WIP: mesa compatibility

CHANGELOG.md

@@ -3,6 +3,27 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+### Added
+* [WIP] Integration with MESA
+* `not_agent_ids` paramter to get sql in history
+### Changed
+* `soil.Environment` now also inherits from `mesa.Model`
+* `soil.Agent` now also inherits from `mesa.Agent`
+* `soil.time` to replace `simpy` events, delays, duration, etc.
+### Removed
+* `simpy` dependency and compatibility. Each agent used to be a simpy generator, but that made debugging and error handling more complex. That has been replaced by a scheduler within the `soil.Environment` class, similar to how `mesa` does it.
+
+### TODO:
+* agent_id -> unique_id?
+* mesa has Agent.model and soil has Agent.env
+* Environments.agents and mesa.Agent.agents are not the same. env is a property, and it only takes into account network and environment agents. Might rename environment_agents to other_agents or sth like that
+* soil.History should mimic a mesa.datacollector :/
+* soil.Simulation *could* mimic a mesa.batchrunner
+* DONE include scheduler in environment
+* DONE environment inherits from `mesa.Model`
+
+
 ## [0.15.2]
 ### Fixed
 * Pass the right known_modules and parameters to stats discovery in simulation

docs/soil_tutorial.rst

@@ -47,12 +47,6 @@ There are three main elements in a soil simulation:
 -  The environment. It assigns agents to nodes in the network, and
    stores the environment parameters (shared state for all agents).
 
-Soil is based on ``simpy``, which is an event-based network simulation
-library. Soil provides several abstractions over events to make
-developing agents easier. This means you can use events (timeouts,
-delays) in soil, but for the most part we will assume your models will
-be step-based.
-
 Modeling behaviour
 ------------------
 

examples/mesa/mesa.yml

@@ -0,0 +1,21 @@
+---
+name: mesa_sim
+group: tests
+dir_path: "/tmp"
+num_trials: 3
+max_time: 100
+interval: 1
+seed: '1'
+network_params:
+  generator: social_wealth.graph_generator
+  n: 5
+network_agents:
+  - agent_type: social_wealth.SocialMoneyAgent
+    weight: 1
+environment_class: social_wealth.MoneyEnv
+environment_params:
+  num_mesa_agents: 5
+  mesa_agent_type: social_wealth.MoneyAgent
+  N: 10
+  width: 50
+  height: 50

examples/mesa/server.py

@@ -0,0 +1,106 @@
+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
+
+
+class MyNetwork(NetworkModule):
+    def render(self, model):
+        return self.portrayal_method(model)
+
+
+def network_portrayal(env):
+    # The model ensures there is 0 or 1 agent per node
+
+    portrayal = dict()
+    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
+    ]
+    # import pdb;pdb.set_trace()
+
+    portrayal["edges"] = [
+        {"id": edge_id, "source": source, "target": target, "color": "#000000"}
+        for edge_id, (source, target) in enumerate(env.G.edges)
+    ]
+
+
+    return portrayal
+
+
+def gridPortrayal(agent):
+    """
+    This function is registered with the visualization server to be called
+    each tick to indicate how to draw the agent in its current state.
+    :param agent:  the agent in the simulation
+    :return: the portrayal dictionary
+    """
+    color = max(10, min(agent.wealth*10, 100))
+    return {
+        "Shape": "rect",
+        "w": 1,
+        "h": 1,
+        "Filled": "true",
+        "Layer": 0,
+        "Label": agent.unique_id,
+        "Text": agent.unique_id,
+        "x": agent.pos[0],
+        "y": agent.pos[1],
+        "Color": f"rgba(31, 10, 255, 0.{color})"
+    }
+
+
+grid = MyNetwork(network_portrayal, 500, 500, library="sigma")
+chart = ChartModule(
+    [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
+)
+
+model_params = {
+    "N": UserSettableParameter(
+        "slider",
+        "N",
+        1,
+        1,
+        10,
+        1,
+        description="Choose how many agents to include in the model",
+    ),
+    "network_agents": [{"agent_type": SocialMoneyAgent}],
+    "height": UserSettableParameter(
+        "slider",
+        "height",
+        5,
+        5,
+        10,
+        1,
+        description="Grid height",
+        ),
+    "width": UserSettableParameter(
+        "slider",
+        "width",
+        5,
+        5,
+        10,
+        1,
+        description="Grid width",
+        ),
+    "network_params": {
+        'generator': graph_generator
+    },
+}
+
+canvas_element = CanvasGrid(gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500)
+
+
+server = ModularServer(
+    MoneyEnv, [grid, chart, canvas_element], "Money Model", model_params
+)
+server.port = 8521
+
+server.launch(open_browser=False)

examples/mesa/social_wealth.py

@@ -0,0 +1,134 @@
+'''
+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
+
+import networkx as nx
+
+from soil import NetworkAgent, Environment
+
+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))
+    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):
+        super().__init__(unique_id=unique_id, model=model)
+        self.wealth = 1
+
+    def move(self):
+        possible_steps = self.model.grid.get_neighborhood(
+            self.pos,
+            moore=True,
+            include_center=False)
+        print(self.pos, possible_steps)
+        new_position = self.random.choice(possible_steps)
+        print(self.pos, new_position)
+        self.model.grid.move_agent(self, new_position)
+
+    def give_money(self):
+        cellmates = self.model.grid.get_cell_list_contents([self.pos])
+        if len(cellmates) > 1:
+            other = self.random.choice(cellmates)
+            other.wealth += 1
+            self.wealth -= 1
+
+    def step(self):
+        self.info("Crying wolf", self.pos)
+        self.move()
+        if self.wealth > 0:
+            self.give_money()
+
+
+class SocialMoneyAgent(NetworkAgent, MoneyAgent):
+    wealth = 1
+
+    def give_money(self):
+        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)
+
+        nearby_friends = list(cellmates & friends)
+
+        if len(nearby_friends):
+            other = self.random.choice(nearby_friends)
+            other.wealth += 1
+            self.wealth -= 1
+
+
+class MoneyEnv(Environment):
+    """A model with some number of agents."""
+    def __init__(self, N, width, height, *args, network_params, **kwargs):
+        self.initialized = True
+        # import pdb;pdb.set_trace()
+
+        network_params['n'] = N
+        super().__init__(*args, network_params=network_params, **kwargs)
+        self.grid = MultiGrid(width, height, False)
+        # self.schedule = RandomActivation(self)
+        self.running = True
+
+        # Create agents
+        for agent in self.agents:
+            self.schedule.add(agent)
+            # a = MoneyAgent(i, self)
+            # self.schedule.add(a)
+            # Add the agent to a random grid cell
+            x = self.random.randrange(self.grid.width)
+            y = self.random.randrange(self.grid.height)
+            self.grid.place_agent(agent, (x, y))
+
+        self.datacollector = DataCollector(
+            model_reporters={"Gini": compute_gini},
+            agent_reporters={"Wealth": "wealth"})
+
+    def step(self):
+        super().step()
+        self.datacollector.collect(self)
+        self.schedule.step()
+
+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,
+                    "width": 10,
+                    "network_agents": [{"agent_type": SocialMoneyAgent,
+                                       'weight': 1}],
+                    "height": 10}
+
+    variable_params = {"N": range(10, 100, 10)}
+
+    batch_run = BatchRunner(MoneyEnv,
+                            variable_parameters=variable_params,
+                            fixed_parameters=fixed_params,
+                            iterations=5,
+                            max_steps=100,
+                            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/mesa/wealth.py

@@ -0,0 +1,83 @@
+from mesa import Agent, Model
+from mesa.space import MultiGrid
+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))
+    return (1 + (1/N) - 2*B)
+
+class MoneyAgent(Agent):
+    """ 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,
+            moore=True,
+            include_center=False)
+        new_position = self.random.choice(possible_steps)
+        self.model.grid.move_agent(self, new_position)
+
+    def give_money(self):
+        cellmates = self.model.grid.get_cell_list_contents([self.pos])
+        if len(cellmates) > 1:
+            other = self.random.choice(cellmates)
+            other.wealth += 1
+            self.wealth -= 1
+
+    def step(self):
+        self.move()
+        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)
+        self.schedule = RandomActivation(self)
+        self.running = True
+
+        # Create agents
+        for i in range(self.num_agents):
+            a = MoneyAgent(i, self)
+            self.schedule.add(a)
+            # Add the agent to a random grid cell
+            x = self.random.randrange(self.grid.width)
+            y = self.random.randrange(self.grid.height)
+            self.grid.place_agent(a, (x, y))
+
+        self.datacollector = DataCollector(
+            model_reporters={"Gini": compute_gini},
+            agent_reporters={"Wealth": "wealth"})
+
+    def step(self):
+        self.datacollector.collect(self)
+        self.schedule.step()
+
+
+if __name__ == '__main__':
+
+    fixed_params = {"width": 10,
+                    "height": 10}
+    variable_params = {"N": range(10, 500, 10)}
+
+    batch_run = BatchRunner(MoneyModel,
+                            variable_params,
+                            fixed_params,
+                            iterations=5,
+                            max_steps=100,
+                            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/terrorism/TerroristNetworkModel.py

@@ -18,12 +18,12 @@ class TerroristSpreadModel(FSM, Geo):
         prob_interaction
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
+    def __init__(self, model=None, unique_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        super().__init__(model=model, unique_id=unique_id, state=state)
 
-        self.information_spread_intensity = environment.environment_params['information_spread_intensity']
+        self.information_spread_intensity = model.environment_params['information_spread_intensity']
-        self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
+        self.terrorist_additional_influence = model.environment_params['terrorist_additional_influence']
-        self.prob_interaction = environment.environment_params['prob_interaction']
+        self.prob_interaction = model.environment_params['prob_interaction']
 
         if self['id'] == self.civilian.id:       # Civilian
             self.mean_belief = random.uniform(0.00, 0.5)
@@ -34,10 +34,10 @@ class TerroristSpreadModel(FSM, Geo):
         else:
             raise Exception('Invalid state id: {}'.format(self['id']))
 
-        if 'min_vulnerability' in environment.environment_params:
+        if 'min_vulnerability' in model.environment_params:
-            self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
+            self.vulnerability = random.uniform( model.environment_params['min_vulnerability'], model.environment_params['max_vulnerability'] )
         else :
-            self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
+            self.vulnerability = random.uniform( 0, model.environment_params['max_vulnerability'] )
 
 
     @state
@@ -93,11 +93,11 @@ class TrainingAreaModel(FSM, Geo):
     Requires TerroristSpreadModel.
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
+    def __init__(self, model=None, unique_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        super().__init__(model=model, unique_id=unique_id, state=state)
-        self.training_influence = environment.environment_params['training_influence']
+        self.training_influence = model.environment_params['training_influence']
-        if 'min_vulnerability' in environment.environment_params:
+        if 'min_vulnerability' in model.environment_params:
-            self.min_vulnerability = environment.environment_params['min_vulnerability']
+            self.min_vulnerability = model.environment_params['min_vulnerability']
         else: self.min_vulnerability = 0
 
     @default_state
@@ -120,13 +120,13 @@ class HavenModel(FSM, Geo):
     Requires TerroristSpreadModel.
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
+    def __init__(self, model=None, unique_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        super().__init__(model=model, unique_id=unique_id, state=state)
-        self.haven_influence = environment.environment_params['haven_influence']
+        self.haven_influence = model.environment_params['haven_influence']
-        if 'min_vulnerability' in environment.environment_params:
+        if 'min_vulnerability' in model.environment_params:
-            self.min_vulnerability = environment.environment_params['min_vulnerability']
+            self.min_vulnerability = model.environment_params['min_vulnerability']
         else: self.min_vulnerability = 0
-        self.max_vulnerability = environment.environment_params['max_vulnerability']
+        self.max_vulnerability = model.environment_params['max_vulnerability']
 
     def get_occupants(self, **kwargs):
         return self.get_neighboring_agents(agent_type=TerroristSpreadModel, **kwargs)
@@ -162,13 +162,13 @@ class TerroristNetworkModel(TerroristSpreadModel):
         weight_link_distance
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
+    def __init__(self, model=None, unique_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        super().__init__(model=model, unique_id=unique_id, state=state)
 
-        self.vision_range = environment.environment_params['vision_range']
+        self.vision_range = model.environment_params['vision_range']
-        self.sphere_influence = environment.environment_params['sphere_influence']
+        self.sphere_influence = model.environment_params['sphere_influence']
-        self.weight_social_distance = environment.environment_params['weight_social_distance']
+        self.weight_social_distance = model.environment_params['weight_social_distance']
-        self.weight_link_distance = environment.environment_params['weight_link_distance']
+        self.weight_link_distance = model.environment_params['weight_link_distance']
 
     @state
     def terrorist(self):

requirements.txt

@@ -1,9 +1,8 @@
-simpy>=4.0
 networkx>=2.5
 numpy
 matplotlib
 pyyaml>=5.1
 pandas>=0.23
-scipy>=1.3
 SALib>=1.3
 Jinja2
+Mesa>=0.8

setup.py

@@ -16,6 +16,12 @@ def parse_requirements(filename):
 
 install_reqs = parse_requirements("requirements.txt")
 test_reqs = parse_requirements("test-requirements.txt")
+extras_require={
+    'mesa': ['mesa>=0.8.9'],
+    'geo': ['scipy>=1.3'],
+    'web': ['tornado']
+}
+extras_require['all'] = [dep for package in extras_require.values() for dep in package]
 
 
 setup(
@@ -40,10 +46,7 @@ setup(
         'Operating System :: POSIX',
         'Programming Language :: Python :: 3'],
     install_requires=install_reqs,
-    extras_require={
+    extras_require=extras_require,
-        'web': ['tornado']
-
-    },
     tests_require=test_reqs,
     setup_requires=['pytest-runner', ],
     include_package_data=True,

soil/__init__.py

@@ -11,6 +11,7 @@ try:
 except NameError:
     basestring = str
 
+from .agents import *
 from . import agents
 from .simulation import *
 from .environment import Environment
@@ -18,6 +19,7 @@ from .history import History
 from . import serialization
 from . import analysis
 from .utils import logger
+from .time import *
 
 def main():
     import argparse

soil/agents/BassModel.py

@@ -1,40 +1,31 @@
 import random
-from . import BaseAgent
+from . import FSM, state, default_state
 
 
-class BassModel(BaseAgent):
+class BassModel(FSM):
     """
     Settings:
         innovation_prob
         imitation_prob
     """
-
+    sentimentCorrelation = 0
-    def __init__(self, environment, agent_id, state, **kwargs):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        env_params = environment.environment_params
-        self.state['sentimentCorrelation'] = 0
 
     def step(self):
         self.behaviour()
 
-    def behaviour(self):
+    @default_state
-        # Outside effects
+    @state
-        if random.random() < self['innovation_prob']:
+    def innovation(self):
-            if self.state['id'] == 0:
+        if random.random() < self.innovation_prob:
-                self.state['id'] = 1
+            self.sentimentCorrelation = 1
-                self.state['sentimentCorrelation'] = 1
+            return self.aware
         else:
-                pass
+            aware_neighbors = self.get_neighboring_agents(state_id=self.aware.id)
-
-            return
-
-        # Imitation effects
-        if self.state['id'] == 0:
-            aware_neighbors = self.get_neighboring_agents(state_id=1)
             num_neighbors_aware = len(aware_neighbors)
             if random.random() < (self['imitation_prob']*num_neighbors_aware):
-                self.state['id'] = 1
+                self.sentimentCorrelation = 1
-                self.state['sentimentCorrelation'] = 1
+                return self.aware
 
-            else:
+    @state
-                pass
+    def aware(self):
+        self.die()

soil/agents/BigMarketModel.py

@@ -1,8 +1,8 @@
 import random
-from . import BaseAgent
+from . import FSM, state, default_state
 
 
-class BigMarketModel(BaseAgent):
+class BigMarketModel(FSM):
     """
     Settings:
         Names:
@@ -19,34 +19,25 @@ class BigMarketModel(BaseAgent):
             sentiment_about [Array]
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
+    def __init__(self, *args, **kwargs):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        super().__init__(*args, **kwargs)
-        self.enterprises = environment.environment_params['enterprises']
+        self.enterprises = self.env.environment_params['enterprises']
         self.type = ""
-        self.number_of_enterprises = len(environment.environment_params['enterprises'])
 
-        if self.id < self.number_of_enterprises:  # Enterprises
+        if self.id < len(self.enterprises):  # Enterprises
-            self.state['id'] = self.id
+            self.set_state(self.enterprise.id)
             self.type = "Enterprise"
             self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id]
         else:  # normal users
-            self.state['id'] = self.number_of_enterprises
             self.type = "User"
+            self.set_state(self.user.id)
             self.tweet_probability = environment.environment_params['tweet_probability_users']
             self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability']
             self.tweet_probability_about = environment.environment_params['tweet_probability_about']  # List
             self.sentiment_about = environment.environment_params['sentiment_about']  # List
 
-    def step(self):
+    @state
-
+    def enterprise(self):
-        if self.id < self.number_of_enterprises:  # Enterprise
-            self.enterpriseBehaviour()
-        else:  # Usuario
-            self.userBehaviour()
-            for i in range(self.number_of_enterprises):  # So that it never is set to 0 if there are not changes (logs)
-                self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
-
-    def enterpriseBehaviour(self):
 
         if random.random() < self.tweet_probability:  # Tweets
             aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbour users
@@ -64,12 +55,12 @@ class BigMarketModel(BaseAgent):
 
                 x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
 
-    def userBehaviour(self):
+    @state
-
+    def user(self):
         if random.random() < self.tweet_probability:  # Tweets
             if random.random() < self.tweet_relevant_probability:  # Tweets something relevant
                 # Tweet probability per enterprise
-                for i in range(self.number_of_enterprises):
+                for i in range(len(self.enterprises)):
                     random_num = random.random()
                     if random_num < self.tweet_probability_about[i]:
                         # The condition is fulfilled, sentiments are evaluated towards that enterprise
@@ -82,6 +73,8 @@ class BigMarketModel(BaseAgent):
                         else:
                             # POSITIVO
                             self.userTweets("positive",i)
+        for i in range(len(self.enterprises)):  # So that it never is set to 0 if there are not changes (logs)
+            self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
 
     def userTweets(self, sentiment,enterprise):
         aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbours users

soil/agents/Geo.py

@@ -0,0 +1,20 @@
+from scipy.spatial import cKDTree as KDTree
+from . import NetworkAgent
+
+class Geo(NetworkAgent):
+    '''In this type of network, nodes have a "pos" attribute.'''
+
+    def geo_search(self, radius, node=None, center=False, **kwargs):
+        '''Get a list of nodes whose coordinates are closer than *radius* to *node*.'''
+        node = as_node(node if node is not None else self)
+
+        G = self.subgraph(**kwargs)
+
+        pos = nx.get_node_attributes(G, 'pos')
+        if not pos:
+            return []
+        nodes, coords = list(zip(*pos.items()))
+        kdtree = KDTree(coords)  # Cannot provide generator.
+        indices = kdtree.query_ball_point(pos[node], radius)
+        return [nodes[i] for i in indices if center or (nodes[i] != node)]
+

soil/agents/IndependentCascadeModel.py

@@ -10,10 +10,10 @@ class IndependentCascadeModel(BaseAgent):
         imitation_prob
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
+    def __init__(self, *args, **kwargs):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        super().__init__(*args, **kwargs)
-        self.innovation_prob = environment.environment_params['innovation_prob']
+        self.innovation_prob = self.env.environment_params['innovation_prob']
-        self.imitation_prob = environment.environment_params['imitation_prob']
+        self.imitation_prob = self.env.environment_params['imitation_prob']
         self.state['time_awareness'] = 0
         self.state['sentimentCorrelation'] = 0
 

soil/agents/ModelM2.py

@@ -21,8 +21,8 @@ class SpreadModelM2(BaseAgent):
         prob_generate_anti_rumor
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
+    def __init__(self, model=None, unique_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        super().__init__(model=environment, unique_id=unique_id, state=state)
 
         self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
                                                            environment.environment_params['standard_variance'])
@@ -123,8 +123,8 @@ class ControlModelM2(BaseAgent):
     """
 
 
-    def __init__(self, environment=None, agent_id=0, state=()):
+    def __init__(self, model=None, unique_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        super().__init__(model=environment, unique_id=unique_id, state=state)
 
         self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
                                                            environment.environment_params['standard_variance'])

soil/agents/SISaModel.py

@@ -29,8 +29,8 @@ class SISaModel(FSM):
         standard_variance
     """
 
-    def __init__(self, environment, agent_id=0, state=()):
+    def __init__(self, environment, unique_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        super().__init__(model=environment, unique_id=unique_id, state=state)
 
         self.neutral_discontent_spon_prob = np.random.normal(self.env['neutral_discontent_spon_prob'],
                                                              self.env['standard_variance'])

soil/agents/SentimentCorrelationModel.py

@@ -16,8 +16,8 @@ class SentimentCorrelationModel(BaseAgent):
         disgust_prob
     """
 
-    def __init__(self, environment, agent_id=0, state=()):
+    def __init__(self, environment, unique_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        super().__init__(model=environment, unique_id=unique_id, state=state)
         self.outside_effects_prob = environment.environment_params['outside_effects_prob']
         self.anger_prob = environment.environment_params['anger_prob']
         self.joy_prob = environment.environment_params['joy_prob']

soil/agents/__init__.py

@@ -1,21 +1,15 @@
-# networkStatus = {}  # Dict that will contain the status of every agent in the network
-# sentimentCorrelationNodeArray = []
-# for x in range(0, settings.network_params["number_of_nodes"]):
-#     sentimentCorrelationNodeArray.append({'id': x})
-# Initialize agent states. Let's assume everyone is normal.
-
-
 import logging
-from collections import OrderedDict
+from collections import OrderedDict, defaultdict
 from copy import deepcopy
 from functools import partial
-from scipy.spatial import cKDTree as KDTree
 import json
-import simpy
+import networkx as nx
 
 from functools import wraps
 
-from .. import serialization, history, utils
+from .. import serialization, history, utils, time
+
+from mesa import Agent
 
 
 def as_node(agent):
@@ -24,39 +18,51 @@ def as_node(agent):
     return agent
 
 
-class BaseAgent:
+class BaseAgent(Agent):
     """
-    A special simpy BaseAgent that keeps track of its state history.
+    A special Agent that keeps track of its state history.
     """
 
     defaults = {}
 
-    def __init__(self, environment, agent_id, state=None,
+    def __init__(self,
-                 name=None, interval=None):
+                 unique_id,
+                 model,
+                 state=None,
+                 name=None,
+                 interval=None):
         # Check for REQUIRED arguments
-        assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. '
-                                                  'Cannot be NoneType.')
         # Initialize agent parameters
-        self.id = agent_id
+        if isinstance(unique_id, Agent):
-        self.name = name or '{}[{}]'.format(type(self).__name__, self.id)
+            raise Exception()
-
+        super().__init__(unique_id=unique_id, model=model)
-        # Register agent to environment
+        self.name = name or '{}[{}]'.format(type(self).__name__, self.unique_id)
-        self.env = environment
 
         self._neighbors = None
         self.alive = True
         real_state = deepcopy(self.defaults)
         real_state.update(state or {})
         self.state = real_state
-        self.interval = interval
 
-        self.logger = logging.getLogger(self.env.name).getChild(self.name)
+        self.interval = interval or self.get('interval', getattr(self.model, 'interval', 1))
+        self.logger = logging.getLogger(self.model.name).getChild(self.name)
 
         if hasattr(self, 'level'):
             self.logger.setLevel(self.level)
 
-        # initialize every time an instance of the agent is created
+
-        self.action = self.env.process(self.run())
+    # TODO: refactor to clean up mesa compatibility
+    @property
+    def id(self):
+        return self.unique_id
+
+    @property
+    def env(self):
+        return self.model
+
+    @env.setter
+    def env(self, model):
+        self.model = model
 
     @property
     def state(self):
@@ -76,17 +82,17 @@ class BaseAgent:
 
     @property
     def environment_params(self):
-        return self.env.environment_params
+        return self.model.environment_params
 
     @environment_params.setter
     def environment_params(self, value):
-        self.env.environment_params = value
+        self.model.environment_params = value
 
     def __getitem__(self, key):
         if isinstance(key, tuple):
             key, t_step = key
             k = history.Key(key=key, t_step=t_step, agent_id=self.id)
-            return self.env[k]
+            return self.model[k]
         return self._state.get(key, None)
 
     def __delitem__(self, key):
@@ -100,7 +106,7 @@ class BaseAgent:
         k = history.Key(t_step=self.now,
                         agent_id=self.id,
                         key=key)
-        self.env[k] = value
+        self.model[k] = value
 
     def items(self):
         return self._state.items()
@@ -111,29 +117,33 @@ class BaseAgent:
     @property
     def now(self):
         try:
-            return self.env.now
+            return self.model.now
         except AttributeError:
             # No environment
             return None
 
-    def run(self):
-        if self.interval is not None:
-            interval = self.interval
-        elif 'interval' in self:
-            interval = self['interval']
-        else:
-            interval = self.env.interval
-        while self.alive:
-            res = self.step()
-            yield res or self.env.timeout(interval)
-
     def die(self, remove=False):
         self.alive = False
         if remove:
             self.remove_node(self.id)
 
     def step(self):
+        if not self.alive:
+            return time.When('inf')
+        return super().step() or time.Delta(self.interval)
+
+    def log(self, message, *args, level=logging.INFO, **kwargs):
+        if not self.logger.isEnabledFor(level):
             return
+        message = message + " ".join(str(i) for i in args)
+        message = " @{:>3}: {}".format(self.now, message)
+        for k, v in kwargs:
+            message += " {k}={v} ".format(k, v)
+        extra = {}
+        extra['now'] = self.now
+        extra['unique_id'] = self.unique_id
+        extra['agent_name'] = self.name
+        return self.logger.log(level, message, extra=extra)
 
     def debug(self, *args, **kwargs):
         return self.log(*args, level=logging.DEBUG, **kwargs)
@@ -149,7 +159,7 @@ class BaseAgent:
         '''
         state = {}
         state['id'] = self.id
-        state['environment'] = self.env
+        state['environment'] = self.model
         state['_state'] = self._state
         return state
 
@@ -157,19 +167,19 @@ class BaseAgent:
         '''
         Get back a serialized agent and try to re-compose it
         '''
-        self.id = state['id']
+        self.state_id = state['id']
         self._state = state['_state']
-        self.env = state['environment']
+        self.model = state['environment']
 
 class NetworkAgent(BaseAgent):
 
     @property
     def topology(self):
-        return self.env.G
+        return self.model.G
 
     @property
     def G(self):
-        return self.env.G
+        return self.model.G
 
     def count_agents(self, **kwargs):
         return len(list(self.get_agents(**kwargs)))
@@ -182,37 +192,26 @@ class NetworkAgent(BaseAgent):
 
     def get_agents(self, agents=None, limit_neighbors=False, **kwargs):
         if limit_neighbors:
-            agents = self.topology.neighbors(self.id)
+            agents = self.topology.neighbors(self.unique_id)
 
-        agents = self.env.get_agents(agents)
+        agents = self.model.get_agents(agents)
         return select(agents, **kwargs)
 
-    def log(self, message, *args, level=logging.INFO, **kwargs):
-        message = message + " ".join(str(i) for i in args)
-        message = " @{:>3}: {}".format(self.now, message)
-        for k, v in kwargs:
-            message += " {k}={v} ".format(k, v)
-        extra = {}
-        extra['now'] = self.now
-        extra['agent_id'] = self.id
-        extra['agent_name'] = self.name
-        return self.logger.log(level, message, extra=extra)
-
     def subgraph(self, center=True, **kwargs):
         include = [self] if center else []
-        return self.topology.subgraph(n.id for n in self.get_agents(**kwargs)+include)
+        return self.topology.subgraph(n.unique_id for n in self.get_agents(**kwargs)+include)
 
-    def remove_node(self, agent_id):
+    def remove_node(self, unique_id):
-        self.topology.remove_node(agent_id)
+        self.topology.remove_node(unique_id)
 
     def add_edge(self, other, edge_attr_dict=None, *edge_attrs):
         # return super(NetworkAgent, self).add_edge(node1=self.id, node2=other, **kwargs)
-        if self.id not in self.topology.nodes(data=False):
+        if self.unique_id not in self.topology.nodes(data=False):
-            raise ValueError('{} not in list of existing agents in the network'.format(self.id))
+            raise ValueError('{} not in list of existing agents in the network'.format(self.unique_id))
         if other not in self.topology.nodes(data=False):
             raise ValueError('{} not in list of existing agents in the network'.format(other))
 
-        self.topology.add_edge(self.id, other, edge_attr_dict=edge_attr_dict, *edge_attrs)
+        self.topology.add_edge(self.unique_id, other, edge_attr_dict=edge_attr_dict, *edge_attrs)
 
 
     def ego_search(self, steps=1, center=False, node=None, **kwargs):
@@ -223,17 +222,17 @@ class NetworkAgent(BaseAgent):
 
     def degree(self, node, force=False):
         node = as_node(node)
-        if force or (not hasattr(self.env, '_degree')) or getattr(self.env, '_last_step', 0) < self.now:
+        if force or (not hasattr(self.model, '_degree')) or getattr(self.model, '_last_step', 0) < self.now:
-            self.env._degree = nx.degree_centrality(self.topology)
+            self.model._degree = nx.degree_centrality(self.topology)
-            self.env._last_step = self.now
+            self.model._last_step = self.now
-        return self.env._degree[node]
+        return self.model._degree[node]
 
     def betweenness(self, node, force=False):
         node = as_node(node)
-        if force or (not hasattr(self.env, '_betweenness')) or getattr(self.env, '_last_step', 0) < self.now:
+        if force or (not hasattr(self.model, '_betweenness')) or getattr(self.model, '_last_step', 0) < self.now:
-            self.env._betweenness = nx.betweenness_centrality(self.topology)
+            self.model._betweenness = nx.betweenness_centrality(self.topology)
-            self.env._last_step = self.now
+            self.model._last_step = self.now
-        return self.env._betweenness[node]
+        return self.model._betweenness[node]
 
 
 def state(name=None):
@@ -301,36 +300,29 @@ class FSM(NetworkAgent, metaclass=MetaFSM):
         super(FSM, self).__init__(*args, **kwargs)
         if 'id' not in self.state:
             if not self.default_state:
-                raise ValueError('No default state specified for {}'.format(self.id))
+                raise ValueError('No default state specified for {}'.format(self.unique_id))
             self['id'] = self.default_state.id
-        self._next_change = simpy.core.Infinity
+
-        self._next_state = self.state
+        self.set_state(self.state['id'])
 
     def step(self):
-        if self._next_change < self.now:
+        self.debug(f'Agent {self.unique_id} @ state {self["id"]}')
-            next_state = self._next_state
+        interval = super().step()
-            self._next_change = simpy.core.Infinity
+        if 'id' not in self:
-            self['id'] = next_state
+            if 'id' in self.state:
-        elif 'id' in self.state:
+                self.set_state(self['state_id'])
-            next_state = self['id']
             elif self.default_state:
-            next_state = self.default_state.id
+                self.set_state(self.default_state.id)
             else:
                 raise Exception('{} has no valid state id or default state'.format(self))
-        if next_state not in self.states:
+        return self.states[self['id']](self) or interval
-            raise Exception('{} is not a valid id for {}'.format(next_state, self))
-        return self.states[next_state](self)
-
-    def next_state(self, state):
-        self._next_change = self.now
-        self._next_state = state
 
     def set_state(self, state):
         if hasattr(state, 'id'):
             state = state.id
         if state not in self.states:
             raise ValueError('{} is not a valid state'.format(state))
-        self['id'] = state
+        self['state_id'] = state
         return state
 
 
@@ -349,9 +341,6 @@ def prob(prob=1):
     return r < prob
 
 
-STATIC_THRESHOLD = (-1, -1)
-
-
 def calculate_distribution(network_agents=None,
                            agent_type=None):
     '''
@@ -379,7 +368,7 @@ def calculate_distribution(network_agents=None,
     'agent_type_1'.
     '''
     if network_agents:
-        network_agents = deepcopy(network_agents)
+        network_agents = [deepcopy(agent) for agent in network_agents if not hasattr(agent, 'id')]
     elif agent_type:
         network_agents = [{'agent_type': agent_type}]
     else:
@@ -394,7 +383,6 @@ def calculate_distribution(network_agents=None,
     acc = 0
     for v in network_agents:
         if 'ids' in v:
-            v['threshold'] = STATIC_THRESHOLD
             continue
         upper = acc + (v['weight']/total)
         v['threshold'] = [acc, upper]
@@ -409,7 +397,7 @@ def serialize_type(agent_type, known_modules=[], **kwargs):
     return serialization.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class
 
 
-def serialize_distribution(network_agents, known_modules=[]):
+def serialize_definition(network_agents, known_modules=[]):
     '''
     When serializing an agent distribution, remove the thresholds, in order
     to avoid cluttering the YAML definition file.
@@ -431,7 +419,7 @@ def deserialize_type(agent_type, known_modules=[]):
     return agent_type
 
 
-def deserialize_distribution(ind, **kwargs):
+def deserialize_definition(ind, **kwargs):
     d = deepcopy(ind)
     for v in d:
         v['agent_type'] = deserialize_type(v['agent_type'], **kwargs)
@@ -452,44 +440,84 @@ def _validate_states(states, topology):
 def _convert_agent_types(ind, to_string=False, **kwargs):
     '''Convenience method to allow specifying agents by class or class name.'''
     if to_string:
-        return serialize_distribution(ind, **kwargs)
+        return serialize_definition(ind, **kwargs)
-    return deserialize_distribution(ind, **kwargs)
+    return deserialize_definition(ind, **kwargs)
 
 
-def _agent_from_distribution(distribution, value=-1, agent_id=None):
+def _agent_from_definition(definition, value=-1, unique_id=None):
     """Used in the initialization of agents given an agent distribution."""
     if value < 0:
         value = random.random()
-    for d in sorted(distribution, key=lambda x: x['threshold']):
+    for d in sorted(definition, key=lambda x: x.get('threshold')):
-        threshold = d['threshold']
+        threshold = d.get('threshold', (-1, -1))
         # Check if the definition matches by id (first) or by threshold
-        if not ((agent_id is not None and threshold == STATIC_THRESHOLD and agent_id in d['ids']) or \
+        if (unique_id is not None and unique_id in d.get('ids', [])) or \
-                (value >= threshold[0] and value < threshold[1])):
+           (value >= threshold[0] and value < threshold[1]):
-            continue
             state = {}
             if 'state' in d:
                 state = deepcopy(d['state'])
             return d['agent_type'], state
 
-    raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
+    raise Exception('Definition for value {} not found in: {}'.format(value, definition))
 
 
-class Geo(NetworkAgent):
+def _definition_to_dict(definition, size=None, default_state=None):
-    '''In this type of network, nodes have a "pos" attribute.'''
+    state = default_state or {}
+    agents = {}
+    remaining = {}
+    if size:
+        for ix in range(size):
+            remaining[ix] = copy(state)
+    else:
+        remaining = defaultdict(lambda x: copy(state))
 
-    def geo_search(self, radius, node=None, center=False, **kwargs):
+    distro = sorted([item for item in definition if 'weight' in item])
-        '''Get a list of nodes whose coordinates are closer than *radius* to *node*.'''
-        node = as_node(node if node is not None else self)
 
-        G = self.subgraph(**kwargs)
+    ix = 0
+    def init_agent(item, id=ix):
+        while id in agents:
+            id += 1
 
-        pos = nx.get_node_attributes(G, 'pos')
+        agent = remaining[id]
-        if not pos:
+        agent['state'].update(copy(item.get('state', {})))
-            return []
+        agents[id] = agent
-        nodes, coords = list(zip(*pos.items()))
+        del remaining[id]
-        kdtree = KDTree(coords)  # Cannot provide generator.
+        return agent
-        indices = kdtree.query_ball_point(pos[node], radius)
+
-        return [nodes[i] for i in indices if center or (nodes[i] != node)]
+    for item in definition:
+        if 'ids' in item:
+            ids = item['ids']
+            del item['ids']
+            for id in ids:
+                agent = init_agent(item, id)
+
+    for item in definition:
+        if 'number' in item:
+            times = item['number']
+            del item['number']
+            for times in range(times):
+                if size:
+                    ix = random.choice(remaining.keys())
+                    agent = init_agent(item, id)
+                else:
+                    agent = init_agent(item)
+    if not size:
+        return agents
+
+    if len(remaining) < 0:
+        raise Exception('Invalid definition. Too many agents to add')
+
+
+    total_weight = float(sum(s['weight'] for s in distro))
+    unit = size / total_weight
+
+    for item in distro:
+        times = unit * item['weight']
+        del item['weight']
+        for times in range(times):
+            ix = random.choice(remaining.keys())
+            agent = init_agent(item, id)
+    return agents
 
 
 def select(agents, state_id=None, agent_type=None, ignore=None, iterator=False, **kwargs):
@@ -502,22 +530,21 @@ def select(agents, state_id=None, agent_type=None, ignore=None, iterator=False,
         except TypeError:
             agent_type = tuple([agent_type])
 
-    def matches_all(agent):
+    checks = []
-        if state_id is not None:
+
-            if agent.state.get('id', None) not in state_id:
+    f = agents
-                return False
-        if agent_type is not None:
-            if not isinstance(agent, agent_type):
-                return False
-        state = agent.state
-        for k, v in kwargs.items():
-            if state.get(k, None) != v:
-                return False
-        return True
 
-    f = filter(matches_all, agents)
     if ignore:
         f = filter(lambda x: x not in ignore, f)
+
+    if state_id is not None:
+        f = filter(lambda agent: agent.state.get('id', None) in state_id, f)
+
+    if agent_type is not None:
+        f = filter(lambda agent: isinstance(agent, agent_type), f)
+    for k, v in kwargs.items():
+        f = filter(lambda agent: agent.state.get(k, None) == v, f)
+
     if iterator:
         return f
     return list(f)
@@ -530,3 +557,10 @@ from .ModelM2 import *
 from .SentimentCorrelationModel import *
 from .SISaModel import *
 from .CounterModel import *
+
+try:
+    import scipy
+    from .Geo import Geo
+except ImportError:
+    import sys
+    print('Could not load the Geo Agent, scipy is not installed', file=sys.stderr)

soil/analysis.py

@@ -61,7 +61,12 @@ def convert_row(row):
 
 
 def convert_types_slow(df):
-    '''This is a slow operation.'''
+    '''
+    Go over every column in a dataframe and convert it to the type determined by the `get_types`
+    function.
+
+    This is a slow operation.
+    '''
     dtypes = get_types(df)
     for k, v in dtypes.items():
         t = df[df['key']==k]
@@ -102,6 +107,9 @@ def process(df, **kwargs):
 
 
 def get_types(df):
+    '''
+    Get the value type for every key stored in a raw history dataframe.
+    '''
     dtypes = df.groupby(by=['key'])['value_type'].unique()
     return {k:v[0] for k,v in dtypes.iteritems()}
 
@@ -126,8 +134,14 @@ def process_one(df, *keys, columns=['key', 'agent_id'], values='value',
 
 
 def get_count(df, *keys):
+    '''
+    For every t_step and key, get the value count.
+
+    The result is a dataframe with `t_step` as index, an a multiindex column based on `key` and the values found for each `key`.
+    '''
     if keys:
         df = df[list(keys)]
+        df.columns = df.columns.remove_unused_levels()
     counts = pd.DataFrame()
     for key in df.columns.levels[0]:
         g = df[[key]].apply(pd.Series.value_counts, axis=1).fillna(0)
@@ -137,10 +151,25 @@ def get_count(df, *keys):
     return counts
 
 
+def get_majority(df, *keys):
+    '''
+    For every t_step and key, get the value of the majority of agents
+
+    The result is a dataframe with `t_step` as index, and columns based on `key`.
+    '''
+    df = get_count(df, *keys)
+    return df.stack(level=0).idxmax(axis=1).unstack()
+
+
 def get_value(df, *keys, aggfunc='sum'):
+    '''
+    For every t_step and key, get the value of *numeric columns*, aggregated using a specific function.
+    '''
     if keys:
         df = df[list(keys)]
-    return df.groupby(axis=1, level=0).agg(aggfunc)
+        df.columns = df.columns.remove_unused_levels()
+    df = df.select_dtypes('number')
+    return df.groupby(level='key', axis=1).agg(aggfunc)
 
 
 def plot_all(*args, plot_args={}, **kwargs):

soil/datacollection.py

@@ -0,0 +1,26 @@
+from mesa import DataCollector as MDC
+
+class SoilDataCollector(MDC):
+
+
+    def __init__(self, environment, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # Populate model and env reporters so they have a key per 
+        # So they can be shown in the web interface
+        self.environment = environment
+
+
+    @property
+    def model_vars(self):
+        pass
+
+    @model_vars.setter
+    def model_vars(self, value):
+        pass
+
+    @property
+    def agent_reporters(self):
+        self.model._history._
+
+        pass
+

soil/environment.py

@@ -1,19 +1,20 @@
 import os
 import sqlite3
-import time
 import csv
+import math
 import random
-import simpy
 import yaml
 import tempfile
 import pandas as pd
+from time import time as current_time
 from copy import deepcopy
 from networkx.readwrite import json_graph
 
 import networkx as nx
-import simpy
 
-from . import serialization, agents, analysis, history, utils
+from mesa import Model
+
+from . import serialization, agents, analysis, history, utils, time
 
 # These properties will be copied when pickling/unpickling the environment
 _CONFIG_PROPS = [ 'name',
@@ -22,7 +23,7 @@ _CONFIG_PROPS = [ 'name',
                   'interval',
                  ]
 
-class Environment(simpy.Environment):
+class Environment(Model):
     """
     The environment is key in a simulation. It contains the network topology,
     a reference to network and environment agents, as well as the environment
@@ -39,25 +40,41 @@ class Environment(simpy.Environment):
                  states=None,
                  default_state=None,
                  interval=1,
+                 network_params=None,
                  seed=None,
                  topology=None,
+                 schedule=None,
                  initial_time=0,
-                 **environment_params):
+                 environment_params=None,
+                 dir_path=None,
+                 **kwargs):
 
 
+        super().__init__()
+
+        self.schedule = schedule
+        if schedule is None:
+            self.schedule = time.TimedActivation()
+
         self.name = name or 'UnnamedEnvironment'
-        seed = seed or time.time()
+        seed = seed or current_time()
         random.seed(seed)
         if isinstance(states, list):
             states = dict(enumerate(states))
         self.states = deepcopy(states) if states else {}
         self.default_state = deepcopy(default_state) or {}
+
+        if topology is None:
+            network_params = network_params or {}
+            topology = serialization.load_network(network_params,
+                                                  dir_path=dir_path)
         if not topology:
             topology = nx.Graph()
         self.G = nx.Graph(topology) 
 
-        super().__init__(initial_time=initial_time)
+
-        self.environment_params = environment_params
+        self.environment_params = environment_params or {}
+        self.environment_params.update(kwargs)
 
         self._env_agents = {}
         self.interval = interval
@@ -66,8 +83,26 @@ class Environment(simpy.Environment):
         self['SEED'] = seed
         # Add environment agents first, so their events get
         # executed before network agents
-        self.environment_agents = environment_agents or []
+
-        self.network_agents = network_agents or []
+
+        if network_agents:
+            distro = agents.calculate_distribution(network_agents)
+            self.network_agents = agents._convert_agent_types(distro)
+        else:
+            self.network_agents = []
+
+        environment_agents = environment_agents or []
+        if environment_agents:
+            distro = agents.calculate_distribution(environment_agents)
+            environment_agents = agents._convert_agent_types(distro)
+        self.environment_agents = environment_agents
+
+
+    @property
+    def now(self):
+        if self.schedule:
+            return self.schedule.time
+        raise Exception('The environment has not been scheduled, so it has no sense of time')
 
     @property
     def agents(self):
@@ -81,15 +116,9 @@ class Environment(simpy.Environment):
 
     @environment_agents.setter
     def environment_agents(self, environment_agents):
-        # Set up environmental agent
+        self._environment_agents = environment_agents
-        self._env_agents = {}
+
-        for item in environment_agents:
+        self._env_agents = agents._definition_to_dict(definition=environment_agents)
-            kwargs = deepcopy(item)
-            atype = kwargs.pop('agent_type')
-            kwargs['agent_id'] = kwargs.get('agent_id', atype.__name__)
-            kwargs['state'] = kwargs.get('state', {})
-            a = atype(environment=self, **kwargs)
-            self._env_agents[a.id] = a
 
     @property
     def network_agents(self):
@@ -102,9 +131,9 @@ class Environment(simpy.Environment):
     def network_agents(self, network_agents):
         self._network_agents = network_agents
         for ix in self.G.nodes():
-            self.init_agent(ix, agent_distribution=network_agents)
+            self.init_agent(ix, agent_definitions=network_agents)
 
-    def init_agent(self, agent_id, agent_distribution):
+    def init_agent(self, agent_id, agent_definitions):
         node = self.G.nodes[agent_id]
         init = False
         state = dict(node)
@@ -119,8 +148,8 @@ class Environment(simpy.Environment):
 
         if agent_type:
             agent_type = agents.deserialize_type(agent_type)
-        elif agent_distribution:
+        elif agent_definitions:
-            agent_type, state = agents._agent_from_distribution(agent_distribution, agent_id=agent_id)
+            agent_type, state = agents._agent_from_definition(agent_definitions, unique_id=agent_id)
         else:
             serialization.logger.debug('Skipping node {}'.format(agent_id))
             return
@@ -136,8 +165,8 @@ class Environment(simpy.Environment):
         a = None
         if agent_type:
             state = defstate
-            a = agent_type(environment=self,
+            a = agent_type(model=self,
-                           agent_id=agent_id,
+                           unique_id=agent_id,
                            state=state)
         node['agent'] = a
         return a
@@ -159,30 +188,18 @@ class Environment(simpy.Environment):
 
     def run(self, until, *args, **kwargs):
         self._save_state()
-        super().run(until, *args, **kwargs)
+        for agent in self.agents:
+            self.schedule.add(agent)
+
+        while self.schedule.next_time <= until and not math.isinf(self.schedule.next_time):
+            self.schedule.step(until=until)
+            utils.logger.debug(f'Simulation step {self.schedule.time}/{until}. Next: {self.schedule.next_time}')
         self._history.flush_cache()
 
     def _save_state(self, now=None):
         serialization.logger.debug('Saving state @{}'.format(self.now))
         self._history.save_records(self.state_to_tuples(now=now))
 
-    def save_state(self):
-        '''
-        :DEPRECATED:
-        Periodically save the state of the environment and the agents.
-        '''
-        self._save_state()
-        while self.peek() != simpy.core.Infinity:
-            delay = max(self.peek() - self.now, self.interval)
-            serialization.logger.debug('Step: {}'.format(self.now))
-            ev = self.event()
-            ev._ok = True
-            # Schedule the event with minimum priority so
-            # that it executes before all agents
-            self.schedule(ev, -999, delay)
-            yield ev
-            self._save_state()
-
     def __getitem__(self, key):
         if isinstance(key, tuple):
             self._history.flush_cache()
@@ -329,7 +346,7 @@ class Environment(simpy.Environment):
         state['G'] = json_graph.node_link_data(self.G)
         state['environment_agents'] = self._env_agents
         state['history'] = self._history
-        state['_now'] = self._now
+        state['schedule'] = self.schedule
         return state
 
     def __setstate__(self, state):
@@ -338,7 +355,8 @@ class Environment(simpy.Environment):
         self._env_agents = state['environment_agents']
         self.G = json_graph.node_link_graph(state['G'])
         self._history = state['history']
-        self._now = state['_now']
+        # self._env = None
+        self.schedule = state['schedule']
         self._queue = []
 
 

soil/history.py

@@ -52,7 +52,7 @@ class History:
 
         with self.db:
             logger.debug('Creating database {}'.format(self.db_path))
-            self.db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step int, key text, value text)''')
+            self.db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step real, key text, value text)''')
             self.db.execute('''CREATE TABLE IF NOT EXISTS value_types (key text, value_type text)''')
             self.db.execute('''CREATE TABLE IF NOT EXISTS stats (trial_id text)''')
             self.db.execute('''CREATE UNIQUE INDEX IF NOT EXISTS idx_history ON history (agent_id, t_step, key);''')
@@ -103,7 +103,7 @@ class History:
                         dtype = 'real'
                         int(value)
                         dtype = 'int'
-                    except ValueError:
+                    except (ValueError, OverflowError):
                         pass
                 self.db.execute('ALTER TABLE stats ADD "{}" "{}"'.format(column, dtype))
                 self._stats_columns.append(column)
@@ -167,6 +167,7 @@ class History:
                 with self.db:
                     self.db.execute("replace into value_types (key, value_type) values (?, ?)", (key, name))
         value = self._dtypes[key][1](value)
+
         self._tups.append(Record(agent_id=agent_id,
                                  t_step=t_step,
                                  key=key,
@@ -183,9 +184,9 @@ class History:
             raise Exception('DB in readonly mode')
         logger.debug('Flushing cache {}'.format(self.db_path))
         with self.db:
-            for rec in self._tups:
+            self.db.executemany("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", self._tups)
-                self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
+            # (rec.agent_id, rec.t_step, rec.key, rec.value))
-        self._tups = list()
+        self._tups.clear()
 
     def to_tuples(self):
         self.flush_cache()
@@ -209,6 +210,7 @@ class History:
             self._dtypes[k] = (v, serializer, deserializer)
 
     def __getitem__(self, key):
+        # raise NotImplementedError()
         self.flush_cache()
         key = Key(*key)
         agent_ids = [key.agent_id] if key.agent_id is not None else []
@@ -223,7 +225,7 @@ class History:
             return r.value()
         return r
 
-    def read_sql(self, keys=None, agent_ids=None, t_steps=None, convert_types=False, limit=-1):
+    def read_sql(self, keys=None, agent_ids=None, not_agent_ids=None, t_steps=None, convert_types=False, limit=-1):
 
         self._read_types()
 
@@ -233,7 +235,8 @@ class History:
             return ",".join(map(lambda x: "\'{}\'".format(x), v))
 
         filters = [("key in ({})".format(escape_and_join(keys)), keys),
-                   ("agent_id in ({})".format(escape_and_join(agent_ids)), agent_ids)
+                   ("agent_id in ({})".format(escape_and_join(agent_ids)), agent_ids),
+                   ("agent_id not in ({})".format(escape_and_join(not_agent_ids)), not_agent_ids)
         ]
         filters = list(k[0] for k in filters if k[1])
 

soil/serialization.py

@@ -13,7 +13,6 @@ from jinja2 import Template
 
 
 logger = logging.getLogger('soil')
-logger.setLevel(logging.INFO)
 
 
 def load_network(network_params, dir_path=None):
@@ -51,6 +50,9 @@ def load_network(network_params, dir_path=None):
 
 
 def load_file(infile):
+    folder = os.path.dirname(infile)
+    if folder not in sys.path:
+        sys.path.append(folder)
     with open(infile, 'r') as f:
         return list(chain.from_iterable(map(expand_template, load_string(f))))
 

soil/simulation.py

@@ -143,7 +143,7 @@ class Simulation:
         return list(self.run_gen(*args, **kwargs))
 
     def _run_sync_or_async(self, parallel=False, *args, **kwargs):
-        if parallel:
+        if parallel and not os.environ.get('SENPY_DEBUG', None):
             p = Pool()
             func = partial(self.run_trial_exceptions,
                            *args,
@@ -226,12 +226,14 @@ class Simulation:
         opts.update({
             'name': trial_id,
             'topology': self.topology.copy(),
+            'network_params': self.network_params,
             'seed': '{}_trial_{}'.format(self.seed, trial_id),
             'initial_time': 0,
             'interval': self.interval,
             'network_agents': self.network_agents,
             'initial_time': 0,
             'states': self.states,
+            'dir_path': self.dir_path,
             'default_state': self.default_state,
             'environment_agents': self.environment_agents,
         })
@@ -304,9 +306,9 @@ class Simulation:
             if k[0] != '_':
                 state[k] = v
                 state['topology'] = json_graph.node_link_data(self.topology)
-                state['network_agents'] = agents.serialize_distribution(self.network_agents,
+                state['network_agents'] = agents.serialize_definition(self.network_agents,
                                                                       known_modules = [])
-                state['environment_agents'] = agents.serialize_distribution(self.environment_agents,
+                state['environment_agents'] = agents.serialize_definition(self.environment_agents,
                                                                           known_modules = [])
                 state['environment_class'] = serialization.serialize(self.environment_class,
                                                                      known_modules=['soil.environment'])[1]  # func, name
@@ -325,7 +327,6 @@ class Simulation:
                                                               known_modules=[self.load_module])
         self.environment_class = serialization.deserialize(self.environment_class,
                                                    known_modules=[self.load_module, 'soil.environment', ])  # func, name
-        return state
 
 
 def all_from_config(config):

soil/time.py

@@ -0,0 +1,84 @@
+from mesa.time import BaseScheduler
+from queue import Empty
+from heapq import heappush, heappop
+import math
+from .utils import logger
+from mesa import Agent
+
+
+class When:
+    def __init__(self, time):
+        self._time = float(time)
+
+    def abs(self, time):
+        return self._time
+
+
+class Delta:
+    def __init__(self, delta):
+        self._delta = delta
+
+    def abs(self, time):
+        return time + self._delta
+
+
+class TimedActivation(BaseScheduler):
+    """A scheduler which activates each agent when the agent requests.
+    In each activation, each agent will update its 'next_time'.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(self)
+        self._queue = []
+        self.next_time = 0
+
+    def add(self, agent: Agent):
+        if agent.unique_id not in self._agents:
+            heappush(self._queue, (self.time, agent.unique_id))
+        super().add(agent)
+
+    def step(self, until: float =float('inf')) -> None:
+        """
+        Executes agents in order, one at a time. After each step,
+        an agent will signal when it wants to be scheduled next.
+        """
+
+        when = None
+        agent_id = None
+        unsched = []
+        until = until or float('inf')
+
+        if not self._queue:
+            self.time = until
+            self.next_time = float('inf')
+            return
+
+        (when, agent_id) = self._queue[0]
+
+        if until and when > until:
+            self.time = until
+            self.next_time = when
+            return
+
+        self.time = when
+        next_time = float("inf")
+
+        while when == self.time:
+            heappop(self._queue)
+            logger.debug(f'Stepping agent {agent_id}')
+            when = (self._agents[agent_id].step() or Delta(1)).abs(self.time)
+            heappush(self._queue, (when, agent_id))
+            if when < next_time:
+                next_time = when
+
+            if not self._queue or self._queue[0][0] > self.time:
+                agent_id = None
+                break
+            else:
+                (when, agent_id) = self._queue[0]
+
+        if when and when < self.time:
+            raise Exception("Invalid scheduling time")
+
+        self.next_time = next_time
+        self.steps += 1

soil/utils.py

@@ -7,8 +7,8 @@ from shutil import copyfile
 from contextlib import contextmanager
 
 logger = logging.getLogger('soil')
-logging.basicConfig()
+# logging.basicConfig()
-logger.setLevel(logging.INFO)
+# logger.setLevel(logging.INFO)
 
 
 @contextmanager

soil/visualization.py

@@ -0,0 +1,5 @@
+from mesa.visualization.UserParam import UserSettableParameter
+
+class UserSettableParameter(UserSettableParameter):
+    def __str__(self):
+        return self.value

test-requirements.txt

@@ -1 +1,4 @@
 pytest
+mesa>=0.8.9
+scipy>=1.3
+tornado

tests/test_analysis.py

@@ -21,11 +21,13 @@ class Ping(agents.FSM):
     @agents.default_state
     @agents.state
     def even(self):
+        self.debug(f'Even {self["count"]}')
         self['count'] += 1
         return self.odd
 
     @agents.state
     def odd(self):
+        self.debug(f'Odd {self["count"]}')
         self['count'] += 1
         return self.even
 
@@ -82,8 +84,7 @@ class TestAnalysis(TestCase):
 
         import numpy as np
         res_mean = analysis.get_value(df, 'count', aggfunc=np.mean)
-        assert res_mean['count'].iloc[0] == 1
+        assert res_mean['count'].iloc[15] == (16+8)/2
-
-        res_total = analysis.get_value(df)
 
+        res_total = analysis.get_majority(df)
         res_total['SEED'].iloc[0] == self.env['SEED']

tests/test_history.py

@@ -1,203 +0,0 @@
-from unittest import TestCase
-
-import os
-import shutil
-from glob import glob
-
-from soil import history
-from soil import utils
-
-
-ROOT = os.path.abspath(os.path.dirname(__file__))
-DBROOT = os.path.join(ROOT, 'testdb')
-
-
-class TestHistory(TestCase):
-
-    def setUp(self):
-        if not os.path.exists(DBROOT):
-            os.makedirs(DBROOT)
-
-    def tearDown(self):
-        if os.path.exists(DBROOT):
-            shutil.rmtree(DBROOT)
-
-    def test_history(self):
-        """
-        """
-        tuples = (
-            ('a_0', 0, 'id', 'h'),
-            ('a_0', 1, 'id', 'e'),
-            ('a_0', 2, 'id', 'l'),
-            ('a_0', 3, 'id', 'l'),
-            ('a_0', 4, 'id', 'o'),
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 3, 'prob', 2),
-            ('env', 5, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        h = history.History()
-        h.save_tuples(tuples)
-        # assert h['env', 0, 'prob'] == 0
-        for i in range(1, 7):
-            assert h['env', i, 'prob'] == ((i-1)//2)+1
-
-
-        for i, k in zip(range(5), 'hello'):
-            assert h['a_0', i, 'id'] == k
-        for record, value in zip(h['a_0', None, 'id'], 'hello'):
-            t_step, val = record
-            assert val == value
-
-        for i, k in zip(range(5), 'value'):
-            assert h['a_1', i, 'id'] == k
-        for i in range(5, 8):
-            assert h['a_1', i, 'id'] == 'e'
-        for i in range(7):
-            assert h['a_2', i, 'finished'] == False
-        assert h['a_2', 7, 'finished']
-
-    def test_history_gen(self):
-        """
-        """
-        tuples = (
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 2, 'prob', 2),
-            ('env', 3, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        h = history.History()
-        h.save_tuples(tuples)
-        for t_step, key, value in h['env', None, None]:
-            assert t_step == value
-            assert key == 'prob'
-
-        records = list(h[None, 7, None])
-        assert len(records) == 3
-        for i in records:
-            agent_id, key, value = i
-            if agent_id == 'a_1':
-                assert key == 'id'
-                assert value == 'e'
-            elif agent_id == 'a_2':
-                assert key == 'finished'
-                assert value
-            else:
-                assert key == 'prob'
-                assert value == 3
-
-        records = h['a_1', 7, None]
-        assert records['id'] == 'e'
-
-    def test_history_file(self):
-        """
-        History should be saved to a file
-        """
-        tuples = (
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 2, 'prob', 2),
-            ('env', 3, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        db_path = os.path.join(DBROOT, 'test')
-        h = history.History(db_path=db_path)
-        h.save_tuples(tuples)
-        h.flush_cache()
-        assert os.path.exists(db_path)
-
-        # Recover the data
-        recovered = history.History(db_path=db_path)
-        assert recovered['a_1', 0, 'id'] == 'v'
-        assert recovered['a_1', 4, 'id'] == 'e'
-
-        # Using backup=True should create a backup copy, and initialize an empty history
-        newhistory = history.History(db_path=db_path, backup=True)
-        backuppaths = glob(db_path + '.backup*.sqlite')
-        assert len(backuppaths) == 1
-        backuppath = backuppaths[0]
-        assert newhistory.db_path == h.db_path
-        assert os.path.exists(backuppath)
-        assert len(newhistory[None, None, None]) == 0
-
-    def test_history_tuples(self):
-        """
-        The data recovered should be equal to the one recorded.
-        """
-        tuples = (
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 2, 'prob', 2),
-            ('env', 3, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        h = history.History()
-        h.save_tuples(tuples)
-        recovered = list(h.to_tuples())
-        assert recovered
-        for i in recovered:
-            assert i in tuples
-
-    def test_stats(self):
-        """
-        The data recovered should be equal to the one recorded.
-        """
-        tuples = (
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 2, 'prob', 2),
-            ('env', 3, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        stat_tuples = [
-            {'num_infected': 5, 'runtime': 0.2},
-            {'num_infected': 5, 'runtime': 0.2},
-            {'new': '40'},
-        ]
-        h = history.History()
-        h.save_tuples(tuples)
-        for stat in stat_tuples:
-            h.save_stats(stat)
-        recovered = h.get_stats()
-        assert recovered
-        assert recovered[0]['num_infected'] == 5
-        assert recovered[1]['runtime'] == 0.2
-        assert recovered[2]['new'] == '40'
-
-    def test_unflatten(self):
-        ex = {'count.neighbors.3': 4,
-              'count.times.2': 4,
-              'count.total.4': 4,
-              'mean.neighbors': 3,
-              'mean.times': 2,
-              'mean.total': 4,
-              't_step': 2,
-              'trial_id': 'exporter_sim_trial_1605817956-4475424'}
-        res = utils.unflatten_dict(ex)
-
-        assert 'count' in res
-        assert 'mean' in res
-        assert 't_step' in res
-        assert 'trial_id' in res

tests/test_main.py

@@ -126,7 +126,7 @@ class TestMain(TestCase):
         env = s.run_simulation(dry_run=True)[0]
         for agent in env.network_agents:
             last = 0
-            assert len(agent[None, None]) == 10
+            assert len(agent[None, None]) == 11
             for step, total in sorted(agent['total', None]):
                 assert total == last + 2
                 last = total
@@ -198,7 +198,7 @@ class TestMain(TestCase):
         """
         config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
         s = simulation.from_config(config)
-        for i in range(5):
+
         s.run_simulation(dry_run=True)
         nconfig = s.to_dict()
         del nconfig['topology']
@@ -211,7 +211,7 @@ class TestMain(TestCase):
         res = list(env.history_to_tuples())
         assert len(res) == len(env.environment_params)
 
-        env._now = 1
+        env.schedule.time = 1
         env['test'] = 'second_value'
         res = list(env.history_to_tuples())
 
@@ -281,7 +281,7 @@ class TestMain(TestCase):
                 'weight': 2
             },
         ]
-        converted = agents.deserialize_distribution(agent_distro)
+        converted = agents.deserialize_definition(agent_distro)
         assert converted[0]['agent_type'] == agents.CounterModel
         assert converted[1]['agent_type'] == CustomAgent
         pickle.dumps(converted)
@@ -297,14 +297,14 @@ class TestMain(TestCase):
                 'weight': 2
             },
         ]
-        converted = agents.serialize_distribution(agent_distro)
+        converted = agents.serialize_definition(agent_distro)
         assert converted[0]['agent_type'] == 'CounterModel'
         assert converted[1]['agent_type'] == 'test_main.CustomAgent'
         pickle.dumps(converted)
 
     def test_pickle_agent_environment(self):
         env = Environment(name='Test')
-        a = agents.BaseAgent(environment=env, agent_id=25)
+        a = agents.BaseAgent(model=env, unique_id=25)
 
         a['key'] = 'test'
 
@@ -345,7 +345,7 @@ class TestMain(TestCase):
 
     def test_until(self):
         config = {
-            'name': 'exporter_sim',
+            'name': 'until_sim',
             'network_params': {},
             'agent_type': 'CounterModel',
             'max_time': 2,

tests/test_mesa.py

@@ -0,0 +1,69 @@
+'''
+Mesa-SOIL integration tests
+
+We have to test that:
+- Mesa agents can be used in SOIL
+- Simplified soil agents can be used in mesa simulations
+- Mesa and soil agents can interact in a simulation
+
+- Mesa visualizations work with SOIL simulations
+
+'''
+from mesa import Agent, Model
+from mesa.time import RandomActivation
+from mesa.space import MultiGrid
+
+class MoneyAgent(Agent):
+    """ An agent with fixed initial wealth."""
+    def __init__(self, unique_id, model):
+        super().__init__(unique_id, model)
+        self.wealth = 1
+
+    def step(self):
+        self.move()
+        if self.wealth > 0:
+            self.give_money()
+
+    def give_money(self):
+        cellmates = self.model.grid.get_cell_list_contents([self.pos])
+        if len(cellmates) > 1:
+            other = self.random.choice(cellmates)
+            other.wealth += 1
+            self.wealth -= 1
+
+    def move(self):
+        possible_steps = self.model.grid.get_neighborhood(
+            self.pos,
+            moore=True,
+            include_center=False)
+        new_position = self.random.choice(possible_steps)
+        self.model.grid.move_agent(self, new_position)
+
+
+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)
+        self.schedule = RandomActivation(self)
+
+        # Create agents
+        for i in range(self.num_agents):
+            a = MoneyAgent(i, self)
+            self.schedule.add(a)
+
+            # Add the agent to a random grid cell
+            x = self.random.randrange(self.grid.width)
+            y = self.random.randrange(self.grid.height)
+            self.grid.place_agent(a, (x, y))
+
+    def step(self):
+        '''Advance the model by one step.'''
+        self.schedule.step()
+
+
+# model = MoneyModel(10)
+# for i in range(10):
+#     model.step()
+
+# agent_wealth = [a.wealth for a in model.schedule.agents]