from_init/to_init
from_pop = Population('from_pop', from_init)
from_next = 40
from_pop.future = [from_next]
to_pop = Population('to_pop', to_init)
if itest == 0:
test_adder = Adder('test_add', from_pop, to_pop)
test_adder.update_expectation()
assert to_pop.future[0] == from_next
elif itest == 1:
sf = 0.2
scale_factor = Parameter('scale_factor', sf, 0., 10.)
test_adder = Adder('test_add', from_pop, to_pop, scale_factor=scale_factor)
test_adder.update_expectation()
assert to_pop.future[0] == from_next * sf
elif itest == 2:
sf = 0.3
ratio_pops = [from_pop, to_pop]
scale_factor = Parameter('scale_factor', sf, 0., 10.)
test_adder = Adder('test_add', from_pop, to_pop, scale_factor=scale_factor, ratio_populations=ratio_pops)
test_adder.update_expectation()
assert to_pop.future[0] == from_next * sf * f_over_t
elif itest == 3:
sf = 0.3
ratio_pops = [from_pop, to_pop]
scale_factor = Parameter('scale_factor', sf, 0, 10.)
test_adder = Adder('test_add', from_pop, to_pop, scale_factor=scale_factor, ratio_populations=ratio_pops)
test_adder.update_data()
prob = sf * f_over_t
sigma = np.sqrt(from_next*prob*(1-prob))
assert np.abs(to_pop.future[0] - from_next * sf * f_over_t)<3.*sigma
elif itest == 4:
sf = 0.31
ratio_pops = [from_pop, to_pop]
scale_factor = Parameter('scale_factor', sf, 0., 10.)
test_adder = Adder('test_add', from_pop, to_pop, scale_factor=scale_factor, ratio_populations=ratio_pops)
test_adder.update_data()
assert np.abs(to_pop.future[0] - from_next * sf * f_over_t)<3.*sigma
elif itest == 5:
sf = 0.31
r1 = Parameter('r1', sf/3., parameter_min=1., parameter_max=100.)
r2 = Parameter('r2', 3., parameter_min=1., parameter_max=100.)
ratio_pops = [from_pop, to_pop]
# scale_factor = Parameter('scale_factor', sf, 0., 10.)
scale_factor = Operator([r1,r2],'*')
test_adder = Adder('test_add', from_pop, to_pop, scale_factor=scale_factor, ratio_populations=ratio_pops)
test_adder.update_data()
assert to_pop.future[0] >= from_next * int(sf) * f_over_t
def test_class_Population():
"""tests to ensure the behaviour class Population"""
init_value = 100
test_pop = Population('test population', init_value, description='For testing populations',
hidden=True, color='black', show_sim=False, report_noise=False,
report_noise_par=None)
assert test_pop.history[0] == init_value
model = Model.open_file(path_model_2_8)
test_pop.set_model(model)
incoming = 10
test_pop.update_future_fast(incoming)
assert test_pop.future[0] == incoming
test_pop.do_time_step()
assert test_pop.history[1] == init_value + incoming
assert len(test_pop.future) == 0 or test_pop.future == 0
scale_factor = 0.5
test_pop.scale_history(scale_factor)
assert test_pop.history[0] == init_value * scale_factor
assert test_pop.history[1] == (init_value + incoming) * scale_factor
# check that noise in reporting makes sense
# Expectations are not effected - data should be
# but the total reported should be the same after all reporting complete
noise_factor = Parameter('noise_factor', 0.3)
backlog_factor = Parameter('backlog_factor', 0.8)
# restart - back to initial value
for expectations in [True, False]:
test_pop.reset()
future = [0, 10, 100, 100, 100, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
future_sum = np.sum(np.array(future))
test_pop.set_report_noise(True, noise_factor, backlog_factor, None)
test_pop.future = future
for i in range(len(future) + 5):
test_pop.do_time_step(expectations=expectations)
assert test_pop.history[-1] == init_value + future_sum
report_days = Parameter('report_days', 127, parameter_min=-7, parameter_max=127, parameter_type='int')
for report_day_value in [127, 63, -1, -2, -5, -7]:
report_days.set_value(report_day_value)
test_pop.reset()
future = [0, 10, 100, 100, 100, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
future_sum = np.sum(np.array(future))
test_pop.set_report_noise(True, noise_factor, backlog_factor, report_days)
test_pop.future = future
for i in range(len(future) + 5):
test_pop.do_time_step(expectations=expectations)
assert test_pop.history[-1] == init_value + future_sum
- add second infection cycle for B117 variant
@author: karlen
"""
from pypmca import Model, Population, Delay, Parameter, Multiplier, Propagator, \
Splitter, Adder, Subtractor, Chain, Modifier, Injector
# Test by building a population model for BC
bc_model = Model('ref_model_2_8')
bc_model.set_t0(2020, 3, 1)
# Initialization
initial_pop_par = Parameter('N_0', 5000000, 5000, 50000000,
'Population of the region at t0', 'int')
total_pop = Population('total', initial_pop_par,
'total population of the region', color='black')
susceptible_pop = Population('susceptible', initial_pop_par,
'number of people who could become infected', color='cornflowerblue')
infected_pop = Population('infected', 0,
'total number of people ever infected', color='orange')
# Define the infection cycle
# oooooooooooooooooooooooooooo
initial_contagious_par = Parameter('cont_0', 10., 0., 5000.,
'Number of contagious people at t0',
hidden=False)
- set death reporting noise to be weekly
@author: karlen
"""
from pypmca import Model, Population, Delay, Parameter, Multiplier, Propagator, \
Splitter, Adder, Subtractor, Chain, Modifier, Injector
# Test by building a population model for BC
bc_model = Model('ref_model_2_6')
bc_model.set_t0(2020, 3, 1)
# Initialization
initial_pop_par = Parameter('N_0', 5000000, 5000, 50000000,
'Population of the region at t0', 'int')
total_pop = Population('total',
initial_pop_par,
'total population of the region',
color='black')
susceptible_pop = Population('susceptible',
initial_pop_par,
'number of people who could become infected',
color='cornflowerblue')
infected_pop = Population('infected',
0,
'total number of people ever infected',
color='orange')
# Define the infection cycle
update: May 21, 2020. Fix error: need to remove "departed ventillator" from "in_icu"
@author: karlen
"""
from pypmca import Model, Population, Delay, Parameter, Multiplier, Propagator, \
Splitter, Adder, Subtractor, Chain, Modifier, Injector
# Test by building a population model for BC
bc_model = Model('ref_model_1')
bc_model.set_t0(2020, 3, 1)
# Initialization
initial_pop_par = Parameter('N_0', 5000000, 5000, 50000000,
'Population of the region at t0', 'int')
total_pop = Population('total', initial_pop_par,
'total population of the region', color='black')
susceptible_pop = Population('susceptible', initial_pop_par,
'number of people who could become infected', color='cornflowerblue')
infected_pop = Population('infected', 0,
'total number of people ever infected', color='orange')
# Define the infection cycle
# oooooooooooooooooooooooooooo
initial_contagious_par = Parameter('cont_0', 55., 0., 5000.,
'Number of contagious people at t0',
hidden=False)
is switched on.
@author: karlen
"""
from pypmca import Model, Population, Delay, Parameter, Multiplier, Propagator, \
Splitter, Adder, Subtractor, Chain, Modifier, Injector
# Test by building a population model for BC
bc_model = Model('ref_model_2')
bc_model.set_t0(2020, 3, 1)
# Initialization
initial_pop_par = Parameter('N_0', 5000000, 5000, 50000000,
'Population of the region at t0', 'int')
total_pop = Population('total',
initial_pop_par,
'total population of the region',
color='black')
susceptible_pop = Population('susceptible',
initial_pop_par,
'number of people who could become infected',
color='cornflowerblue')
infected_pop = Population('infected',
0,
'total number of people ever infected',
color='orange')
# Define the infection cycle
