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Code Issues Releases Wiki Activity

Pre-release version of v1.0

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This commit is contained in:
J. Fernando Sánchez
2023-04-20 17:56:44 +02:00
parent be65592055
commit cc238d84ec
75 changed files with 3438 additions and 7528 deletions
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21
examples/events_and_messages/cars_sim.py
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@@ -94,9 +94,9 @@ class Driver(Evented, FSM):
def check_passengers(self):
"""If there are no more passengers, stop forever"""
c = self.count_agents(agent_class=Passenger)
self.info(f"Passengers left {c}")
self.debug(f"Passengers left {c}")
if not c:
self.die()
self.die("No more passengers")
@default_state
@state
@@ -129,10 +129,13 @@ class Driver(Evented, FSM):
@state
def driving(self):
"""The journey has been accepted. Pick them up and take them to their destination"""
self.info(f"Driving towards Passenger {self.journey.passenger.unique_id}")
while self.move_towards(self.journey.origin):
yield
self.info(f"Driving {self.journey.passenger.unique_id} from {self.journey.origin} to {self.journey.destination}")
while self.move_towards(self.journey.destination, with_passenger=True):
yield
self.info("Arrived at destination")
self.earnings += self.journey.tip
self.model.total_earnings += self.journey.tip
self.check_passengers()
@@ -140,7 +143,7 @@ class Driver(Evented, FSM):
def move_towards(self, target, with_passenger=False):
"""Move one cell at a time towards a target"""
self.info(f"Moving { self.pos } -> { target }")
self.debug(f"Moving { self.pos } -> { target }")
if target[0] == self.pos[0] and target[1] == self.pos[1]:
return False
@@ -174,8 +177,8 @@ class Passenger(Evented, FSM):
@state
def asking(self):
destination = (
self.random.randint(0, self.model.grid.height),
self.random.randint(0, self.model.grid.width),
self.random.randint(0, self.model.grid.height-1),
self.random.randint(0, self.model.grid.width-1),
)
self.journey = None
journey = Journey(
@@ -187,19 +190,21 @@ class Passenger(Evented, FSM):
timeout = 60
expiration = self.now + timeout
self.info(f"Asking for journey at: { self.pos }")
self.model.broadcast(journey, ttl=timeout, sender=self, agent_class=Driver)
while not self.journey:
self.info(f"Passenger at: { self.pos }. Checking for responses.")
self.debug(f"Waiting for responses at: { self.pos }")
try:
# This will call check_messages behind the scenes, and the agent's status will be updated
# If you want to avoid that, you can call it with: check=False
yield self.received(expiration=expiration)
except events.TimedOut:
self.info(f"Passenger at: { self.pos }. Asking for journey.")
self.info(f"Still no response. Waiting at: { self.pos }")
self.model.broadcast(
journey, ttl=timeout, sender=self, agent_class=Driver
)
expiration = self.now + timeout
self.info(f"Got a response! Waiting for driver")
return self.driving_home
@state
@@ -220,7 +225,7 @@ simulation = Simulation(name="RideHailing",
model=City,
seed="carsSeed",
max_time=1000,
model_params=dict(n_passengers=2))
parameters=dict(n_passengers=2))
if __name__ == "__main__":
easy(simulation)

2
examples/mesa/mesa_sim.py
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@@ -1,7 +1,7 @@
from soil import Simulation
from social_wealth import MoneyEnv, graph_generator
sim = Simulation(name="mesa_sim", dump=False, max_steps=10, interval=2, model=MoneyEnv, model_params=dict(generator=graph_generator, N=10, width=50, height=50))
sim = Simulation(name="mesa_sim", dump=False, max_steps=10, interval=2, model=MoneyEnv, parameters=dict(generator=graph_generator, N=10, width=50, height=50))
if __name__ == "__main__":
sim.run()

6
examples/mesa/server.py
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@@ -63,7 +63,7 @@ chart = ChartModule(
[{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
)
model_params = {
parameters = {
"N": Slider(
"N",
5,
@@ -98,12 +98,12 @@ model_params = {
canvas_element = CanvasGrid(
gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500
gridPortrayal, parameters["width"].value, parameters["height"].value, 500, 500
)
server = ModularServer(
MoneyEnv, [grid, chart, canvas_element], "Money Model", model_params
MoneyEnv, [grid, chart, canvas_element], "Money Model", parameters
)
server.port = 8521

4
examples/newsspread/newsspread_sim.py
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@@ -116,7 +116,7 @@ for [r1, r2] in product([0, 0.5, 1.0], repeat=2):
Simulation(
name='newspread_sim',
model=NewsSpread,
model_params=dict(
parameters=dict(
ratio_dumb=r1,
ratio_herd=r2,
ratio_wise=1-r1-r2,
@@ -124,7 +124,7 @@ for [r1, r2] in product([0, 0.5, 1.0], repeat=2):
network_params=netparams,
prob_neighbor_spread=0,
),
num_trials=5,
iterations=5,
max_steps=300,
dump=False,
).run()

2
examples/programmatic/programmatic_sim.py
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@@ -38,7 +38,7 @@ simulation = Simulation(
name="Programmatic",
model=ProgrammaticEnv,
seed='Program',
num_trials=1,
iterations=1,
max_time=100,
dump=False,
)

4
examples/pubcrawl/pubcrawl_sim.py
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@@ -178,10 +178,10 @@ class Police(FSM):
sim = Simulation(
model=CityPubs,
name="pubcrawl",
num_trials=3,
iterations=3,
max_steps=10,
dump=False,
model_params=dict(
parameters=dict(
network_generator=nx.empty_graph,
network_params={"n": 30},
model=CityPubs,

2
examples/rabbits/rabbit_improved_sim.py
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@@ -147,7 +147,7 @@ class RandomAccident(BaseAgent):
self.debug("Rabbits alive: {}".format(rabbits_alive))
sim = Simulation(model=RabbitsImprovedEnv, max_time=100, seed="MySeed", num_trials=1)
sim = Simulation(model=RabbitsImprovedEnv, max_time=100, seed="MySeed", iterations=1)
if __name__ == "__main__":
sim.run()

2
examples/rabbits/rabbits_basic_sim.py
View File

@@ -155,7 +155,7 @@ class RandomAccident(BaseAgent):
sim = Simulation(model=RabbitEnv, max_time=100, seed="MySeed", num_trials=1)
sim = Simulation(model=RabbitEnv, max_time=100, seed="MySeed", iterations=1)
if __name__ == "__main__":
sim.run()

2
examples/random_delays/random_delays_sim.py
View File

@@ -38,7 +38,7 @@ class RandomEnv(Environment):
s = Simulation(
name="Programmatic",
model=RandomEnv,
num_trials=1,
iterations=1,
max_time=100,
dump=False,
)

23
examples/terrorism/TerroristNetworkModel_sim.py
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@@ -2,6 +2,7 @@ import networkx as nx
from soil.agents import Geo, NetworkAgent, FSM, custom, state, default_state
from soil import Environment, Simulation
from soil.parameters import *
from soil.utils import int_seed
class TerroristEnvironment(Environment):
@@ -38,9 +39,8 @@ class TerroristEnvironment(Environment):
HavenModel
], [self.ratio_civil, self.ratio_leader, self.ratio_training, self.ratio_haven])
@staticmethod
def generator(*args, **kwargs):
return nx.random_geometric_graph(*args, **kwargs)
def generator(self, *args, **kwargs):
return nx.random_geometric_graph(*args, **kwargs, seed=int_seed(self._seed))
class TerroristSpreadModel(FSM, Geo):
"""
@@ -137,7 +137,7 @@ class TerroristSpreadModel(FSM, Geo):
def ego_search(self, steps=1, center=False, agent=None, **kwargs):
"""Get a list of nodes in the ego network of *node* of radius *steps*"""
node = agent.node_id
node = agent.node_id if agent else self.node_id
G = self.subgraph(**kwargs)
return nx.ego_graph(G, node, center=center, radius=steps).nodes()
@@ -279,26 +279,26 @@ class TerroristNetworkModel(TerroristSpreadModel):
)
)
neighbours = set(
agent.id
agent.unique_id
for agent in self.get_neighbors(agent_class=TerroristNetworkModel)
)
search = (close_ups | step_neighbours) - neighbours
for agent in self.get_agents(search):
social_distance = 1 / self.shortest_path_length(agent.id)
spatial_proximity = 1 - self.get_distance(agent.id)
social_distance = 1 / self.shortest_path_length(agent.unique_id)
spatial_proximity = 1 - self.get_distance(agent.unique_id)
prob_new_interaction = (
self.weight_social_distance * social_distance
+ self.weight_link_distance * spatial_proximity
)
if (
agent["id"] == agent.civilian.id
agent.state_id == "civilian"
and self.random.random() < prob_new_interaction
):
self.add_edge(agent)
break
def get_distance(self, target):
source_x, source_y = nx.get_node_attributes(self.G, "pos")[self.id]
source_x, source_y = nx.get_node_attributes(self.G, "pos")[self.unique_id]
target_x, target_y = nx.get_node_attributes(self.G, "pos")[target]
dx = abs(source_x - target_x)
dy = abs(source_y - target_y)
@@ -306,16 +306,17 @@ class TerroristNetworkModel(TerroristSpreadModel):
def shortest_path_length(self, target):
try:
return nx.shortest_path_length(self.G, self.id, target)
return nx.shortest_path_length(self.G, self.unique_id, target)
except nx.NetworkXNoPath:
return float("inf")
sim = Simulation(
model=TerroristEnvironment,
num_trials=1,
iterations=1,
name="TerroristNetworkModel_sim",
max_steps=150,
seed="default2",
skip_test=False,
dump=False,
)

6363
examples/tutorial/soil_tutorial.ipynb
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