black formatting

examples/custom_generator/mymodule.py

@@ -2,11 +2,12 @@ from networkx import Graph
 import random
 import networkx as nx
 
+
 def mygenerator(n=5, n_edges=5):
-    '''
+    """
     Just a simple generator that creates a network with n nodes and
     n_edges edges. Edges are assigned randomly, only avoiding self loops.
-    '''
+    """
     G = nx.Graph()
 
     for i in range(n):
@@ -19,9 +20,3 @@ def mygenerator(n=5, n_edges=5):
         n_out = random.choice(nodes)
         G.add_edge(n_in, n_out)
     return G
-    
-
-
-
-
-    

examples/custom_timeouts/custom_timeouts.py

@@ -2,33 +2,36 @@ from soil.agents import FSM, state, default_state
 
 
 class Fibonacci(FSM):
-    '''Agent that only executes in t_steps that are Fibonacci numbers'''
+    """Agent that only executes in t_steps that are Fibonacci numbers"""
 
-    defaults = {
+    defaults = {"prev": 1}
-        'prev': 1
-    }
 
     @default_state
     @state
     def counting(self):
-        self.log('Stopping at {}'.format(self.now))
+        self.log("Stopping at {}".format(self.now))
-        prev, self['prev'] = self['prev'], max([self.now, self['prev']])
+        prev, self["prev"] = self["prev"], max([self.now, self["prev"]])
         return None, self.env.timeout(prev)
 
 
 class Odds(FSM):
-    '''Agent that only executes in odd t_steps'''
+    """Agent that only executes in odd t_steps"""
+
     @default_state
     @state
     def odds(self):
-        self.log('Stopping at {}'.format(self.now))
+        self.log("Stopping at {}".format(self.now))
-        return None, self.env.timeout(1+self.now%2)
+        return None, self.env.timeout(1 + self.now % 2)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     from soil import Simulation
-    s = Simulation(network_agents=[{'ids': [0], 'agent_class': Fibonacci},
+
-                                   {'ids': [1], 'agent_class': Odds}],
+    s = Simulation(
+        network_agents=[
+            {"ids": [0], "agent_class": Fibonacci},
+            {"ids": [1], "agent_class": Odds},
+        ],
         network_params={"generator": "complete_graph", "n": 2},
         max_time=100,
     )

examples/mesa/server.py

@@ -14,16 +14,18 @@ def network_portrayal(env):
     # The model ensures there is 0 or 1 agent per node
 
     portrayal = dict()
-    wealths = {node_id: data['agent'].wealth for (node_id, data) in env.G.nodes(data=True)}
+    wealths = {
+        node_id: data["agent"].wealth for (node_id, data) in env.G.nodes(data=True)
+    }
     portrayal["nodes"] = [
         {
             "id": node_id,
-            "size": 2*(wealth+1),
+            "size": 2 * (wealth + 1),
             "color": "#CC0000" if wealth == 0 else "#007959",
             # "color": "#CC0000",
             "label": f"{node_id}: {wealth}",
-        } for (node_id, wealth) in wealths.items()
+        }
-
+        for (node_id, wealth) in wealths.items()
     ]
 
     portrayal["edges"] = [
@@ -41,7 +43,7 @@ def gridPortrayal(agent):
     :param agent:  the agent in the simulation
     :return: the portrayal dictionary
     """
-    color = max(10, min(agent.wealth*10, 100))
+    color = max(10, min(agent.wealth * 10, 100))
     return {
         "Shape": "rect",
         "w": 1,
@@ -52,7 +54,7 @@ def gridPortrayal(agent):
         "Text": agent.unique_id,
         "x": agent.pos[0],
         "y": agent.pos[1],
-        "Color": f"rgba(31, 10, 255, 0.{color})"
+        "Color": f"rgba(31, 10, 255, 0.{color})",
     }
 
 
@@ -89,15 +91,19 @@ model_params = {
         1,
         description="Grid width",
     ),
-    "agent_class": UserSettableParameter('choice', 'Agent class', value='MoneyAgent',
+    "agent_class": UserSettableParameter(
-                                         choices=['MoneyAgent', 'SocialMoneyAgent']),
+        "choice",
+        "Agent class",
+        value="MoneyAgent",
+        choices=["MoneyAgent", "SocialMoneyAgent"],
+    ),
     "generator": graph_generator,
 }
 
 
-canvas_element = CanvasGrid(gridPortrayal,
+canvas_element = CanvasGrid(
-                            model_params["width"].value,
+    gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500
-                            model_params["height"].value, 500, 500)
+)
 
 
 server = ModularServer(

examples/mesa/social_wealth.py

@@ -1,9 +1,10 @@
-'''
+"""
 This is an example that adds soil agents and environment in a normal
 mesa workflow.
-'''
+"""
 from mesa import Agent as MesaAgent
 from mesa.space import MultiGrid
+
 # from mesa.time import RandomActivation
 from mesa.datacollection import DataCollector
 from mesa.batchrunner import BatchRunner
@@ -12,12 +13,13 @@ import networkx as nx
 
 from soil import NetworkAgent, Environment, serialization
 
+
 def compute_gini(model):
     agent_wealths = [agent.wealth for agent in model.agents]
     x = sorted(agent_wealths)
     N = len(list(model.agents))
-    B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
+    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
-    return (1 + (1/N) - 2*B)
+    return 1 + (1 / N) - 2 * B
 
 
 class MoneyAgent(MesaAgent):
@@ -32,9 +34,8 @@ class MoneyAgent(MesaAgent):
 
     def move(self):
         possible_steps = self.model.grid.get_neighborhood(
-            self.pos,
+            self.pos, moore=True, include_center=False
-            moore=True,
+        )
-            include_center=False)
         new_position = self.random.choice(possible_steps)
         self.model.grid.move_agent(self, new_position)
 
@@ -69,6 +70,7 @@ class SocialMoneyAgent(NetworkAgent, MoneyAgent):
             other.wealth += 1
             self.wealth -= 1
 
+
 def graph_generator(n=5):
     G = nx.Graph()
     for ix in range(n):
@@ -78,16 +80,22 @@ def graph_generator(n=5):
 
 class MoneyEnv(Environment):
     """A model with some number of agents."""
-    def __init__(self, width, height, N, generator=graph_generator,
+
+    def __init__(
+        self,
+        width,
+        height,
+        N,
+        generator=graph_generator,
         agent_class=SocialMoneyAgent,
-                 topology=None, **kwargs):
+        topology=None,
+        **kwargs
+    ):
 
         generator = serialization.deserialize(generator)
         agent_class = serialization.deserialize(agent_class, globs=globals())
         topology = generator(n=N)
-        super().__init__(topology=topology,
+        super().__init__(topology=topology, N=N, **kwargs)
-                         N=N,
-                         **kwargs)
         self.grid = MultiGrid(width, height, False)
 
         self.populate_network(agent_class=agent_class)
@@ -99,26 +107,29 @@ class MoneyEnv(Environment):
             self.grid.place_agent(agent, (x, y))
 
         self.datacollector = DataCollector(
-            model_reporters={"Gini": compute_gini},
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
-            agent_reporters={"Wealth": "wealth"})
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
-    fixed_params = {"generator": nx.complete_graph,
+    fixed_params = {
+        "generator": nx.complete_graph,
         "width": 10,
-                    "network_agents": [{"agent_class": SocialMoneyAgent,
+        "network_agents": [{"agent_class": SocialMoneyAgent, "weight": 1}],
-                                       'weight': 1}],
+        "height": 10,
-                    "height": 10}
+    }
 
     variable_params = {"N": range(10, 100, 10)}
 
-    batch_run = BatchRunner(MoneyEnv,
+    batch_run = BatchRunner(
+        MoneyEnv,
         variable_parameters=variable_params,
         fixed_parameters=fixed_params,
         iterations=5,
         max_steps=100,
-                            model_reporters={"Gini": compute_gini})
+        model_reporters={"Gini": compute_gini},
+    )
     batch_run.run_all()
 
     run_data = batch_run.get_model_vars_dataframe()

examples/mesa/wealth.py

@@ -4,24 +4,26 @@ from mesa.time import RandomActivation
 from mesa.datacollection import DataCollector
 from mesa.batchrunner import BatchRunner
 
+
 def compute_gini(model):
     agent_wealths = [agent.wealth for agent in model.schedule.agents]
     x = sorted(agent_wealths)
     N = model.num_agents
-    B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
+    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
-    return (1 + (1/N) - 2*B)
+    return 1 + (1 / N) - 2 * B
+
 
 class MoneyAgent(Agent):
-    """ An agent with fixed initial wealth."""
+    """An agent with fixed initial wealth."""
+
     def __init__(self, unique_id, model):
         super().__init__(unique_id, model)
         self.wealth = 1
 
     def move(self):
         possible_steps = self.model.grid.get_neighborhood(
-            self.pos,
+            self.pos, moore=True, include_center=False
-            moore=True,
+        )
-            include_center=False)
         new_position = self.random.choice(possible_steps)
         self.model.grid.move_agent(self, new_position)
 
@@ -37,8 +39,10 @@ class MoneyAgent(Agent):
         if self.wealth > 0:
             self.give_money()
 
+
 class MoneyModel(Model):
     """A model with some number of agents."""
+
     def __init__(self, N, width, height):
         self.num_agents = N
         self.grid = MultiGrid(width, height, True)
@@ -55,29 +59,29 @@ class MoneyModel(Model):
             self.grid.place_agent(a, (x, y))
 
         self.datacollector = DataCollector(
-            model_reporters={"Gini": compute_gini},
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
-            agent_reporters={"Wealth": "wealth"})
+        )
 
     def step(self):
         self.datacollector.collect(self)
         self.schedule.step()
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
-    fixed_params = {"width": 10,
+    fixed_params = {"width": 10, "height": 10}
-                    "height": 10}
     variable_params = {"N": range(10, 500, 10)}
 
-    batch_run = BatchRunner(MoneyModel,
+    batch_run = BatchRunner(
+        MoneyModel,
         variable_params,
         fixed_params,
         iterations=5,
         max_steps=100,
-                            model_reporters={"Gini": compute_gini})
+        model_reporters={"Gini": compute_gini},
+    )
     batch_run.run_all()
 
     run_data = batch_run.get_model_vars_dataframe()
     run_data.head()
     print(run_data.Gini)
-

examples/newsspread/newsspread.py

@@ -3,84 +3,83 @@ import logging
 
 
 class DumbViewer(FSM, NetworkAgent):
-    '''
+    """
     A viewer that gets infected via TV (if it has one) and tries to infect
     its neighbors once it's infected.
-    '''
+    """
+
     defaults = {
-        'prob_neighbor_spread': 0.5,
+        "prob_neighbor_spread": 0.5,
-        'prob_tv_spread': 0.1,
+        "prob_tv_spread": 0.1,
     }
 
     @default_state
     @state
     def neutral(self):
-        if self['has_tv']:
+        if self["has_tv"]:
-            if self.prob(self.model['prob_tv_spread']):
+            if self.prob(self.model["prob_tv_spread"]):
                 return self.infected
 
     @state
     def infected(self):
         for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
-            if self.prob(self.model['prob_neighbor_spread']):
+            if self.prob(self.model["prob_neighbor_spread"]):
                 neighbor.infect()
 
     def infect(self):
-        '''
+        """
         This is not a state. It is a function that other agents can use to try to
         infect this agent. DumbViewer always gets infected, but other agents like
         HerdViewer might not become infected right away
-        '''
+        """
 
         self.set_state(self.infected)
 
 
 class HerdViewer(DumbViewer):
-    '''
+    """
     A viewer whose probability of infection depends on the state of its neighbors.
-    '''
+    """
 
     def infect(self):
-        '''Notice again that this is NOT a state. See DumbViewer.infect for reference'''
+        """Notice again that this is NOT a state. See DumbViewer.infect for reference"""
         infected = self.count_neighboring_agents(state_id=self.infected.id)
         total = self.count_neighboring_agents()
-        prob_infect = self.model['prob_neighbor_spread'] * infected/total
+        prob_infect = self.model["prob_neighbor_spread"] * infected / total
-        self.debug('prob_infect', prob_infect)
+        self.debug("prob_infect", prob_infect)
         if self.prob(prob_infect):
             self.set_state(self.infected)
 
 
 class WiseViewer(HerdViewer):
-    '''
+    """
     A viewer that can change its mind.
-    '''
+    """
 
     defaults = {
-        'prob_neighbor_spread': 0.5,
+        "prob_neighbor_spread": 0.5,
-        'prob_neighbor_cure': 0.25,
+        "prob_neighbor_cure": 0.25,
-        'prob_tv_spread': 0.1,
+        "prob_tv_spread": 0.1,
     }
 
     @state
     def cured(self):
-        prob_cure = self.model['prob_neighbor_cure']
+        prob_cure = self.model["prob_neighbor_cure"]
         for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
             if self.prob(prob_cure):
                 try:
                     neighbor.cure()
                 except AttributeError:
-                    self.debug('Viewer {} cannot be cured'.format(neighbor.id))
+                    self.debug("Viewer {} cannot be cured".format(neighbor.id))
 
     def cure(self):
         self.set_state(self.cured.id)
 
     @state
     def infected(self):
-        cured = max(self.count_neighboring_agents(self.cured.id),
+        cured = max(self.count_neighboring_agents(self.cured.id), 1.0)
-                    1.0)
+        infected = max(self.count_neighboring_agents(self.infected.id), 1.0)
-        infected = max(self.count_neighboring_agents(self.infected.id),
+        prob_cure = self.model["prob_neighbor_cure"] * (cured / infected)
-                       1.0)
-        prob_cure = self.model['prob_neighbor_cure'] * (cured/infected)
         if self.prob(prob_cure):
             return self.cured
         return self.set_state(super().infected)

examples/programmatic/programmatic.py

@@ -1,6 +1,6 @@
-'''
+"""
 Example of a fully programmatic simulation, without definition files.
-'''
+"""
 from soil import Simulation, agents
 from networkx import Graph
 import logging
@@ -14,21 +14,22 @@ def mygenerator():
 
 
 class MyAgent(agents.FSM):
-
     @agents.default_state
     @agents.state
     def neutral(self):
-        self.debug('I am running')
+        self.debug("I am running")
         if agents.prob(0.2):
-            self.info('This runs 2/10 times on average')
+            self.info("This runs 2/10 times on average")
 
 
-s = Simulation(name='Programmatic',
+s = Simulation(
-               network_params={'generator': mygenerator},
+    name="Programmatic",
+    network_params={"generator": mygenerator},
     num_trials=1,
     max_time=100,
     agent_class=MyAgent,
-               dry_run=True)
+    dry_run=True,
+)
 
 
 # By default, logging will only print WARNING logs (and above).

examples/pubcrawl/pubcrawl.py

@@ -5,7 +5,8 @@ import logging
 
 
 class CityPubs(Environment):
-    '''Environment with Pubs'''
+    """Environment with Pubs"""
+
     level = logging.INFO
 
     def __init__(self, *args, number_of_pubs=3, pub_capacity=10, **kwargs):
@@ -13,51 +14,52 @@ class CityPubs(Environment):
         pubs = {}
         for i in range(number_of_pubs):
             newpub = {
-                'name': 'The awesome pub #{}'.format(i),
+                "name": "The awesome pub #{}".format(i),
-                'open': True,
+                "open": True,
-                'capacity': pub_capacity,
+                "capacity": pub_capacity,
-                'occupancy': 0,
+                "occupancy": 0,
             }
-            pubs[newpub['name']] = newpub
+            pubs[newpub["name"]] = newpub
-        self['pubs'] = pubs
+        self["pubs"] = pubs
 
     def enter(self, pub_id, *nodes):
-        '''Agents will try to enter. The pub checks if it is possible'''
+        """Agents will try to enter. The pub checks if it is possible"""
         try:
-            pub = self['pubs'][pub_id]
+            pub = self["pubs"][pub_id]
         except KeyError:
-            raise ValueError('Pub {} is not available'.format(pub_id))
+            raise ValueError("Pub {} is not available".format(pub_id))
-        if not pub['open'] or (pub['capacity'] < (len(nodes) + pub['occupancy'])):
+        if not pub["open"] or (pub["capacity"] < (len(nodes) + pub["occupancy"])):
             return False
-        pub['occupancy'] += len(nodes)
+        pub["occupancy"] += len(nodes)
         for node in nodes:
-            node['pub'] = pub_id
+            node["pub"] = pub_id
         return True
 
     def available_pubs(self):
-        for pub in self['pubs'].values():
+        for pub in self["pubs"].values():
-            if pub['open'] and (pub['occupancy'] < pub['capacity']):
+            if pub["open"] and (pub["occupancy"] < pub["capacity"]):
-                yield pub['name']
+                yield pub["name"]
 
     def exit(self, pub_id, *node_ids):
-        '''Agents will notify the pub they want to leave'''
+        """Agents will notify the pub they want to leave"""
         try:
-            pub = self['pubs'][pub_id]
+            pub = self["pubs"][pub_id]
         except KeyError:
-            raise ValueError('Pub {} is not available'.format(pub_id))
+            raise ValueError("Pub {} is not available".format(pub_id))
         for node_id in node_ids:
             node = self.get_agent(node_id)
-            if pub_id == node['pub']:
+            if pub_id == node["pub"]:
-                del node['pub']
+                del node["pub"]
-                pub['occupancy'] -= 1
+                pub["occupancy"] -= 1
 
 
 class Patron(FSM, NetworkAgent):
-    '''Agent that looks for friends to drink with. It will do three things:
+    """Agent that looks for friends to drink with. It will do three things:
     1) Look for other patrons to drink with
     2) Look for a bar where the agent and other agents in the same group can get in.
     3) While in the bar, patrons only drink, until they get drunk and taken home.
-    '''
+    """
+
     level = logging.DEBUG
 
     pub = None
@@ -69,13 +71,13 @@ class Patron(FSM, NetworkAgent):
     @default_state
     @state
     def looking_for_friends(self):
-        '''Look for friends to drink with'''
+        """Look for friends to drink with"""
-        self.info('I am looking for friends')
+        self.info("I am looking for friends")
-        available_friends = list(self.get_agents(drunk=False,
+        available_friends = list(
-                                                 pub=None,
+            self.get_agents(drunk=False, pub=None, state_id=self.looking_for_friends.id)
-                                                 state_id=self.looking_for_friends.id))
+        )
         if not available_friends:
-            self.info('Life sucks and I\'m alone!')
+            self.info("Life sucks and I'm alone!")
             return self.at_home
         befriended = self.try_friends(available_friends)
         if befriended:
@@ -83,93 +85,91 @@ class Patron(FSM, NetworkAgent):
 
     @state
     def looking_for_pub(self):
-        '''Look for a pub that accepts me and my friends'''
+        """Look for a pub that accepts me and my friends"""
-        if self['pub'] != None:
+        if self["pub"] != None:
             return self.sober_in_pub
-        self.debug('I am looking for a pub')
+        self.debug("I am looking for a pub")
         group = list(self.get_neighboring_agents())
         for pub in self.model.available_pubs():
-            self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group)))
+            self.debug("We're trying to get into {}: total: {}".format(pub, len(group)))
             if self.model.enter(pub, self, *group):
-                self.info('We\'re all {} getting in {}!'.format(len(group), pub))
+                self.info("We're all {} getting in {}!".format(len(group), pub))
                 return self.sober_in_pub
 
     @state
     def sober_in_pub(self):
-        '''Drink up.'''
+        """Drink up."""
         self.drink()
-        if self['pints'] > self['max_pints']:
+        if self["pints"] > self["max_pints"]:
             return self.drunk_in_pub
 
     @state
     def drunk_in_pub(self):
-        '''I'm out. Take me home!'''
+        """I'm out. Take me home!"""
-        self.info('I\'m so drunk. Take me home!')
+        self.info("I'm so drunk. Take me home!")
-        self['drunk'] = True
+        self["drunk"] = True
         if self.kicked_out:
             return self.at_home
         pass  # out drun
 
     @state
     def at_home(self):
-        '''The end'''
+        """The end"""
         others = self.get_agents(state_id=Patron.at_home.id, limit_neighbors=True)
-        self.debug('I\'m home. Just like {} of my friends'.format(len(others)))
+        self.debug("I'm home. Just like {} of my friends".format(len(others)))
 
     def drink(self):
-        self['pints'] += 1
+        self["pints"] += 1
-        self.debug('Cheers to that')
+        self.debug("Cheers to that")
 
     def kick_out(self):
         self.kicked_out = True
 
     def befriend(self, other_agent, force=False):
-        '''
+        """
         Try to become friends with another agent. The chances of
         success depend on both agents' openness.
-        '''
+        """
-        if force or self['openness'] > self.random.random():
+        if force or self["openness"] > self.random.random():
             self.add_edge(self, other_agent)
-            self.info('Made some friend {}'.format(other_agent))
+            self.info("Made some friend {}".format(other_agent))
             return True
         return False
 
     def try_friends(self, others):
-        ''' Look for random agents around me and try to befriend them'''
+        """Look for random agents around me and try to befriend them"""
         befriended = False
-        k = int(10*self['openness'])
+        k = int(10 * self["openness"])
         self.random.shuffle(others)
         for friend in islice(others, k):  # random.choice >= 3.7
             if friend == self:
                 continue
             if friend.befriend(self):
                 self.befriend(friend, force=True)
-                self.debug('Hooray! new friend: {}'.format(friend.id))
+                self.debug("Hooray! new friend: {}".format(friend.id))
                 befriended = True
             else:
-                self.debug('{} does not want to be friends'.format(friend.id))
+                self.debug("{} does not want to be friends".format(friend.id))
         return befriended
 
 
 class Police(FSM):
-    '''Simple agent to take drunk people out of pubs.'''
+    """Simple agent to take drunk people out of pubs."""
+
     level = logging.INFO
 
     @default_state
     @state
     def patrol(self):
-        drunksters = list(self.get_agents(drunk=True,
+        drunksters = list(self.get_agents(drunk=True, state_id=Patron.drunk_in_pub.id))
-                                          state_id=Patron.drunk_in_pub.id))
         for drunk in drunksters:
-            self.info('Kicking out the trash: {}'.format(drunk.id))
+            self.info("Kicking out the trash: {}".format(drunk.id))
             drunk.kick_out()
         else:
-            self.info('No trash to take out. Too bad.')
+            self.info("No trash to take out. Too bad.")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     from soil import simulation
-    simulation.run_from_config('pubcrawl.yml',
+
-                               dry_run=True,
+    simulation.run_from_config("pubcrawl.yml", dry_run=True, dump=None, parallel=False)
-                               dump=None,
-                               parallel=False)

examples/rabbits/basic/rabbit_agents.py

@@ -5,7 +5,6 @@ import math
 
 
 class RabbitEnv(Environment):
-
     @property
     def num_rabbits(self):
         return self.count_agents(agent_class=Rabbit)
@@ -27,7 +26,7 @@ class Rabbit(NetworkAgent, FSM):
     @default_state
     @state
     def newborn(self):
-        self.info('I am a newborn.')
+        self.info("I am a newborn.")
         self.age = 0
         self.offspring = 0
         return self.youngling
@@ -36,7 +35,7 @@ class Rabbit(NetworkAgent, FSM):
     def youngling(self):
         self.age += 1
         if self.age >= self.sexual_maturity:
-            self.info(f'I am fertile! My age is {self.age}')
+            self.info(f"I am fertile! My age is {self.age}")
             return self.fertile
 
     @state
@@ -60,11 +59,11 @@ class Male(Rabbit):
             return self.dead
 
         # Males try to mate
-        for f in self.model.agents(agent_class=Female,
+        for f in self.model.agents(
-                                   state_id=Female.fertile.id,
+            agent_class=Female, state_id=Female.fertile.id, limit=self.max_females
-                                   limit=self.max_females):
+        ):
-            self.debug('FOUND A FEMALE: ', repr(f), self.mating_prob)
+            self.debug("FOUND A FEMALE: ", repr(f), self.mating_prob)
-            if self.prob(self['mating_prob']):
+            if self.prob(self["mating_prob"]):
                 f.impregnate(self)
                 break  # Take a break
 
@@ -83,14 +82,14 @@ class Female(Rabbit):
             return self.pregnant
 
     def impregnate(self, male):
-        self.info(f'impregnated by {repr(male)}')
+        self.info(f"impregnated by {repr(male)}")
         self.mate = male
         self.pregnancy = 0
-        self.number_of_babies = int(8+4*self.random.random())
+        self.number_of_babies = int(8 + 4 * self.random.random())
 
     @state
     def pregnant(self):
-        self.info('I am pregnant')
+        self.info("I am pregnant")
         self.age += 1
 
         if self.age >= self.life_expectancy:
@@ -100,18 +99,17 @@ class Female(Rabbit):
             self.pregnancy += 1
             return
 
-        self.info('Having {} babies'.format(self.number_of_babies))
+        self.info("Having {} babies".format(self.number_of_babies))
         for i in range(self.number_of_babies):
             state = {}
             agent_class = self.random.choice([Male, Female])
-            child = self.model.add_node(agent_class=agent_class,
+            child = self.model.add_node(agent_class=agent_class, **state)
-                                        **state)
             child.add_edge(self)
             try:
                 child.add_edge(self.mate)
                 self.model.agents[self.mate].offspring += 1
             except ValueError:
-                self.debug('The father has passed away')
+                self.debug("The father has passed away")
 
             self.offspring += 1
         self.mate = None
@@ -119,32 +117,34 @@ class Female(Rabbit):
         return self.fertile
 
     def die(self):
-        if 'pregnancy' in self and self['pregnancy'] > -1:
+        if "pregnancy" in self and self["pregnancy"] > -1:
-            self.info('A mother has died carrying a baby!!')
+            self.info("A mother has died carrying a baby!!")
         return super().die()
 
 
 class RandomAccident(BaseAgent):
-
     def step(self):
         rabbits_alive = self.model.G.number_of_nodes()
 
         if not rabbits_alive:
             return self.die()
 
-        prob_death = self.model.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
+        prob_death = self.model.get("prob_death", 1e-100) * math.floor(
-        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
+            math.log10(max(1, rabbits_alive))
+        )
+        self.debug("Killing some rabbits with prob={}!".format(prob_death))
         for i in self.iter_agents(agent_class=Rabbit):
             if i.state_id == i.dead.id:
                 continue
             if self.prob(prob_death):
-                self.info('I killed a rabbit: {}'.format(i.id))
+                self.info("I killed a rabbit: {}".format(i.id))
                 rabbits_alive -= 1
                 i.die()
-        self.debug('Rabbits alive: {}'.format(rabbits_alive))
+        self.debug("Rabbits alive: {}".format(rabbits_alive))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     from soil import easy
-    with easy('rabbits.yml') as sim:
+
+    with easy("rabbits.yml") as sim:
         sim.run()

examples/rabbits/improved/rabbit_agents.py

@@ -7,7 +7,6 @@ import math
 
 
 class RabbitEnv(Environment):
-
     @property
     def num_rabbits(self):
         return self.count_agents(agent_class=Rabbit)
@@ -36,7 +35,7 @@ class Rabbit(FSM, NetworkAgent):
     @default_state
     @state
     def newborn(self):
-        self.info('I am a newborn.')
+        self.info("I am a newborn.")
         self.birth = self.now
         self.offspring = 0
         return self.youngling, Delta(self.sexual_maturity - self.age)
@@ -44,7 +43,7 @@ class Rabbit(FSM, NetworkAgent):
     @state
     def youngling(self):
         if self.age >= self.sexual_maturity:
-            self.info(f'I am fertile! My age is {self.age}')
+            self.info(f"I am fertile! My age is {self.age}")
             return self.fertile
 
     @state
@@ -66,11 +65,11 @@ class Male(Rabbit):
             return self.dead
 
         # Males try to mate
-        for f in self.model.agents(agent_class=Female,
+        for f in self.model.agents(
-                                   state_id=Female.fertile.id,
+            agent_class=Female, state_id=Female.fertile.id, limit=self.max_females
-                                   limit=self.max_females):
+        ):
-            self.debug('FOUND A FEMALE: ', repr(f), self.mating_prob)
+            self.debug("FOUND A FEMALE: ", repr(f), self.mating_prob)
-            if self.prob(self['mating_prob']):
+            if self.prob(self["mating_prob"]):
                 f.impregnate(self)
                 break  # Do not try to impregnate other females
 
@@ -94,32 +93,31 @@ class Female(Rabbit):
         return self.now - self.conception
 
     def impregnate(self, male):
-        self.info(f'impregnated by {repr(male)}')
+        self.info(f"impregnated by {repr(male)}")
         self.mate = male
         self.conception = self.now
-        self.number_of_babies = int(8+4*self.random.random())
+        self.number_of_babies = int(8 + 4 * self.random.random())
 
     @state
     def pregnant(self):
-        self.debug('I am pregnant')
+        self.debug("I am pregnant")
 
         if self.age > self.life_expectancy:
             self.info("Dying before giving birth")
             return self.die()
 
         if self.pregnancy >= self.gestation:
-            self.info('Having {} babies'.format(self.number_of_babies))
+            self.info("Having {} babies".format(self.number_of_babies))
             for i in range(self.number_of_babies):
                 state = {}
                 agent_class = self.random.choice([Male, Female])
-                child = self.model.add_node(agent_class=agent_class,
+                child = self.model.add_node(agent_class=agent_class, **state)
-                                            **state)
                 child.add_edge(self)
                 if self.mate:
                     child.add_edge(self.mate)
                     self.mate.offspring += 1
                 else:
-                    self.debug('The father has passed away')
+                    self.debug("The father has passed away")
 
                 self.offspring += 1
             self.mate = None
@@ -127,31 +125,33 @@ class Female(Rabbit):
 
     def die(self):
         if self.pregnancy is not None:
-            self.info('A mother has died carrying a baby!!')
+            self.info("A mother has died carrying a baby!!")
         return super().die()
 
 
 class RandomAccident(BaseAgent):
-
     def step(self):
         rabbits_alive = self.model.G.number_of_nodes()
 
         if not rabbits_alive:
             return self.die()
 
-        prob_death = self.model.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
+        prob_death = self.model.get("prob_death", 1e-100) * math.floor(
-        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
+            math.log10(max(1, rabbits_alive))
+        )
+        self.debug("Killing some rabbits with prob={}!".format(prob_death))
         for i in self.iter_agents(agent_class=Rabbit):
             if i.state_id == i.dead.id:
                 continue
             if self.prob(prob_death):
-                self.info('I killed a rabbit: {}'.format(i.id))
+                self.info("I killed a rabbit: {}".format(i.id))
                 rabbits_alive -= 1
                 i.die()
-        self.debug('Rabbits alive: {}'.format(rabbits_alive))
+        self.debug("Rabbits alive: {}".format(rabbits_alive))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     from soil import easy
-    with easy('rabbits.yml') as sim:
+
+    with easy("rabbits.yml") as sim:
         sim.run()

examples/random_delays/random_delays.py

@@ -1,42 +1,43 @@
-'''
+"""
 Example of setting a 
 Example of a fully programmatic simulation, without definition files.
-'''
+"""
 from soil import Simulation, agents
 from soil.time import Delta
 
 
-
 class MyAgent(agents.FSM):
-    '''
+    """
     An agent that first does a ping
-    '''
+    """
 
-    defaults = {'pong_counts': 2}
+    defaults = {"pong_counts": 2}
 
     @agents.default_state
     @agents.state
     def ping(self):
-        self.info('Ping')
+        self.info("Ping")
-        return self.pong, Delta(self.random.expovariate(1/16))
+        return self.pong, Delta(self.random.expovariate(1 / 16))
 
     @agents.state
     def pong(self):
-        self.info('Pong')
+        self.info("Pong")
         self.pong_counts -= 1
         self.info(str(self.pong_counts))
         if self.pong_counts < 1:
             return self.die()
-        return None, Delta(self.random.expovariate(1/16))
+        return None, Delta(self.random.expovariate(1 / 16))
 
 
-s = Simulation(name='Programmatic',
+s = Simulation(
-               network_agents=[{'agent_class': MyAgent, 'id': 0}],
+    name="Programmatic",
-               topology={'nodes': [{'id': 0}], 'links': []},
+    network_agents=[{"agent_class": MyAgent, "id": 0}],
+    topology={"nodes": [{"id": 0}], "links": []},
     num_trials=1,
     max_time=100,
     agent_class=MyAgent,
-               dry_run=True)
+    dry_run=True,
+)
 
 
 envs = s.run()

examples/terrorism/TerroristNetworkModel.py

@@ -20,56 +20,83 @@ class TerroristSpreadModel(FSM, Geo):
     def __init__(self, model=None, unique_id=0, state=()):
         super().__init__(model=model, unique_id=unique_id, state=state)
 
-        self.information_spread_intensity = model.environment_params['information_spread_intensity']
+        self.information_spread_intensity = model.environment_params[
-        self.terrorist_additional_influence = model.environment_params['terrorist_additional_influence']
+            "information_spread_intensity"
-        self.prob_interaction = model.environment_params['prob_interaction']
+        ]
+        self.terrorist_additional_influence = model.environment_params[
+            "terrorist_additional_influence"
+        ]
+        self.prob_interaction = model.environment_params["prob_interaction"]
 
-        if self['id'] == self.civilian.id:       # Civilian
+        if self["id"] == self.civilian.id:  # Civilian
             self.mean_belief = self.random.uniform(0.00, 0.5)
-        elif self['id'] == self.terrorist.id:     # Terrorist
+        elif self["id"] == self.terrorist.id:  # Terrorist
             self.mean_belief = self.random.uniform(0.8, 1.00)
-        elif self['id'] == self.leader.id:     # Leader
+        elif self["id"] == self.leader.id:  # Leader
             self.mean_belief = 1.00
         else:
-            raise Exception('Invalid state id: {}'.format(self['id']))
+            raise Exception("Invalid state id: {}".format(self["id"]))
-
-        if 'min_vulnerability' in model.environment_params:
-            self.vulnerability = self.random.uniform( model.environment_params['min_vulnerability'], model.environment_params['max_vulnerability'] )
-        else :
-            self.vulnerability = self.random.uniform( 0, model.environment_params['max_vulnerability'] )
 
+        if "min_vulnerability" in model.environment_params:
+            self.vulnerability = self.random.uniform(
+                model.environment_params["min_vulnerability"],
+                model.environment_params["max_vulnerability"],
+            )
+        else:
+            self.vulnerability = self.random.uniform(
+                0, model.environment_params["max_vulnerability"]
+            )
 
     @state
     def civilian(self):
         neighbours = list(self.get_neighboring_agents(agent_class=TerroristSpreadModel))
         if len(neighbours) > 0:
             # Only interact with some of the neighbors
-            interactions = list(n for n in neighbours if self.random.random() <= self.prob_interaction)
+            interactions = list(
-            influence = sum( self.degree(i) for i in interactions )
+                n for n in neighbours if self.random.random() <= self.prob_interaction
-            mean_belief = sum( i.mean_belief * self.degree(i) / influence for i in interactions )
+            )
-            mean_belief = mean_belief * self.information_spread_intensity + self.mean_belief * ( 1 - self.information_spread_intensity )
+            influence = sum(self.degree(i) for i in interactions)
-            self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
+            mean_belief = sum(
+                i.mean_belief * self.degree(i) / influence for i in interactions
+            )
+            mean_belief = (
+                mean_belief * self.information_spread_intensity
+                + self.mean_belief * (1 - self.information_spread_intensity)
+            )
+            self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * (
+                1 - self.vulnerability
+            )
 
         if self.mean_belief >= 0.8:
             return self.terrorist
 
     @state
     def leader(self):
-        self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
+        self.mean_belief = self.mean_belief ** (1 - self.terrorist_additional_influence)
-        for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
+        for neighbour in self.get_neighboring_agents(
+            state_id=[self.terrorist.id, self.leader.id]
+        ):
             if self.betweenness(neighbour) > self.betweenness(self):
                 return self.terrorist
 
     @state
     def terrorist(self):
-        neighbours = self.get_agents(state_id=[self.terrorist.id, self.leader.id],
+        neighbours = self.get_agents(
+            state_id=[self.terrorist.id, self.leader.id],
             agent_class=TerroristSpreadModel,
-                                     limit_neighbors=True)
+            limit_neighbors=True,
+        )
         if len(neighbours) > 0:
-            influence = sum( self.degree(n) for n in neighbours )
+            influence = sum(self.degree(n) for n in neighbours)
-            mean_belief = sum( n.mean_belief * self.degree(n) / influence for n in neighbours )
+            mean_belief = sum(
-            mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
+                n.mean_belief * self.degree(n) / influence for n in neighbours
-            self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
+            )
+            mean_belief = mean_belief * self.vulnerability + self.mean_belief * (
+                1 - self.vulnerability
+            )
+            self.mean_belief = self.mean_belief ** (
+                1 - self.terrorist_additional_influence
+            )
 
         # Check if there are any leaders in the group
         leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
@@ -82,21 +109,29 @@ class TerroristSpreadModel(FSM, Geo):
             return self.leader
 
     def ego_search(self, steps=1, center=False, node=None, **kwargs):
-        '''Get a list of nodes in the ego network of *node* of radius *steps*'''
+        """Get a list of nodes in the ego network of *node* of radius *steps*"""
         node = as_node(node if node is not None else self)
         G = self.subgraph(**kwargs)
         return nx.ego_graph(G, node, center=center, radius=steps).nodes()
 
     def degree(self, node, force=False):
         node = as_node(node)
-        if force or (not hasattr(self.model, '_degree')) or getattr(self.model, '_last_step', 0) < self.now:
+        if (
+            force
+            or (not hasattr(self.model, "_degree"))
+            or getattr(self.model, "_last_step", 0) < self.now
+        ):
             self.model._degree = nx.degree_centrality(self.G)
             self.model._last_step = self.now
         return self.model._degree[node]
 
     def betweenness(self, node, force=False):
         node = as_node(node)
-        if force or (not hasattr(self.model, '_betweenness')) or getattr(self.model, '_last_step', 0) < self.now:
+        if (
+            force
+            or (not hasattr(self.model, "_betweenness"))
+            or getattr(self.model, "_last_step", 0) < self.now
+        ):
             self.model._betweenness = nx.betweenness_centrality(self.G)
             self.model._last_step = self.now
         return self.model._betweenness[node]
@@ -114,17 +149,20 @@ class TrainingAreaModel(FSM, Geo):
 
     def __init__(self, model=None, unique_id=0, state=()):
         super().__init__(model=model, unique_id=unique_id, state=state)
-        self.training_influence = model.environment_params['training_influence']
+        self.training_influence = model.environment_params["training_influence"]
-        if 'min_vulnerability' in model.environment_params:
+        if "min_vulnerability" in model.environment_params:
-            self.min_vulnerability = model.environment_params['min_vulnerability']
+            self.min_vulnerability = model.environment_params["min_vulnerability"]
-        else: self.min_vulnerability = 0
+        else:
+            self.min_vulnerability = 0
 
     @default_state
     @state
     def terrorist(self):
         for neighbour in self.get_neighboring_agents(agent_class=TerroristSpreadModel):
             if neighbour.vulnerability > self.min_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )        
+                neighbour.vulnerability = neighbour.vulnerability ** (
+                    1 - self.training_influence
+                )
 
 
 class HavenModel(FSM, Geo):
@@ -141,11 +179,12 @@ class HavenModel(FSM, Geo):
 
     def __init__(self, model=None, unique_id=0, state=()):
         super().__init__(model=model, unique_id=unique_id, state=state)
-        self.haven_influence = model.environment_params['haven_influence']
+        self.haven_influence = model.environment_params["haven_influence"]
-        if 'min_vulnerability' in model.environment_params:
+        if "min_vulnerability" in model.environment_params:
-            self.min_vulnerability = model.environment_params['min_vulnerability']
+            self.min_vulnerability = model.environment_params["min_vulnerability"]
-        else: self.min_vulnerability = 0
+        else:
-        self.max_vulnerability = model.environment_params['max_vulnerability']
+            self.min_vulnerability = 0
+        self.max_vulnerability = model.environment_params["max_vulnerability"]
 
     def get_occupants(self, **kwargs):
         return self.get_neighboring_agents(agent_class=TerroristSpreadModel, **kwargs)
@@ -158,14 +197,18 @@ class HavenModel(FSM, Geo):
 
         for neighbour in self.get_occupants():
             if neighbour.vulnerability > self.min_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
+                neighbour.vulnerability = neighbour.vulnerability * (
+                    1 - self.haven_influence
+                )
         return self.civilian
 
     @state
     def terrorist(self):
         for neighbour in self.get_occupants():
             if neighbour.vulnerability < self.max_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
+                neighbour.vulnerability = neighbour.vulnerability ** (
+                    1 - self.haven_influence
+                )
         return self.terrorist
 
 
@@ -184,10 +227,10 @@ class TerroristNetworkModel(TerroristSpreadModel):
     def __init__(self, model=None, unique_id=0, state=()):
         super().__init__(model=model, unique_id=unique_id, state=state)
 
-        self.vision_range = model.environment_params['vision_range']
+        self.vision_range = model.environment_params["vision_range"]
-        self.sphere_influence = model.environment_params['sphere_influence']
+        self.sphere_influence = model.environment_params["sphere_influence"]
-        self.weight_social_distance = model.environment_params['weight_social_distance']
+        self.weight_social_distance = model.environment_params["weight_social_distance"]
-        self.weight_link_distance = model.environment_params['weight_link_distance']
+        self.weight_link_distance = model.environment_params["weight_link_distance"]
 
     @state
     def terrorist(self):
@@ -201,27 +244,48 @@ class TerroristNetworkModel(TerroristSpreadModel):
 
     def update_relationships(self):
         if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
-            close_ups = set(self.geo_search(radius=self.vision_range, agent_class=TerroristNetworkModel))
+            close_ups = set(
-            step_neighbours = set(self.ego_search(self.sphere_influence, agent_class=TerroristNetworkModel, center=False))
+                self.geo_search(
-            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_class=TerroristNetworkModel))
+                    radius=self.vision_range, agent_class=TerroristNetworkModel
+                )
+            )
+            step_neighbours = set(
+                self.ego_search(
+                    self.sphere_influence,
+                    agent_class=TerroristNetworkModel,
+                    center=False,
+                )
+            )
+            neighbours = set(
+                agent.id
+                for agent in self.get_neighboring_agents(
+                    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) )
+                spatial_proximity = 1 - self.get_distance(agent.id)
-                prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
+                prob_new_interaction = (
-                if agent['id'] == agent.civilian.id and self.random.random() < prob_new_interaction:
+                    self.weight_social_distance * social_distance
+                    + self.weight_link_distance * spatial_proximity
+                )
+                if (
+                    agent["id"] == agent.civilian.id
+                    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.id]
-        target_x, target_y = nx.get_node_attributes(self.G, 'pos')[target]
+        target_x, target_y = nx.get_node_attributes(self.G, "pos")[target]
-        dx = abs( source_x - target_x )
+        dx = abs(source_x - target_x)
-        dy = abs( source_y - target_y )
+        dy = abs(source_y - target_y)
-        return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
+        return (dx**2 + dy**2) ** (1 / 2)
 
     def shortest_path_length(self, target):
         try:
             return nx.shortest_path_length(self.G, self.id, target)
         except nx.NetworkXNoPath:
-            return float('inf')
+            return float("inf")

soil/__init__.py

@@ -225,6 +225,7 @@ def easy(cfg, pdb=False, debug=False, **kwargs):
     except Exception as e:
         if os.environ.get("SOIL_POSTMORTEM"):
             from .debugging import post_mortem
+
             print(traceback.format_exc())
             post_mortem()
         ex = e
@@ -232,5 +233,6 @@ def easy(cfg, pdb=False, debug=False, **kwargs):
         if ex:
             raise ex
 
+
 if __name__ == "__main__":
     main(do_run=True)

soil/__main__.py

@@ -1,7 +1,9 @@
 from . import main as init_main
 
+
 def main():
     init_main(do_run=True)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     init_main(do_run=True)

soil/agents/__init__.py

@@ -43,9 +43,9 @@ class MetaAgent(ABCMeta):
         }
 
         for attr, func in namespace.items():
-            if attr == 'step' and inspect.isgeneratorfunction(func):
+            if attr == "step" and inspect.isgeneratorfunction(func):
                 orig_func = func
-                new_nmspc['_MetaAgent__coroutine'] = None
+                new_nmspc["_MetaAgent__coroutine"] = None
 
                 @wraps(func)
                 def func(self):
@@ -303,7 +303,7 @@ class NetworkAgent(BaseAgent):
         return G
 
     def remove_node(self):
-        print(f'Removing node for {self.unique_id}: {self.node_id}')
+        print(f"Removing node for {self.unique_id}: {self.node_id}")
         self.G.remove_node(self.node_id)
         self.node_id = None
 

soil/environment.py

@@ -146,7 +146,7 @@ class BaseEnvironment(Model):
         if unique_id is None:
             unique_id = self.next_id()
 
-        kwargs['unique_id'] = unique_id
+        kwargs["unique_id"] = unique_id
         a = self._agent_from_dict(kwargs)
 
         self.schedule.add(a)
@@ -169,7 +169,9 @@ class BaseEnvironment(Model):
         Advance one step in the simulation, and update the data collection and scheduler appropriately
         """
         super().step()
-        self.logger.info(f"--- Step: {self.schedule.steps:^5} - Time: {self.now:^5} ---")
+        self.logger.info(
+            f"--- Step: {self.schedule.steps:^5} - Time: {self.now:^5} ---"
+        )
         self.schedule.step()
         self.datacollector.collect(self)
 
@@ -236,7 +238,7 @@ class NetworkEnvironment(BaseEnvironment):
         node_id = agent.get("node_id", None)
         if node_id is None:
             node_id = network.find_unassigned(self.G, random=self.random)
-        self.G.nodes[node_id]['agent'] = None
+        self.G.nodes[node_id]["agent"] = None
         agent["node_id"] = node_id
         agent["unique_id"] = unique_id
         agent["topology"] = self.G
@@ -271,7 +273,7 @@ class NetworkEnvironment(BaseEnvironment):
                 G=self.G, shuffle=True, random=self.random
             )
             if node_id is None:
-                node_id =  f'node_for_{unique_id}'
+                node_id = f"node_for_{unique_id}"
 
         if node_id not in self.G.nodes:
             self.G.add_node(node_id)
@@ -280,17 +282,23 @@ class NetworkEnvironment(BaseEnvironment):
         self.G.nodes[node_id]["agent"] = None  # Reserve
 
         a = self.add_agent(
-            unique_id=unique_id, agent_class=agent_class, topology=self.G, node_id=node_id, **kwargs
+            unique_id=unique_id,
+            agent_class=agent_class,
+            topology=self.G,
+            node_id=node_id,
+            **kwargs,
         )
         a["visible"] = True
         return a
 
     def add_agent(self, *args, **kwargs):
         a = super().add_agent(*args, **kwargs)
-        if 'node_id' in a:
+        if "node_id" in a:
             if a.node_id == 24:
-                import pdb;pdb.set_trace()
+                import pdb
-            assert self.G.nodes[a.node_id]['agent'] == a
+
+                pdb.set_trace()
+            assert self.G.nodes[a.node_id]["agent"] == a
         return a
 
     def agent_for_node_id(self, node_id):

soil/exporters.py

@@ -202,7 +202,12 @@ class summary(Exporter):
         for (t, df) in self.get_dfs(env):
             if not len(df):
                 continue
-            msg = indent(str(df.describe()), '    ')
+            msg = indent(str(df.describe()), "    ")
-            logger.info(dedent(f'''
+            logger.info(
+                dedent(
+                    f"""
             Dataframe {t}:
-            ''') + msg)
+            """
+                )
+                + msg
+            )

soil/simulation.py

@@ -226,7 +226,9 @@ Model stats:
             )
             model.step()
 
-        if model.schedule.time < until:  # Simulation ended (no more steps) before the expected time
+        if (
+            model.schedule.time < until
+        ):  # Simulation ended (no more steps) before the expected time
             model.schedule.time = until
         return model
 

soil/time.py

@@ -174,7 +174,6 @@ class TimedActivation(BaseScheduler):
                 agent.alive = False
                 continue
 
-
             if not getattr(agent, "alive", True):
                 self.remove(agent)
                 continue