def test_human_parameter_changed():
# Store old human properties
old_sample_human_1 = copy.copy(sample_human_1)
# Change parameters and reroll properties
parameters = base.SimulationParameters(mask_user_rate=0.0,
imune_rate=0.0,
weareable_adoption_rate=0.0)
base.set_parameters(parameters)
sample_human_1.parameter_changed()
# Make sure they're different
assert old_sample_human_1.mask_user != sample_human_1.mask_user
assert old_sample_human_1.immune != sample_human_1.immune
assert old_sample_human_1.early_symptom_detection != sample_human_1.early_symptom_detection
population_size = 1000
simulation_cycles = 180 # days
################################################################################
# Scenarios
scenario = {}
# ------------------------------------------------------------------------------
sc = 1 # Do nothing
print(f"Setting up scenario {sc}")
scenario[sc] = {}
scenario[sc]['parameters'] = copy.deepcopy(common_parameters)
set_parameters(scenario[sc]['parameters'])
scenario[sc]['model'] = CovidModel()
np.random.seed(seed)
setup_city_layout(scenario[sc]['model'], population_size)
# ------------------------------------------------------------------------------
sc = 2 # complete lockdown
print(f"Setting up scenario {sc}")
scenario[sc] = {}
scenario[sc]['parameters'] = copy.deepcopy(common_parameters)
scenario[sc]['parameters'].params['social_policies'] = [
SocialPolicy.LOCKDOWN_OFFICE,
SocialPolicy.LOCKDOWN_FACTORY,
SocialPolicy.LOCKDOWN_RETAIL,
SocialPolicy.LOCKDOWN_ELEMENTARY_SCHOOL,
def test_flip_coin():
assert type(base.flip_coin(0.5)) == bool
with pytest.raises(TypeError):
base.flip_coin("50")
def test_random_selection():
random_numbers = np.random.rand(5)
assert base.random_selection(random_numbers, 1) in random_numbers
# Setting up parameters
hospitalization_capacity = 0.05
parameters = base.SimulationParameters(
hospitalization_capacity=hospitalization_capacity)
base.set_parameters(parameters)
model = base.CovidModel()
population_size = 1000
setup_city_layout(model, population_size)
def test_agent_base():
random_id = np.random.randint(50)
sample_agent = base.AgentBase(random_id, model)
assert sample_agent.id == random_id
assert sample_agent in model.agents
def test_initial_model_state():
# Assert initial model state
assert model.agents
from model import location
from model.base import CovidModel, SimulationParameters, set_parameters
from model.human import Human, Adult, InfectionStatus
from model.utils import SimulationState, RestaurantType
parameters = SimulationParameters(mask_user_rate=0.0)
set_parameters(parameters)
model = CovidModel()
capacity = 2000
def check_spread(sample_location,
spreading_states=None,
check_step=True,
in_social_event=False):
if spreading_states is None:
spreading_states = list(SimulationState)
# Creates an infected and a susceptible human
sample_human_1 = Human.factory(covid_model=model, forced_age=20)
sample_human_1.infection_status = InfectionStatus.INFECTED
sample_human_1.infection_days_count = 1
sample_human_1.infection_latency = 0
assert sample_human_1.is_infected() and sample_human_1.is_contagious()
sample_human_2 = Adult(covid_model=model,
age=50,
msp=0.05,
hsp=0.1,
mfd=False)
sample_human_2.strid = 'human_2'
sample_human_2.infection_status = InfectionStatus.SUSCEPTIBLE
assert not sample_human_2.is_infected()
mask_user_rate=mask_user_rate,
mask_efficacy=mask_efficacy,
imune_rate=imune_rate,
initial_infection_rate=initial_infection_rate,
hospitalization_capacity=hospitalization_capacity,
latency_period_mean=latency_period_mean,
latency_period_stdev=latency_period_stdev,
incubation_period_mean=incubation_period_mean,
incubation_period_stdev=incubation_period_stdev,
disease_period_mean=disease_period_mean,
disease_period_stdev=disease_period_stdev,
daily_interaction_count=daily_interaction_count,
contagion_probability=contagion_probability,
asymptomatic_isolation_rate=asymptomatic_isolation_rate,
symptomatic_isolation_rate=symptomatic_isolation_rate)
set_parameters(scenario[sc]['parameters'])
scenario[sc]['model'] = CovidModel()
scenario[sc]['location'] = SimpleLocation(0, scenario[sc]['model'],
population_size)
# ------------------------------------------------------------------------------
sc = 2 # Restrict the mobility only for infected people - no weareables
scenario[sc] = {}
scenario[sc]['parameters'] = SimulationParameters(
weareable_adoption_rate=0.0,
mask_user_rate=mask_user_rate,
mask_efficacy=mask_efficacy,
imune_rate=imune_rate,
initial_infection_rate=initial_infection_rate,
hospitalization_capacity=hospitalization_capacity,
def multiple_runs(params,
population_size,
simulation_cycles,
num_runs=5,
seeds=[],
debug=False,
desired_stats=None,
fname="scenario",
listeners=[],
do_print=False,
home_grid_height=1,
home_grid_width=1,
work_height=1,
work_width=1,
school_height=1,
school_width=1):
color = {
'susceptible': 'lightblue',
'infected': 'gray',
'recovered': 'lightgreen',
'death': 'black',
'hospitalization': 'orange',
'icu': 'red',
'income': 'magenta'
}
if desired_stats is None:
desired_stats = [
"susceptible", "infected", "recovered", "hospitalization", "icu",
"death", "income"
]
randomlist = random.sample(range(10000),
num_runs) if len(seeds) == 0 else seeds
if do_print:
print("Save these seeds if you want to rerun a scenario")
print(randomlist)
all_runs = {}
avg = {}
last = {}
peak = {}
average = {}
lower = {}
upper = {}
for stat in desired_stats:
all_runs[stat] = {}
avg[stat] = []
last[stat] = []
peak[stat] = []
average[stat] = []
upper[stat] = []
lower[stat] = []
for s in randomlist:
paramcopy = copy.deepcopy(params)
set_parameters(paramcopy)
model = CovidModel(debug=debug)
np.random.seed(s + 1)
random.seed(s + 2)
model.reset_randomizer(s)
l = []
ls = copy.deepcopy(listeners)
for listener in ls:
funct = listener.pop(0)
listener[:0] = [model]
l.append(globals()[funct](*listener))
for k in l:
model.add_listener(k)
#for m in model.ls:
#print(m)
if debug:
model.debug_each_n_cycles = 20
setup_grid_layout(model, population_size, home_grid_height,
home_grid_width, work_height, work_width,
school_height, school_width)
if do_print:
print("run with seed {0}:".format(str(s)))
statistics = BasicStatistics(model)
model.add_listener(statistics)
for i in range(simulation_cycles):
model.step()
for stat in desired_stats:
all_runs[stat][s] = getattr(statistics, stat)
if stat is "income":
all_runs[stat][s].pop(1)
avg[stat].append(np.mean(all_runs[stat][s]))
last[stat].append(all_runs[stat][s][-1])
peak[stat].append(max(all_runs[stat][s]))
fig, ax = plt.subplots()
ax.set_title('Contagion Evolution')
ax.set_xlim((0, simulation_cycles))
ax.set_ylim((-0.1, 1.1))
ax.axhline(y=get_parameters().get('icu_capacity'),
c="black",
ls='--',
label='Critical limit')
for s in randomlist:
adict = {stat: all_runs[stat][s] for stat in desired_stats}
df = pd.DataFrame(data=adict)
df.to_csv(fname + "-" + str(s) + ".csv")
each_step = {}
for stat in desired_stats:
each_step[stat] = []
for i in range(simulation_cycles):
each_step[stat].append([all_runs[stat][s][i] for s in randomlist])
for i in range(simulation_cycles):
loweri, averagei, upperi = confidence_interval(each_step[stat][i],
confidence=0.95)
lower[stat].append(loweri)
average[stat].append(averagei)
upper[stat].append(upperi)
#print (stat)
#print (lower[stat])
#print (upper[stat])
#Plotting:
ax.plot(lower[stat], color=color[stat], linewidth=2) #mean curve.
ax.plot(average[stat], color=color[stat], linewidth=2)
ax.plot(upper[stat], color=color[stat], linewidth=2)
ax.fill_between(simulation_cycles,
lower[stat],
upper[stat],
color=color[stat],
alpha=.1,
label=stat) #std curves.
ax.set_xlabel("Days")
ax.set_ylabel("% of Population")
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels, loc='upper right')
fig.show()
fig.savefig(fname + ".png")
if do_print:
for stat, x in avg.items():
print("using average of time series:")
print("stats on {}:".format(stat))
print("data: {}".format(x))
print("min:")
print(np.min(x))
print("max:")
print(np.max(x))
print("std:")
print(np.std(x))
low, mean, high = confidence_interval(x, confidence=0.95)
print("mean:")
print(mean)
print("median:")
print(np.median(x))
print("95% confidence interval for the mean:")
print("({0},{1})".format(low, high))
for stat, x in last.items():
print("using last of time series:")
print("stats on {}:".format(stat))
print("data: {}".format(x))
print("min:")
print(np.min(x))
print("max:")
print(np.max(x))
print("std:")
print(np.std(x))
low, mean, high = confidence_interval(x, confidence=0.95)
print("mean:")
print(mean)
print("median:")
print(np.median(x))
print("95% confidence interval for the mean:")
print("({0},{1})".format(low, high))
for stat, x in peak.items():
print("using peak of time series:")
print("stats on {}:".format(stat))
print("data: {}".format(x))
print("min:")
print(np.min(x))
print("max:")
print(np.max(x))
print("std:")
print(np.std(x))
low, mean, high = confidence_interval(x, confidence=0.95)
print("mean:")
print(mean)
print("median:")
print(np.median(x))
print("95% confidence interval for the mean:")
print("({0},{1})".format(low, high))
return avg.items, last.items, peak.items
ICR = [0.3, 0.4, 0.5, 0.6, 0.7]
ICS = [0.3, 0.4, 0.5, 0.6, 0.7]
# Baseline
set_parameters(
SimulationParameters(
mask_user_rate=0.0,
mask_efficacy=0.0,
isolation_cheater_rate=0.0,
isolation_cheating_severity=0.0,
imune_rate=imune_rate,
initial_infection_rate=initial_infection_rate,
hospitalization_capacity=hospitalization_capacity,
latency_period_mean=latency_period_mean,
latency_period_stdev=latency_period_stdev,
incubation_period_mean=incubation_period_mean,
incubation_period_stdev=incubation_period_stdev,
disease_period_mean=disease_period_mean,
disease_period_stdev=disease_period_stdev,
asymptomatic_isolation_rate=asymptomatic_isolation_rate,
symptomatic_isolation_rate=symptomatic_isolation_rate,
daily_interaction_count=daily_interaction_count,
contagion_probability=contagion_probability))
sum = 0.0
for k in range(epochs):
model = CovidModel()
location = SimpleLocation(0, model, population_size)
statistics = BasicStatistics(model)
def multiple_runs(params,
population_size,
simulation_cycles,
num_runs=5,
seeds=None,
debug=False,
desired_stats=[
"susceptible", "infected", "recovered",
"hospitalization", "icu", "death", "income"
],
do_print=False):
randomlist = random.sample(range(10000),
num_runs) if seeds is None else seeds
if (do_print):
print("Save these seeds if you want to rerun a scenario")
print(randomlist)
all_runs = {}
avg = {}
last = {}
peak = {}
for stat in desired_stats:
all_runs[stat] = {}
avg[stat] = []
last[stat] = []
peak[stat] = []
for s in randomlist:
set_parameters(params)
model = CovidModel(debug=debug)
if debug:
model.debug_each_n_cycles = 20
np.random.seed(s + 1)
random.seed(s + 2)
setup_city_layout(model, population_size)
if (do_print):
print("run with seed {0}:".format(str(s)))
model.reset_randomizer(s)
statistics = BasicStatistics(model)
model.add_listener(statistics)
for i in range(simulation_cycles):
model.step()
for stat in desired_stats:
all_runs[stat][s] = getattr(statistics, stat)
avg[stat].append(np.mean(all_runs[stat][s]))
last[stat].append(all_runs[stat][s][-1])
peak[stat].append(max(all_runs[stat][s]))
if (do_print):
for stat, x in avg.items():
print("using average of time series:")
print("stats on {}:".format(stat))
print("data: {}".format(x))
print("min:")
print(np.min(x))
print("max:")
print(np.max(x))
print("std:")
print(np.std(x))
print("mean:")
print(np.mean(x))
print("median:")
print(np.median(x))
print("95% confidence interval for the mean:")
low, high = confidence_interval(x, confidence=0.95)
print("({0},{1})".format(low, high))
for stat, x in last.items():
print("using last of time series:")
print("stats on {}:".format(stat))
print("data: {}".format(x))
print("min:")
print(np.min(x))
print("max:")
print(np.max(x))
print("std:")
print(np.std(x))
print("mean:")
print(np.mean(x))
print("median:")
print(np.median(x))
print("95% confidence interval for the mean:")
low, high = confidence_interval(x, confidence=0.95)
print("({0},{1})".format(low, high))
for stat, x in peak.items():
print("using peak of time series:")
print("stats on {}:".format(stat))
print("data: {}".format(x))
print("min:")
print(np.min(x))
print("max:")
print(np.max(x))
print("std:")
print(np.std(x))
print("mean:")
print(np.mean(x))
print("median:")
print(np.median(x))
print("95% confidence interval for the mean:")
low, high = confidence_interval(x, confidence=0.95)
print("({0},{1})".format(low, high))
return (avg.items, last.items, peak.items)
