def calc_nutrient(group, agent):
nutrient_strength = 0
for nutrient in group:
if distance(group[nutrient], agent) != 0:
nutrient_strength += 1 / (distance(group[nutrient], agent)**2)
else:
nutrient_strength += sys.maxsize
return nutrient_strength
def calc_toxin(group, agent):
"""
Calculate the strength of a toxin / nutrient field for an agent.
We will use an inverse square law.
"""
toxin_strength = 0
for toxin in group:
if distance(group[toxin], agent) != 0:
toxin_strength += 1 / (distance(group[toxin], agent)**2)
else:
toxin_strength += sys.maxsize
toxin_strength *= -1
return toxin_strength
def bird_action(this_bird, **kwargs):
"""
Finds the closest agent to the current agent and calculates
the distance between the two, inverting the direction if the
distance is too close.
"""
if DEBUG:
print(str(this_bird), " is at ", this_bird.get_pos())
nearest_bird = get_env().get_closest_agent(this_bird)
if nearest_bird is not None:
curr_distance = distance(this_bird, nearest_bird)
if DEBUG:
print("Distance between ", nearest_bird, " and ", this_bird,
" is ", curr_distance)
if abs(curr_distance - DEF_DESIRED_DISTANCE) < (DEF_DESIRED_DISTANCE *
ACCEPTABLE_DEV):
return True
this_bird["angle"] = calc_angle(this_bird, nearest_bird)
if DEBUG:
print(this_bird.name, "'s angle rel. to ", nearest_bird.name, "is",
this_bird["angle"])
if curr_distance < DEF_DESIRED_DISTANCE:
this_bird["angle"] = invert_direction(this_bird["angle"])
return MOVE
def test_get_closest_agent(self):
closest = self.space.get_closest_agent(self.newton)
self.assertTrue(
distance(self.newton, closest) <= self.space.get_max_distance())
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