Added rabbits

docs/quickstart.rst

@@ -13,7 +13,7 @@ Here's an example (``example.yml``).
     name: MyExampleSimulation
     max_time: 50
     num_trials: 3
-    timeout: 2
+    interval: 2
     network_params:
         network_type: barabasi_albert_graph
         n: 100
@@ -34,6 +34,12 @@ Here's an example (``example.yml``).
     environment_params:
         prob_infect: 0.075
 
+
+This example configuration will run three trials of a simulation containing a randomly generated network.
+The 100 nodes in the network will be SISaModel agents, 10% of them will start in the content state, 10% in the discontent state, and the remaining 80% in the neutral state.
+All agents will have access to the environment, which only contains one variable, ``prob_infected``.
+The state of the agents will be updated every 2 seconds (``interval``).
+
 Now run the simulation with the command line tool:
 
 .. code:: bash
@@ -41,7 +47,7 @@ Now run the simulation with the command line tool:
    soil example.yml
 
 Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
-Four types of objects are saved by default: a pickle of the simulation, a ``YAML`` representation of the simulation (to re-launch it), for every trial, a csv file with the content of the state of every network node and the environment parameters at every step of the simulation as well as the network in gephi format (``gexf``).
+Four types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a csv file with the content of the state of every network node and the environment parameters at every step of the simulation, as well as the network in gephi format (``gexf``).
 
 
 .. code::
@@ -54,12 +60,6 @@ Four types of objects are saved by default: a pickle of the simulation, a ``YAML
     │   └── Sim_prob_0_trial_0.gexf
 
 
-This example configuration will run three trials of a simulation containing a randomly generated network.
-The 100 nodes in the network will be SISaModel agents, 10% of them will start in the content state, 10% in the discontent state, and the remaining 80% in the neutral state.
-All agents will have access to the environment, which only contains one variable, ``prob_infected``.
-The state of the agents will be updated every 2 seconds (``timeout``).
-
-
 Network
 =======
 
@@ -94,7 +94,7 @@ For example, the following configuration is equivalent to :code:`nx.complete_gra
 Environment
 ============
 The environment is the place where the shared state of the simulation is stored.
-For instance, the probability of certain events.
+For instance, the probability of disease outbreak.
 The configuration file may specify the initial value of the environment parameters:
 
 .. code:: yaml
@@ -103,14 +103,17 @@ The configuration file may specify the initial value of the environment paramete
         daily_probability_of_earthquake: 0.001
         number_of_earthquakes: 0
 
+Any agent has unrestricted access to the environment.
+However, for the sake of simplicity, we recommend limiting environment updates to environment agents.
+
 Agents
 ======
 Agents are a way of modelling behavior.
 Agents can be characterized with two variables: an agent type (``agent_type``) and its state.
-Only one agent is executed at a time (generally, every ``timeout`` seconds), and it has access to its state and the environment parameters.
+Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters.
 Through the environment, it can access the network topology and the state of other agents.
 
-There are three three types of agents according to how they are added to the simulation: network agents, environment agent, and other agents.
+There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
 
 Network Agents
 ##############
@@ -118,13 +121,13 @@ Network agents are attached to a node in the topology.
 The configuration file allows you to specify how agents will be mapped to topology nodes.
 
 The simplest way is to specify a single type of agent.
-Hence, every node in the network will have an associated agent of that type.
+Hence, every node in the network will be associated to an agent of that type.
 
 .. code:: yaml
 
    agent_type: SISaModel
 
-It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (``weight``).
+It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property).
 For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
 
 .. code:: yaml

examples/custom.yml

@@ -0,0 +1,21 @@
+---
+load_module: custom_agents
+name: custom_agent_example
+max_time: 2500
+interval: 1
+seed: MySimulationSeed
+agent_type: RabbitModel
+environment_agents:
+    - agent_type: RandomAccident
+default_state:
+  mating_prob: 1
+topology:
+  nodes:
+    - id: 1
+      state:
+        gender: female
+    - id: 0
+      state:
+        gender: male
+  directed: true
+  links: []

examples/custom_agents.py

@@ -0,0 +1,103 @@
+import logging
+from soil.agents import NetworkAgent, FSM, state, default_state, BaseAgent
+from enum import Enum
+from random import random, choice
+from itertools import islice
+
+logger = logging.getLogger(__name__)
+
+class Genders(Enum):
+    male = 'male'
+    female = 'female'
+
+
+class RabbitModel(NetworkAgent, FSM):
+
+    defaults = {
+        'age': 0,
+        'gender': Genders.male.value,
+        'mating_prob': 0.001,
+        'offspring': 0,
+    }
+
+    sexual_maturity = 4*30
+    life_expectancy = 365 * 3
+    gestation = 33
+    pregnancy = -1
+    max_females = 10
+
+    @default_state
+    @state
+    def newborn(self):
+        self['age'] += 1
+
+        if self['age'] >= self.sexual_maturity:
+            return self.fertile
+
+    @state
+    def fertile(self):
+        self['age'] += 1
+        if self['age'] > self.life_expectancy:
+            return self.dead
+
+        if self['gender'] == Genders.female.value:
+            return
+
+        # Males try to mate
+        females = self.get_agents(state_id=self.fertile.id, gender=Genders.female.value, limit_neighbors=False)
+        for f in islice(females, self.max_females):
+            r = random()
+            if r < self['mating_prob']:
+                self.impregnate(f)
+                break # Take a break
+
+    def impregnate(self, whom):
+        if self['gender'] == Genders.female.value:
+            raise NotImplementedError('Females cannot impregnate')
+        whom['pregnancy'] = 0
+        whom['mate'] = self.id
+        whom.set_state(whom.pregnant)
+        logger.debug('{} impregnating: {}. {}'.format(self.id, whom.id, whom.state))
+
+    @state
+    def pregnant(self):
+        self['age'] += 1
+        if self['age'] > self.life_expectancy:
+            return self.dead
+
+        self['pregnancy'] += 1
+        logger.debug('Pregnancy: {}'.format(self['pregnancy']))
+        if self['pregnancy'] >= self.gestation:
+
+            state = {}
+            state['gender'] = choice(list(Genders)).value
+            child = self.env.add_node(self.__class__, state)
+            self.env.add_edge(self.id, child.id)
+            self.env.add_edge(self['mate'], child.id)
+            # self.add_edge()
+            logger.info("A rabbit has been born: {}. Total: {}".format(child.id, len(self.global_topology.nodes)))
+            self['offspring'] += 1
+            self.env.get_agent(self['mate'])['offspring'] += 1
+            del self['mate']
+            self['pregnancy'] = -1
+            return self.fertile
+
+    @state
+    def dead(self):
+        logger.info('Agent {} is dying'.format(self.id))
+        if 'pregnancy' in self and self['pregnancy'] > -1:
+            logger.info('A mother has died carrying a baby!: {}!'.format(self.state))
+        self.die()
+        return
+
+
+class RandomAccident(BaseAgent):
+
+    def step(self):
+        logger.debug('Killing some rabbits!')
+        prob_death = self.env.get('prob_death', -1)
+        for i in self.env.network_agents:
+            r = random()
+            if r < prob_death:
+                logger.info('I killed a rabbit: {}'.format(i.id))
+                i.set_state(i.dead)

examples/torvalds.yml

@@ -1,6 +1,6 @@
 ---
 name: torvalds_example
-max_time: 1
+max_time: 10
 interval: 2
 agent_type: CounterModel
 default_state:

soil/__init__.py

@@ -1,6 +1,8 @@
 import importlib
 import sys
 import os
+import pdb
+import logging
 
 __version__ = "0.9.7"
 
@@ -29,6 +31,8 @@ def main():
                         help='file containing the code of any custom agents.')
     parser.add_argument('--dry-run', '--dry', action='store_true',
                         help='Do not store the results of the simulation.')
+    parser.add_argument('--pdb', action='store_true',
+                        help='Use a pdb console in case of exception.')
     parser.add_argument('--output', '-o', type=str,
                         help='folder to write results to. It defaults to the current directory.')
 
@@ -38,8 +42,16 @@ def main():
         sys.path.append(os.getcwd())
         importlib.import_module(args.module)
 
-    print('Loading config file: {}'.format(args.file, args.output))
+    logging.basicConfig(level=logging.INFO)
+    logger = logging.getLogger(__name__)
+    logger.info('Loading config file: {}'.format(args.file, args.output))
+    try:
         simulation.run_from_config(args.file, dump=(not args.dry_run), results_dir=args.output)
+    except Exception as ex:
+        if args.pdb:
+            pdb.post_mortem()
+        else:
+            raise
 
 
 if __name__ == '__main__':

soil/__main__.py

@@ -1,8 +1,12 @@
 import importlib
 import sys
+import os
 import argparse
+import logging
 from . import simulation
 
+logger = logging.getLogger(__name__)
+
 
 def main():
 

soil/agents/__init__.py

@@ -8,6 +8,7 @@
 import nxsim
 from collections import OrderedDict
 from copy import deepcopy
+from functools import partial
 import json
 
 from functools import wraps
@@ -27,28 +28,33 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
     A special simpy BaseAgent that keeps track of its state history.
     """
 
-    def __init__(self, *args, **kwargs):
+    defaults = {}
-        self._history = OrderedDict()
+
+    def __init__(self, **kwargs):
         self._neighbors = None
-        super().__init__(*args, **kwargs)
+        self.alive = True
+        state = deepcopy(self.defaults)
+        state.update(kwargs.pop('state', {}))
+        kwargs['state'] = state
+        super().__init__(**kwargs)
 
     def __getitem__(self, key):
         if isinstance(key, tuple):
             k, t_step = key
-            if k is not None:
+            return self.env[t_step, self.id, k]
-                if t_step is not None:
+        return self.state.get(key, None)
-                    return self._history[t_step][k]
+
-                else:
+    def __delitem__(self, key):
-                    return {tt: tv.get(k, None) for tt, tv in self._history.items()}
+        del self.state[key]
-            else:
+
-                return self._history[t_step]
+    def __contains__(self, key):
-        return self.state[key]
+        return key in self.state
 
     def __setitem__(self, key, value):
         self.state[key] = value
 
-    def save_state(self):
+    def get(self, key, default=None):
-        self._history[self.now] = deepcopy(self.state)
+        return self[key] if key in self else default
 
     @property
     def now(self):
@@ -59,19 +65,21 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
             return None
 
     def run(self):
-        while True:
+        while self.alive:
             res = self.step()
             yield res or self.env.timeout(self.env.interval)
 
+    def die(self, remove=False):
+        self.alive = False
+        if remove:
+            super().die()
+
     def step(self):
         pass
 
     def to_json(self):
         return json.dumps(self._history)
 
-
-class NetworkAgent(BaseAgent, nxsim.BaseNetworkAgent):
-
     def count_agents(self, state_id=None, limit_neighbors=False):
         if limit_neighbors:
             agents = self.global_topology.neighbors(self.id)
@@ -84,6 +92,25 @@ class NetworkAgent(BaseAgent, nxsim.BaseNetworkAgent):
             count += 1
         return count
 
+    def get_agents(self, state_id=None, limit_neighbors=False, **kwargs):
+        if limit_neighbors:
+            agents = super().get_agents(state_id, limit_neighbors)
+        else:
+            agents = filter(lambda x: state_id is None or x.state.get('id', None) == state_id,
+                            self.env.agents)
+
+        def matches_all(agent):
+            state = agent.state
+            for k, v in kwargs.items():
+                if state.get(k, None) != v:
+                    return False
+            return True
+
+        return filter(matches_all, agents)
+
+
+class NetworkAgent(BaseAgent, nxsim.BaseNetworkAgent):
+
     def count_neighboring_agents(self, state_id=None):
         return self.count_agents(state_id, limit_neighbors=True)
 
@@ -155,6 +182,13 @@ class FSM(BaseAgent, metaclass=MetaFSM):
             raise Exception('{} is not a valid id for {}'.format(next_state, self))
         self.states[next_state](self)
 
+    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.state['id'] = state
+
 
 from .BassModel import *
 from .BigMarketModel import *

soil/environment.py

@@ -1,12 +1,16 @@
 import os
+import time
 import csv
 import weakref
-from random import random
+import random
 from copy import deepcopy
+from functools import partial
 
 import networkx as nx
 import nxsim
 
+from . import utils
+
 
 class SoilEnvironment(nxsim.NetworkEnvironment):
 
@@ -16,6 +20,8 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
                  states=None,
                  default_state=None,
                  interval=1,
+                 seed=None,
+                 dump=False,
                  *args, **kwargs):
         self.name = name or 'UnnamedEnvironment'
         self.states = deepcopy(states) or {}
@@ -25,8 +31,11 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         self._history = {}
         self.interval = interval
         self.logger = None
+        self.dump = dump
         # Add environment agents first, so their events get
         # executed before network agents
+        self['SEED'] = seed or time.time()
+        random.seed(self['SEED'])
         self.environment_agents = environment_agents or []
         self.network_agents = network_agents or []
         self.process(self.save_state())
@@ -65,26 +74,32 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         for ix in self.G.nodes():
             i = ix
             node = self.G.node[i]
-            v = random()
+            agent, state = utils.agent_from_distribution(network_agents)
-            found = False
+            self.set_agent(i, agent_type=agent, state=state)
-            for d in network_agents:
+
-                threshold = d['threshold']
+    def set_agent(self, agent_id, agent_type, state=None):
-                if v >= threshold[0] and v < threshold[1]:
+        node = self.G.nodes[agent_id]
-                    agent = d['agent_type']
+        defstate = deepcopy(self.default_state)
-                    state = None
+        defstate.update(self.states.get(agent_id, {}))
-                    if 'state' in d:
+        if state:
-                        state = deepcopy(d['state'])
+            defstate.update(state)
-                    else:
+        state = defstate
-                        try:
+        state.update(node.get('state', {}))
-                            state = self.states[i]
+        a = agent_type(environment=self,
-                        except (IndexError, KeyError):
+                       agent_id=agent_id,
-                            state = deepcopy(self.default_state)
-                    node['agent'] = agent(environment=self,
-                                          agent_id=i,
                        state=state)
-                    found = True
+        node['agent'] = a
-                    break
+        return a
-            assert found
+
+    def add_node(self, agent_type, state=None):
+        agent_id = int(len(self.G.nodes()))
+        self.G.add_node(agent_id)
+        a = self.set_agent(agent_id, agent_type, state)
+        a['visible'] = True
+        return a
+
+    def add_edge(self, agent1, agent2, attrs=None):
+        return self.G.add_edge(agent1, agent2)
 
     def run(self, *args, **kwargs):
         self._save_state()
@@ -92,9 +107,12 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         self._save_state()
 
     def _save_state(self):
+        # for agent in self.agents:
+        #     agent.save_state()
+        nowd = self._history[self.now] = {}
+        nowd['env'] = deepcopy(self.environment_params)
         for agent in self.agents:
-            agent.save_state()
+            nowd[agent.id] = deepcopy(agent.state)
-        self._history[self.now] = deepcopy(self.environment_params)
 
     def save_state(self):
         while True:
@@ -107,11 +125,32 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
             self._save_state()
 
     def __getitem__(self, key):
+        if isinstance(key, tuple):
+            t_step, agent_id, k = key
+
+            def key_or_dict(d, k, nfunc):
+                if k is None:
+                    if d is None:
+                        return {}
+                    return {k: nfunc(v) for k, v in d.items()}
+                if k in d:
+                    return nfunc(d[k])
+                return {}
+
+            f1 = partial(key_or_dict, k=k, nfunc=lambda x: x)
+            f2 = partial(key_or_dict, k=agent_id, nfunc=f1)
+            return key_or_dict(self._history, t_step, f2)
         return self.environment_params[key]
 
     def __setitem__(self, key, value):
         self.environment_params[key] = value
 
+    def __contains__(self, key):
+        return key in self.environment_params
+
+    def get(self, key, default=None):
+        return self[key] if key in self else default
+
     def get_path(self, dir_path=None):
         dir_path = dir_path or self.sim().dir_path
         if not os.path.exists(dir_path):
@@ -141,13 +180,10 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         nx.write_gexf(G, graph_path, version="1.2draft")
 
     def history_to_tuples(self):
-        for tstep, state in self._history.items():
+        for tstep, states in self._history.items():
+            for a_id, state in states.items():
                 for attribute, value in state.items():
-                yield ('env', tstep, attribute, value)
+                    yield (a_id, tstep, attribute, value)
-        for agent in self.agents:
-            for tstep, state in agent._history.items():
-                for attribute, value in state.items():
-                    yield (agent.id, tstep, attribute, value)
 
     def history_to_graph(self):
         G = nx.Graph(self.G)
@@ -159,7 +195,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
             spells = []
             lastvisible = False
             laststep = None
-            for t_step, state in reversed(list(agent._history.items())):
+            for t_step, state in reversed(list(self[None, agent.id, None].items())):
                 for attribute, value in state.items():
                     if attribute == 'visible':
                         nowvisible = state[attribute]

soil/simulation.py

@@ -1,14 +1,14 @@
 import weakref
 import os
-import csv
 import time
+import imp
+import sys
 import yaml
+import logging
 import networkx as nx
 from networkx.readwrite import json_graph
 
 from copy import deepcopy
-from random import random
-from matplotlib import pyplot as plt
 
 import pickle
 
@@ -16,6 +16,7 @@ from nxsim import NetworkSimulation
 
 from . import agents, utils, environment, basestring
 
+logger = logging.getLogger(__name__)
 
 class SoilSimulation(NetworkSimulation):
     """
@@ -47,9 +48,9 @@ class SoilSimulation(NetworkSimulation):
     """
     def __init__(self, name=None, topology=None, network_params=None,
                  network_agents=None, agent_type=None, states=None,
-                 default_state=None, interval=1,
+                 default_state=None, interval=1, dump=False,
-                 dir_path=None, num_trials=3, max_time=100,
+                 dir_path=None, num_trials=1, max_time=100,
-                 agent_module=None,
+                 agent_module=None, load_module=None, seed=None,
                  environment_agents=None, environment_params=None):
 
         if topology is None:
@@ -58,6 +59,8 @@ class SoilSimulation(NetworkSimulation):
         elif isinstance(topology, basestring) or isinstance(topology, dict):
             topology = json_graph.node_link_graph(topology)
 
+
+        self.load_module = load_module
         self.topology = nx.Graph(topology)
         self.network_params = network_params
         self.name = name or 'UnnamedSimulation'
@@ -66,8 +69,15 @@ class SoilSimulation(NetworkSimulation):
         self.default_state = default_state or {}
         self.dir_path = dir_path or os.getcwd()
         self.interval = interval
+        self.seed = seed
+        self.dump = dump
         self.environment_params = environment_params or {}
 
+        if load_module:
+            path = sys.path + [self.dir_path]
+            f, fp, desc = imp.find_module(load_module, path)
+            imp.load_module('soil.agents.custom', f, fp, desc)
+
         environment_agents = environment_agents or []
         self.environment_agents = self._convert_agent_types(environment_agents)
 
@@ -132,12 +142,23 @@ class SoilSimulation(NetworkSimulation):
     def run(self):
         return list(self.run_simulation_gen())
 
-    def run_simulation_gen(self):
+    def run_simulation_gen(self, *args, **kwargs):
         with utils.timer('simulation'):
             for i in range(self.num_trials):
-                yield self.run_trial(i)
+                res = self.run_trial(i)
+                if self.dump:
+                    res.dump_gexf(self.dir_path)
+                    res.dump_csv(self.dir_path)
+                yield res
 
-    def run_trial(self, trial_id=0):
+            if self.dump:
+                logger.info('Dumping results to {}'.format(self.dir_path))
+                self.dump_pickle(self.dir_path)
+                self.dump_yaml(self.dir_path)
+            else:
+                logger.info('NOT dumping results')
+
+    def run_trial(self, trial_id=0, dump=False, dir_path=None):
         """Run a single trial of the simulation
 
         Parameters
@@ -145,11 +166,13 @@ class SoilSimulation(NetworkSimulation):
         trial_id : int
         """
         # Set-up trial environment and graph
-        print('Trial: {}'.format(trial_id))
+        logger.info('Trial: {}'.format(trial_id))
         env_name = '{}_trial_{}'.format(self.name, trial_id)
         env = environment.SoilEnvironment(name=env_name,
                                           topology=self.topology.copy(),
+                                          seed=self.seed,
                                           initial_time=0,
+                                          dump=self.dump,
                                           interval=self.interval,
                                           network_agents=self.network_agents,
                                           states=self.states,
@@ -159,7 +182,7 @@ class SoilSimulation(NetworkSimulation):
 
         env.sim = weakref.ref(self)
         # Set up agents on nodes
-        print('\tRunning')
+        logger.info('\tRunning')
         with utils.timer('trial'):
             env.run(until=self.max_time)
         return env
@@ -221,7 +244,7 @@ def run_from_config(*configs, dump=True, results_dir=None, timestamp=False):
     for config_def in configs:
         for config, cpath in utils.load_config(config_def):
             name = config.get('name', 'unnamed')
-            print("Using config(s): {name}".format(name=name))
+            logger.info("Using config(s): {name}".format(name=name))
 
             sim = SoilSimulation(**config)
             if timestamp:
@@ -229,13 +252,7 @@ def run_from_config(*configs, dump=True, results_dir=None, timestamp=False):
                                             time.strftime("%Y-%m-%d_%H:%M:%S"))
             else:
                 sim_folder = sim.name
-            dir_path = os.path.join(results_dir,
+            sim.dir_path = os.path.join(results_dir, sim_folder)
-                                    sim_folder)
+            sim.dump = dump
+            logger.info('Dumping results to {} : {}'.format(sim.dir_path, dump))
             results = sim.run_simulation()
-
-            if dump:
-                sim.dump_pickle(dir_path)
-                sim.dump_yaml(dir_path)
-                for env in results:
-                    env.dump_gexf(dir_path)
-                    env.dump_csv(dir_path)

soil/utils.py

@@ -1,12 +1,17 @@
 import os
 import yaml
+import logging
 from time import time
 from glob import glob
+from random import random
+from copy import deepcopy
 
 import networkx as nx
 
 from contextlib import contextmanager
 
+logger = logging.getLogger(__name__)
+
 
 def load_network(network_params, dir_path=None):
     path = network_params.get('path', None)
@@ -51,7 +56,7 @@ def load_config(config):
 
 
 @contextmanager
-def timer(name='task', pre="", function=print, to_object=None):
+def timer(name='task', pre="", function=logger.info, to_object=None):
     start = time()
     yield start
     end = time()
@@ -59,3 +64,18 @@ def timer(name='task', pre="", function=print, to_object=None):
     if to_object:
         to_object.start = start
         to_object.end = end
+
+
+def agent_from_distribution(distribution, value=-1):
+    """Find the agent """
+    if value < 0:
+        value = random()
+    for d in distribution:
+        threshold = d['threshold']
+        if value >= threshold[0] and value < threshold[1]:
+            state = None
+            if 'state' in d:
+                state = deepcopy(d['state'])
+            return d['agent_type'], state
+
+    raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))

tests/test_main.py

@@ -111,7 +111,7 @@ class TestMain(TestCase):
         env = s.run_simulation()[0]
         for agent in env.network_agents:
             last = 0
-            assert len(agent._history) == 11
+            assert len(agent[None, None]) == 11
             for step, total in agent['total', None].items():
                 if step > 0:
                     assert total == last + 2
@@ -177,6 +177,7 @@ class TestMain(TestCase):
             recovered = yaml.load(serial)
         with utils.timer('deleting'):
             del recovered['topology']
+            del recovered['load_module']
         assert config == recovered
 
     def test_configuration_changes(self):
@@ -190,6 +191,7 @@ class TestMain(TestCase):
             s.run_simulation()
             nconfig = s.to_dict()
             del nconfig['topology']
+            del nconfig['load_module']
             assert config == nconfig
 
     def test_examples(self):
@@ -205,9 +207,11 @@ def make_example_test(path, config):
         s = simulation.from_config(config)
         envs = s.run_simulation()
         for env in envs:
-            n = config['network_params'].get('n', None)
+            try:
-            if n is not None:
+                n = config['network_params']['n']
                 assert len(env.get_agents()) == n
+            except KeyError:
+                pass
         os.chdir(root)
     return wrapped