def create_business(self, social_stratum=None):
x, y = self.random_position()
if social_stratum is None:
social_stratum = int(np.random.rand(1) * 100 // 20)
self.business.append(Business(x=x, y=y, status=Status.Susceptible, social_stratum=social_stratum,
#fixed_expenses=(social_stratum+1)*self.minimum_expense
#fixed_expenses=self.minimum_expense / (5 - social_stratum)
environment=self
))
def initialize(self):
"""
Initializate the Simulation by creating its population of agents
"""
x, y = self.random_position()
self.healthcare = Business(x=x,
y=y,
status=Status.Susceptible,
type=AgentType.Healthcare,
environment=self)
self.healthcare.fixed_expenses += self.minimum_expense * 3
x, y = self.random_position()
self.government = Business(x=x,
y=y,
status=Status.Susceptible,
type=AgentType.Government,
social_stratum=4,
price=1.0,
environment=self)
self.government.fixed_expenses += self.population_size * (
self.minimum_expense * 0.05)
#number of houses
for i in np.arange(0, int(self.population_size // self.homemates_avg)):
self.create_house(social_stratum=i % 5)
# number of business
for i in np.arange(0, self.total_business):
self.create_business(social_stratum=5 - (i % 5))
# Initial infected population
for i in np.arange(
0, int(self.population_size * self.initial_infected_perc)):
self.create_agent(Status.Infected, infected_time=5)
# Initial immune population
for i in np.arange(
0, int(self.population_size * self.initial_immune_perc)):
self.create_agent(Status.Recovered_Immune)
# Initial susceptible population
for i in np.arange(0, self.population_size - len(self.population)):
self.create_agent(Status.Susceptible, social_stratum=i % 5)
# Share the common wealth of 10^4 among the population, according each agent social stratum
self.government.wealth = self.total_wealth * self.public_gdp_share
for quintile in [0, 1, 2, 3, 4]:
_houses = [
x for x in filter(lambda x: x.social_stratum == quintile,
self.houses)
]
nhouses = len(_houses)
if nhouses == 0:
self.create_house(social_stratum=quintile)
_houses = [self.houses[-1]]
nhouses = 1
if self.total_business > 5:
btotal = lorenz_curve[quintile] * (self.total_wealth *
self.business_gdp_share)
bqty = max(
1.0,
np.sum([
1.0 for a in self.business
if a.social_stratum == quintile
]))
else:
btotal = self.total_wealth * self.business_gdp_share
bqty = self.total_business
ag_share = btotal / bqty
for bus in filter(lambda x: x.social_stratum == quintile,
self.business):
bus.wealth = ag_share
ptotal = lorenz_curve[quintile] * self.total_wealth * (
1 - (self.public_gdp_share + self.business_gdp_share))
pqty = max(
1.0,
np.sum([
1 for a in self.population if a.social_stratum == quintile
and a.economical_status == EconomicalStatus.Active
]))
ag_share = ptotal / pqty
for agent in filter(lambda x: x.social_stratum == quintile,
self.population):
# distribute wealth
if agent.economical_status == EconomicalStatus.Active:
agent.wealth = ag_share
agent.incomes = basic_income[
agent.social_stratum] * self.minimum_income
# distribute employ
unemployed_test = np.random.rand()
if unemployed_test >= self.unemployment_rate:
ix = np.random.randint(0, self.total_business)
self.business[ix].hire(agent)
agent.expenses = basic_income[
agent.social_stratum] * self.minimum_expense
#distribute habitation
homeless_test = np.random.rand()
if not (quintile == 0 and homeless_test <= self.homeless_rate):
for kp in range(0, 5):
ix = np.random.randint(0, nhouses)
house = _houses[ix]
if house.size < self.homemates_avg + self.homemates_std:
house.append_mate(agent)
continue
if agent.house is None:
ix = np.random.randint(0, nhouses)
self.houses[ix].append_mate(agent)