WIP: all tests pass

Documentation needs some improvement The API has been simplified to only allow for ONE topology per NetworkEnvironment. This covers the main use case, and simplifies the code.
2025-08-23 19:52:19 +00:00 · 2022-10-16 17:54:03 +02:00
parent cd62c23cb9
commit d9947c2c52
34 changed files with 693 additions and 736 deletions
--- a/examples/complete.yml
+++ b/examples/complete.yml
@@ -10,19 +10,14 @@ seed: "CompleteSeed!"
 model_class: Environment
 model_params:
  am_i_complete: true
-  topologies:
-    default:
-      params:
-        generator: complete_graph
-        n: 10
-    another_graph:
-      params:
-        generator: complete_graph
-        n: 2
+  topology:
+    params:
+      generator: complete_graph
+      n: 12
  environment:
  agents:
    agent_class: CounterModel
-    topology: default
+    topology: true
    state:
      times: 1
      # In this group we are not specifying any topology
@@ -30,25 +25,23 @@ model_params:
      - name: 'Environment Agent 1'
        agent_class: BaseAgent
        group: environment
-        topology: null
+        topology: false
        hidden: true
        state:
          times: 10
      - agent_class: CounterModel
        id: 0
-        group: other_counters
-        topology: another_graph
+        group: fixed_counters
        state:
          times: 1
          total: 0
      - agent_class: CounterModel
-        topology: another_graph
-        group: other_counters
+        group: fixed_counters
        id: 1
    distribution:
      - agent_class: CounterModel
        weight: 1
-        group: general_counters
+        group: distro_counters
        state:
          times: 3
      - agent_class: AggregatedCounter
--- a/examples/complete_opt2.yml
+++ b/examples/complete_opt2.yml
@@ -1,63 +0,0 @@
---
-version: '2'
-id: simple
-group: tests
-dir_path: "/tmp/"
-num_trials: 3
-max_steps: 100
-interval: 1
-seed: "CompleteSeed!"
-model_class: "soil.Environment"
-model_params:
-  topologies:
-    default:
-      params:
-        generator: complete_graph
-        n: 10
-    another_graph:
-      params:
-        generator: complete_graph
-        n: 2
-  agents:
-    # The values here will be used as default values for any agent
-    agent_class: CounterModel
-    topology: default
-    state:
-      times: 1
-    # This specifies a distribution of agents, each with a `weight` or an explicit number of agents
-    distribution:
-      - agent_class: CounterModel
-        weight: 1
-        # This is inherited from the default settings
-        #topology: default
-        state:
-          times: 3
-      - agent_class: AggregatedCounter
-        topology: default
-        weight: 0.2
-    fixed:
-      - name: 'Environment Agent 1'
-        # All the other agents will assigned to the 'default' group
-        group: environment
-        # Do not count this agent towards total limits
-        hidden: true
-        agent_class: soil.BaseAgent
-        topology: null
-        state:
-          times: 10
-      - agent_class: CounterModel
-        topology: another_graph
-        id: 0
-        state:
-          times: 1
-          total: 0
-      - agent_class: CounterModel
-        topology: another_graph
-        id: 1
-    override:
-      # 2 agents that match this filter will be updated to match the state {times: 5}
-      - filter:
-          agent_class: AggregatedCounter
-        n: 2
-        state:
-            times: 5
--- a/examples/custom_timeouts/custom_timeouts.py
+++ b/examples/custom_timeouts/custom_timeouts.py
@@ -15,6 +15,7 @@ class Fibonacci(FSM):
        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'''
    @default_state
@@ -23,9 +24,8 @@ class Odds(FSM):
        self.log('Stopping at {}'.format(self.now))
        return None, self.env.timeout(1+self.now%2)

+
 if __name__ == '__main__':
-    import logging
-    logging.basicConfig(level=logging.INFO)
    from soil import Simulation
    s = Simulation(network_agents=[{'ids': [0], 'agent_class': Fibonacci},
                                   {'ids': [1], 'agent_class': Odds}],
--- a/examples/mesa/mesa.yml
+++ b/examples/mesa/mesa.yml
@@ -8,17 +8,12 @@ interval: 1
 seed: '1'
 model_class: social_wealth.MoneyEnv
 model_params:
-  topologies:
-    default: 
-      params:
-        generator: social_wealth.graph_generator
-        n: 5
+  generator: social_wealth.graph_generator
  agents:
+    topology: true
    distribution:
      - agent_class: social_wealth.SocialMoneyAgent
-        topology: default
        weight: 1
-  mesa_agent_class: social_wealth.MoneyAgent
  N: 10
  width: 50
  height: 50
--- a/examples/mesa/server.py
+++ b/examples/mesa/server.py
@@ -2,6 +2,7 @@ from mesa.visualization.ModularVisualization import ModularServer
 from soil.visualization import UserSettableParameter
 from mesa.visualization.modules import ChartModule, NetworkModule, CanvasGrid
 from social_wealth import MoneyEnv, graph_generator, SocialMoneyAgent
+import networkx as nx


 class MyNetwork(NetworkModule):
@@ -13,15 +14,16 @@ 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)}
    portrayal["nodes"] = [
        {
-            "id": agent_id,
-            "size": env.get_agent(agent_id).wealth,
-            # "color": "#CC0000" if not agents or agents[0].wealth == 0 else "#007959",
-            "color": "#CC0000",
-            "label": f"{agent_id}: {env.get_agent(agent_id).wealth}",
-        }
-        for (agent_id) in env.G.nodes
+            "id": node_id,
+            "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()
+
    ]

    portrayal["edges"] = [
@@ -29,7 +31,6 @@ def network_portrayal(env):
        for edge_id, (source, target) in enumerate(env.G.edges)
    ]

-
    return portrayal


@@ -55,7 +56,7 @@ def gridPortrayal(agent):
    }


-grid = MyNetwork(network_portrayal, 500, 500, library="sigma")
+grid = MyNetwork(network_portrayal, 500, 500)
 chart = ChartModule(
    [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
 )
@@ -70,7 +71,6 @@ model_params = {
        1,
        description="Choose how many agents to include in the model",
    ),
-    "network_agents": [{"agent_class": SocialMoneyAgent}],
    "height": UserSettableParameter(
        "slider",
        "height",
@@ -89,12 +89,15 @@ model_params = {
        1,
        description="Grid width",
        ),
-    "network_params": {
-        'generator': graph_generator
-    },
+    "agent_class": UserSettableParameter('choice', 'Agent class', value='MoneyAgent',
+                                         choices=['MoneyAgent', 'SocialMoneyAgent']),
+    "generator": graph_generator,
 }

-canvas_element = CanvasGrid(gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500)
+
+canvas_element = CanvasGrid(gridPortrayal,
+                            model_params["width"].value,
+                            model_params["height"].value, 500, 500)


 server = ModularServer(
--- a/examples/mesa/social_wealth.py
+++ b/examples/mesa/social_wealth.py
@@ -10,7 +10,7 @@ from mesa.batchrunner import BatchRunner

 import networkx as nx

-from soil import NetworkAgent, Environment
+from soil import NetworkAgent, Environment, serialization

 def compute_gini(model):
    agent_wealths = [agent.wealth for agent in model.agents]
@@ -19,15 +19,16 @@ def compute_gini(model):
    B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
    return (1 + (1/N) - 2*B)

+
 class MoneyAgent(MesaAgent):
    """
    A MESA agent with fixed initial wealth.
    It will only share wealth with neighbors based on grid proximity
    """

-    def __init__(self, unique_id, model):
+    def __init__(self, unique_id, model, wealth=1):
        super().__init__(unique_id=unique_id, model=model)
-        self.wealth = 1
+        self.wealth = wealth

    def move(self):
        possible_steps = self.model.grid.get_neighborhood(
@@ -45,7 +46,7 @@ class MoneyAgent(MesaAgent):
            self.wealth -= 1

    def step(self):
-        self.info("Crying wolf", self.pos)
+        print("Crying wolf", self.pos)
        self.move()
        if self.wealth > 0:
            self.give_money()
@@ -58,8 +59,8 @@ class SocialMoneyAgent(NetworkAgent, MoneyAgent):
        cellmates = set(self.model.grid.get_cell_list_contents([self.pos]))
        friends = set(self.get_neighboring_agents())
        self.info("Trying to give money")
-        self.debug("Cellmates: ", cellmates)
-        self.debug("Friends: ", friends)
+        self.info("Cellmates: ", cellmates)
+        self.info("Friends: ", friends)

        nearby_friends = list(cellmates & friends)

@@ -68,14 +69,29 @@ class SocialMoneyAgent(NetworkAgent, MoneyAgent):
            other.wealth += 1
            self.wealth -= 1

+def graph_generator(n=5):
+    G = nx.Graph()
+    for ix in range(n):
+        G.add_edge(0, ix)
+    return G
+

 class MoneyEnv(Environment):
    """A model with some number of agents."""
-    def __init__(self, width, height, *args, topologies, **kwargs):
+    def __init__(self, width, height, N, generator=graph_generator,
+                 agent_class=SocialMoneyAgent,
+                 topology=None, **kwargs):

-        super().__init__(*args, topologies=topologies, **kwargs)
+        generator = serialization.deserialize(generator)
+        agent_class = serialization.deserialize(agent_class, globs=globals())
+        topology = generator(n=N)
+        super().__init__(topology=topology,
+                         N=N,
+                         **kwargs)
        self.grid = MultiGrid(width, height, False)

+        self.populate_network(agent_class=agent_class)
+
        # Create agents
        for agent in self.agents:
            x = self.random.randrange(self.grid.width)
@@ -87,17 +103,9 @@ class MoneyEnv(Environment):
            agent_reporters={"Wealth": "wealth"})


-def graph_generator(n=5):
-    G = nx.Graph()
-    for ix in range(n):
-        G.add_edge(0, ix)
-    return G
-
 if __name__ == '__main__':

-
-    G = graph_generator()
-    fixed_params = {"topology": G,
+    fixed_params = {"generator": nx.complete_graph,
                    "width": 10,
                    "network_agents": [{"agent_class": SocialMoneyAgent,
                                       'weight': 1}],
@@ -116,4 +124,3 @@ if __name__ == '__main__':
    run_data = batch_run.get_model_vars_dataframe()
    run_data.head()
    print(run_data.Gini)
-
--- a/examples/pubcrawl/pubcrawl.py
+++ b/examples/pubcrawl/pubcrawl.py
@@ -126,7 +126,7 @@ class Patron(FSM, NetworkAgent):
        success depend on both agents' openness.
        '''
        if force or self['openness'] > self.random.random():
-            self.model.add_edge(self, other_agent)
+            self.add_edge(self, other_agent)
            self.info('Made some friend {}'.format(other_agent))
            return True
        return False
--- a/examples/rabbits/basic/rabbit_agents.py
+++ b/examples/rabbits/basic/rabbit_agents.py
@@ -57,7 +57,7 @@ class Male(RabbitModel):


 class Female(RabbitModel):
-    gestation = 100
+    gestation = 30

    @state
    def fertile(self):
@@ -72,10 +72,10 @@ class Female(RabbitModel):
        self.pregnancy = -1
        self.set_state(self.pregnant, when=self.now)
        self.number_of_babies = int(8+4*self.random.random())
-        self.debug('I am pregnant')

    @state
    def pregnant(self):
+        self.debug('I am pregnant')
        self.age += 1
        self.pregnancy += 1

@@ -88,7 +88,6 @@ class Female(RabbitModel):
                state = {}
                agent_class = self.random.choice([Male, Female])
                child = self.model.add_node(agent_class=agent_class,
-                                            topology=self.topology,
                                            **state)
                child.add_edge(self)
                try:
@@ -113,7 +112,7 @@ class RandomAccident(BaseAgent):
    level = logging.INFO

    def step(self):
-        rabbits_alive = self.model.topology.number_of_nodes()
+        rabbits_alive = self.model.G.number_of_nodes()

        if not rabbits_alive:
            return self.die()
@@ -121,10 +120,15 @@ class RandomAccident(BaseAgent):
        prob_death = self.model.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
        for i in self.iter_agents(agent_class=RabbitModel):
-            if i.state.id == i.dead.id:
+            if i.state_id == i.dead.id:
                continue
            if self.prob(prob_death):
                self.info('I killed a rabbit: {}'.format(i.id))
                rabbits_alive -= 1
                i.set_state(i.dead)
        self.debug('Rabbits alive: {}'.format(rabbits_alive))
+
+if __name__ == '__main__':
+    from soil import easy
+    sim = easy('rabbits.yml')
+    sim.run()
--- a/examples/rabbits/basic/rabbits.yml
+++ b/examples/rabbits/basic/rabbits.yml
@@ -10,18 +10,16 @@ max_time: 100
 model_class: soil.environment.Environment
 model_params:
  agents:
-    topology: default
+    topology: true
    agent_class: rabbit_agents.RabbitModel
    distribution:
    - agent_class: rabbit_agents.Male
-      topology: default
      weight: 1
    - agent_class: rabbit_agents.Female
-      topology: default
      weight: 1
    fixed:
    - agent_class: rabbit_agents.RandomAccident
-      topology: null
+      topology: false
      hidden: true
      state:
        group: environment
@@ -29,13 +27,12 @@ model_params:
      group: network
      mating_prob: 0.1
  prob_death: 0.001
-  topologies:
-    default:
-      topology:
-        directed: true
-        links: []
-        nodes:
-        - id: 1
-        - id: 0
+  topology:
+    fixed:
+      directed: true
+      links: []
+      nodes:
+      - id: 1
+      - id: 0
 extra:
  visualization_params: {}
--- a/examples/rabbits/improved/rabbits.yml
+++ b/examples/rabbits/improved/rabbits.yml
@@ -10,18 +10,16 @@ max_time: 100
 model_class: soil.environment.Environment
 model_params:
  agents:
-    topology: default
+    topology: true
    agent_class: rabbit_agents.RabbitModel
    distribution:
    - agent_class: rabbit_agents.Male
-      topology: default
      weight: 1
    - agent_class: rabbit_agents.Female
-      topology: default
      weight: 1
    fixed:
    - agent_class: rabbit_agents.RandomAccident
-      topology: null
+      topology: false
      hidden: true
      state:
        group: environment
@@ -29,13 +27,12 @@ model_params:
      group: network
      mating_prob: 0.1
  prob_death: 0.001
-  topologies:
-    default:
-      topology:
-        directed: true
-        links: []
-        nodes:
-        - id: 1
-        - id: 0
+  topology:
+    fixed:
+      directed: true
+      links: []
+      nodes:
+      - id: 1
+      - id: 0
 extra:
  visualization_params: {}
--- a/examples/random_delays/random_delays.py
+++ b/examples/random_delays/random_delays.py
@@ -4,7 +4,6 @@ Example of a fully programmatic simulation, without definition files.
 '''
 from soil import Simulation, agents
 from soil.time import Delta
-import logging



@@ -40,5 +39,4 @@ s = Simulation(name='Programmatic',
               dry_run=True)


-logging.basicConfig(level=logging.INFO)
 envs = s.run()