def test_prob_state_trans(self):
global STATE_TRANS
for i in range(REP_RAND_TESTS):
new_state = prob_state_trans(0, STATE_TRANS)
self.assertNotEqual(new_state, 2)
self.assertNotEqual(new_state, 3)
new_state = prob_state_trans(1, STATE_TRANS)
self.assertEqual(new_state, 2)
new_state = prob_state_trans(2, STATE_TRANS)
self.assertNotEqual(new_state, 0)
self.assertNotEqual(new_state, 1)
new_state = prob_state_trans(3, STATE_TRANS)
self.assertEqual(new_state, 0)
def tree_action(agent, **kwargs):
"""
This is what trees do each turn in the forest.
"""
execution_key = get_exec_key(kwargs=kwargs)
old_state = agent["state"]
if is_healthy(agent):
if exists_neighbor(agent, pred=is_on_fire,
execution_key=execution_key):
if DEBUG2:
user_log_notif("Setting nearby tree on fire!")
agent["state"] = NF
# if we didn't catch on fire above, do probabilistic transition:
if old_state == agent["state"]:
# we gotta do these str/int shenanigans with state cause
# JSON only allows strings as dict keys
agent["state"] = \
str(prob_state_trans(int(old_state),
get_env_attr(TRANS_TABLE,
execution_key=execution_key)))
if DEBUG2:
if agent["state"] == NF:
user_log_notif("Tree spontaneously catching fire.")
if old_state != agent["state"]:
# if we entered a new state, then...
env = get_env(execution_key=execution_key)
group_map = get_env_attr(GROUP_MAP, execution_key=execution_key)
if group_map is None:
user_log_err("group_map is None!")
return True
agent.has_acted = True
env.add_switch(agent, group_map[old_state], group_map[agent["state"]])
return True
def tree_action(agent):
"""
This is what trees do each turn in the forest.
"""
global on_fire
old_state = agent["state"]
if is_healthy(agent):
nearby_fires = Composite(agent.name + "'s nearby fires")
neighbors = agent.locator.get_moore_hood(agent)
if neighbors is not None:
nearby_fires = neighbors.subset(is_on_fire, agent)
if len(nearby_fires) > 0:
if DEBUG2:
print("Setting nearby tree on fire!")
agent["state"] = NF
# if we didn't catch on fire above, do probabilistic transition:
if old_state == agent["state"]:
agent["state"] = prob_state_trans(old_state, STATE_TRANS)
if old_state != agent["state"]:
agent.has_acted = True
agent.locator.add_switch(agent, group_map[old_state],
group_map[agent["state"]])
return True
def test_prob_state_trans(self):
STATE_TRANS = [
[.98, .02, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, .96, .04],
[1.0, 0.0, 0.0, 0.0],
]
for i in range(REP_RAND_TESTS):
new_state = prob_state_trans(0, STATE_TRANS)
self.assertNotEqual(new_state, 2)
self.assertNotEqual(new_state, 3)
new_state = prob_state_trans(1, STATE_TRANS)
self.assertEqual(new_state, 2)
new_state = prob_state_trans(2, STATE_TRANS)
self.assertNotEqual(new_state, 0)
self.assertNotEqual(new_state, 1)
new_state = prob_state_trans(3, STATE_TRANS)
self.assertEqual(new_state, 0)
def test_prob_state_trans(self):
STATE_TRANS = [
[.98, .02, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, .96, .04],
[1.0, 0.0, 0.0, 0.0],
]
for i in range(REP_RAND_TESTS):
new_state = prob_state_trans(0, STATE_TRANS)
self.assertNotEqual(new_state, 2)
self.assertNotEqual(new_state, 3)
new_state = prob_state_trans(1, STATE_TRANS)
self.assertEqual(new_state, 2)
new_state = prob_state_trans(2, STATE_TRANS)
self.assertNotEqual(new_state, 0)
self.assertNotEqual(new_state, 1)
new_state = prob_state_trans(3, STATE_TRANS)
self.assertEqual(new_state, 0)
def tree_action(agent):
"""
This is what trees do each turn in the forest.
"""
global on_fire
old_state = agent["state"]
if is_healthy(agent):
nearby_fires = on_fire.subset(in_hood, agent, NEARBY)
if len(nearby_fires) > 0:
if DEBUG2:
print("Setting nearby tree on fire!")
agent["state"] = NF
# if we didn't catch on fire above, do probabilistic transition:
if old_state == agent["state"]:
agent["state"] = prob_state_trans(old_state, STATE_TRANS)
if old_state != agent["state"]:
agent.locator.add_switch(agent, group_map[old_state],
group_map[agent["state"]])
return True
def person_action(agent, **kwargs):
"""
This is what people do each turn in the epidemic.
"""
execution_key = get_exec_key(kwargs=kwargs)
infec_dist = get_prop('infection_distance', DEF_INFEC_DIST,
execution_key=execution_key)
old_state = agent[STATE]
if is_healthy(agent):
distance_mod = 1
if agent["is_wearing_mask"]:
distance_mod = 0.5
curr_reg = CircularRegion(center=agent.get_pos(),
radius=(2 * infec_dist * distance_mod),
execution_key=execution_key)
sub_reg = curr_reg.create_sub_reg(center=agent.get_pos(),
radius=(infec_dist * distance_mod),
execution_key=execution_key)
agent_list = sub_reg.get_agents(exclude_self=True)
if (agent_list is not None and (len(agent_list) > 0)):
if DEBUG2:
user_log_notif("Exposing nearby people!")
agent[STATE] = EX
else:
for curr_agent in curr_reg.get_agents(exclude_self=True):
curr_distance = (distance(curr_agent, agent) -
DEF_INFEC_DIST)
if (curr_distance > infec_dist and
curr_distance <= (infec_dist * 2)):
inverse_square_val = ((1 / (curr_distance ** 2)) *
distance_mod)
if inverse_square_val > 0:
r = random()
if inverse_square_val / 100 > r:
agent[STATE] = EX
# if we didn't catch disease above, do probabilistic transition:
if old_state == agent[STATE]:
# we gotta do these str/int shenanigans with state cause
# JSON only allows strings as dict keys
agent[STATE] = str(prob_state_trans(int(old_state), STATE_TRANS))
if agent[STATE] == EX:
user_log_notif("Person spontaneously catching virus.")
if old_state != agent[STATE]:
# if we entered a new state, then...
group_map = get_env(execution_key=execution_key).get_attr(GROUP_MAP)
if group_map is None:
user_log_err("group_map is None!")
return DONT_MOVE
agent.has_acted = True
get_env(execution_key=execution_key).add_switch(agent,
group_map[old_state],
group_map[
agent[STATE]])
if is_dead(agent):
return DONT_MOVE
if not is_isolated(agent, **kwargs):
social_distancing(agent, **kwargs)
return MOVE