def execute(self, agent: Agent, state: SimState) -> None:
"""
Isolate (Remove from Grid) a given share of infected people for the sickness-duration.
Afterwards they need to be added again to the Grid as removed/dead/immune.
"""
if agent.is_quarantined():
if agent.state() is AgentState.DEAD or agent.state() is AgentState.IMMUNE or agent.state() is AgentState.REMOVED:
grid = agent.grid()
for row in range(grid.get_size()):
for col in range(grid.get_size()):
grid_pos = GridPos(np.uint(row), np.uint(col))
if not grid.is_occupied(grid_pos):
grid.set_agent(agent, grid_pos)
agent.set_pos(grid_pos)
agent.set_quarantined(False)
agent.grid().get_quarantinedAgents().remove(agent)
state.add_to_quarantined_count(-1)
return
else:
isolate_share = state.quarantine_share() # Share of infected cells to isolate
infected = state.infected_count()
if agent.state() == AgentState.INFECTIVE and state.get_quarantined_count() < isolate_share * (
infected + state.get_quarantined_count()):
agent.set_quarantined(True)
agent.grid().get_quarantinedAgents().append(agent)
agent.grid().set_agent(None, agent.get_pos())
agent.get_scheduler().update_gui_state(agent.get_pos(), AgentState.EMPTY)
state.add_to_quarantined_count(1)
def execute(self, agent: Agent, state: SimState) -> None:
if agent.is_quarantined():
return
if agent.state() is AgentState.INFECTIVE or AgentState.INCUBATION:
infection_radius = state.infection_env_radius()
infection_env_size = infection_radius * 2 + 1
size = agent.grid().get_size()
check_list = list()
grid_pos = agent.get_pos()
x = grid_pos.row()
y = grid_pos.col()
if state.infection_env_metric() == EnvironmentMetric.MANHATTAN:
for r in range(0, infection_env_size):
offset = abs(infection_radius - r)
check_row = y - infection_radius + r
for c in range(offset, infection_env_size - offset):
check_column = x - infection_radius + c
check_list.append((check_column, check_row))
elif state.infection_env_metric() == EnvironmentMetric.EUCLIDEAN:
for r in range(0, infection_env_size):
check_row = y - infection_radius + r
for c in range(0, infection_env_size):
check_column = x - infection_radius + c
distance = np.round(np.sqrt((infection_radius - r) ** 2 + (infection_radius - c) ** 2))
if 0 < distance <= infection_radius:
check_list.append((check_column, check_row))
else:
raise ValueError('Metric not implemented')
check_list = list(filter(lambda pos: 0 <= pos[0] < size and 0 <= pos[1] < size, check_list))
for check_pos in check_list:
to_check = agent.grid().get_agent(GridPos(np.uint(check_pos[0]), np.uint(check_pos[1])))
if to_check is not None and to_check.state() is AgentState.SUSCEPTIBLE:
if np.random.random() < state.infection_prob():
if state.incubation_period_enabled():
to_check.set_state(AgentState.INCUBATION)
else:
to_check.set_state(AgentState.INFECTIVE)
agent.update_infected_count()
def move_agent(self, agent: Agent, state: SimState) -> None:
grid = agent.grid()
if grid.is_fully_occupied():
return
if agent.state() is AgentState.DEAD or agent.is_quarantined():
return # We don't want zombies
move_probability = np.random.randint(low=0, high=100)
if move_probability <= state.get_mixing_value_m() * 100:
new_grid_pos = get_free_pos(grid)
old_grid_pos = agent.get_pos()
grid.move_agent(old_grid_pos, new_grid_pos)
def move_agent(self, agent: Agent, state: SimState) -> None:
grid = agent.grid()
if grid.is_fully_occupied():
return
if agent.state() is AgentState.DEAD or agent.is_quarantined():
return # We don't want zombies
move_probability = np.random.randint(low=0, high=100)
if move_probability <= state.get_mixing_value_m() * 100:
radius = state.movement_limit_radius()
if state.movement_limit_high_distances_are_uncommon():
# Recalculate radius -> lower radius is more probable
mean = 0
standard_deviation = radius / 3
radius = min(
max(
1,
int(
np.round(
np.abs(
norm.rvs(size=1,
loc=mean,
scale=standard_deviation)[0])))),
radius)
try:
new_grid_pos = get_free_pos_limited(
grid,
pos=agent.get_pos(),
radius=radius,
metric=state.movement_limit_metric(),
)
old_grid_pos = agent.get_pos()
grid.move_agent(old_grid_pos, new_grid_pos)
finally:
return