Parallelism and granular exporting options

* Graphs are not saved by default (not backwards compatible)
* Modified newsspread examples
* More granular options to save results (exporting to CSV and GEXF are now
optional)
* Updated tutorial to include exporting options
* Removed references from environment to simulation
* Added parallelism to simulations (can be turned off with a flag or argument).

examples/newsspread/NewsSpread.yml

@@ -68,7 +68,7 @@ network_agents:
 - agent_type: HerdViewer
   state:
     has_tv: true
-    id: infected
+    id: neutral
   weight: 1
 - agent_type: HerdViewer
   state:
@@ -95,7 +95,7 @@ network_agents:
 - agent_type: HerdViewer
   state:
     has_tv: true
-    id: infected
+    id: neutral
   weight: 1
 - agent_type: WiseViewer
   state:
@@ -121,7 +121,7 @@ network_agents:
 - agent_type: WiseViewer
   state:
     has_tv: true
-    id: infected
+    id: neutral
   weight: 1
 - agent_type: WiseViewer
   state:

examples/newsspread/newsspread.py

@@ -1,5 +1,4 @@
-from soil.agents import BaseAgent,FSM, state, default_state
-import random
+from soil.agents import FSM, state, default_state, prob
 import logging
 
 
@@ -10,30 +9,26 @@ class DumbViewer(FSM):
     '''
     defaults = {
         'prob_neighbor_spread': 0.5,
-        'prob_neighbor_cure': 0.25,
+        'prob_tv_spread': 0.1,
     }
 
     @default_state
     @state
     def neutral(self):
-        r = random.random()
-        if self['has_tv'] and r < self.env['prob_tv_spread']:
-            self.infect()
-        return
+        if self['has_tv']:
+            if prob(self.env['prob_tv_spread']):
+                self.set_state(self.infected)
 
     @state
     def infected(self):
         for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
-            prob_infect = self.env['prob_neighbor_spread']
-            r = random.random()
-            if r < prob_infect:
-                self.set_state(self.infected.id)
+            if prob(self.env['prob_neighbor_spread']):
                 neighbor.infect()
-        return
 
     def infect(self):
         self.set_state(self.infected)
 
+
 class HerdViewer(DumbViewer):
     '''
     A viewer whose probability of infection depends on the state of its neighbors.
@@ -46,34 +41,41 @@ class HerdViewer(DumbViewer):
         total = self.count_neighboring_agents()
         prob_infect = self.env['prob_neighbor_spread'] * infected/total
         self.debug('prob_infect', prob_infect)
-        r = random.random()
-        if r < prob_infect:
+        if prob(prob_infect):
             self.set_state(self.infected.id)
 
+
 class WiseViewer(HerdViewer):
     '''
     A viewer that can change its mind.
     '''
+
+    defaults = {
+        'prob_neighbor_spread': 0.5,
+        'prob_neighbor_cure': 0.25,
+        'prob_tv_spread': 0.1,
+    }
+
     @state
     def cured(self):
         prob_cure = self.env['prob_neighbor_cure']
         for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
-            r = random.random()
-            if r < prob_cure:
+            if prob(prob_cure):
                 try:
                     neighbor.cure()
                 except AttributeError:
                     self.debug('Viewer {} cannot be cured'.format(neighbor.id))
-        return
 
     def cure(self):
         self.set_state(self.cured.id)
 
     @state
     def infected(self):
-        prob_cure = self.env['prob_neighbor_cure']
-        r = random.random()
-        if r < prob_cure:
-            self.cure()
-            return
-        return super().infected()
+        cured = max(self.count_neighboring_agents(self.cured.id),
+                    1.0)
+        infected = max(self.count_neighboring_agents(self.infected.id),
+                       1.0)
+        prob_cure = self.env['prob_neighbor_cure'] * (cured/infected)
+        if prob(prob_cure):
+            return self.cure()
+        return self.set_state(super().infected)

soil/__init__.py

@@ -35,8 +35,14 @@ def main():
                         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,
+    parser.add_argument('--graph', '-g', action='store_true',
+                        help='Dump GEXF graph. Defaults to false.')
+    parser.add_argument('--csv', action='store_true',
+                        help='Dump history in CSV format. Defaults to false.')
+    parser.add_argument('--output', '-o', type=str, default="soil_output",
                         help='folder to write results to. It defaults to the current directory.')
+    parser.add_argument('--synchronous', action='store_true',
+                        help='Run trials serially and synchronously instead of in parallel. Defaults to false.')
 
     args = parser.parse_args()
 
@@ -47,7 +53,17 @@ def main():
     logging.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)
+        dump = []
+        if not args.dry_run:
+            if args.csv:
+                dump.append('csv')
+            if args.graph:
+                dump.append('gexf')
+        simulation.run_from_config(args.file,
+                                   dry_run=args.dry_run,
+                                   dump=dump,
+                                   parallel=(not args.synchronous),
+                                   results_dir=args.output)
     except Exception as ex:
         if args.pdb:
             pdb.post_mortem()

soil/agents/DrawingAgent.py

@@ -15,4 +15,4 @@ class DrawingAgent(BaseAgent):
         # Outside effects
         f = plt.figure()
         nx.draw(self.env.G, node_size=10, width=0.2, pos=nx.spring_layout(self.env.G, scale=100), ax=f.add_subplot(111))
-        f.savefig(os.path.join(self.env.sim().dir_path, "graph-"+str(self.env.now)+".png"))
+        f.savefig(os.path.join(self.env.get_path(), "graph-"+str(self.env.now)+".png"))

soil/agents/__init__.py

@@ -12,9 +12,9 @@ from copy import deepcopy
 from functools import partial
 import json
 
-
 from functools import wraps
 
+from .. import utils
 
 agent_types = {}
 
@@ -41,7 +41,7 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
         super().__init__(**kwargs)
         if not hasattr(self, 'level'):
             self.level = logging.DEBUG
-        self.logger = logging.getLogger('Agent-{}'.format(self.id))
+        self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name, self.id))
         self.logger.setLevel(self.level)
 
 
@@ -140,20 +140,24 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
 
 
 def state(func):
+    '''
+    A state function should return either a state id, or a tuple (state_id, when)
+    The default value for state_id is the current state id.
+    The default value for when is the interval defined in the nevironment.
+    '''
 
     @wraps(func)
     def func_wrapper(self):
-        when = None
         next_state = func(self)
+        when = None
+        if next_state is None:
+            return when
         try:
             next_state, when = next_state
-        except TypeError:
+        except (ValueError, TypeError):
             pass
         if next_state:
-            try:
-                self.state['id'] = next_state.id
-            except AttributeError:
-                raise ValueError('State id %s is not valid.' % next_state)
+            self.set_state(next_state)
         return when
 
     func_wrapper.id = func.__name__
@@ -212,6 +216,116 @@ class FSM(BaseAgent, metaclass=MetaFSM):
         if state not in self.states:
             raise ValueError('{} is not a valid state'.format(state))
         self.state['id'] = state
+        return state
+
+
+def prob(prob=1):
+    '''
+    A true/False uniform distribution with a given probability.
+    To be used like this:
+
+    .. code-block:: python
+          
+          if prob(0.3):
+              do_something()
+
+    '''
+    r = random.random()
+    return r < prob
+
+
+def calculate_distribution(network_agents=None,
+                           agent_type=None):
+    '''
+    Calculate the threshold values (thresholds for a uniform distribution)
+    of an agent distribution given the weights of each agent type.
+
+    The input has this form: ::
+
+            [
+            {'agent_type': 'agent_type_1',
+                'weight': 0.2,
+                'state': {
+                    'id': 0
+                }
+            },
+            {'agent_type': 'agent_type_2',
+                'weight': 0.8,
+                'state': {
+                    'id': 1
+                }
+            }
+            ]
+
+    In this example, 20% of the nodes will be marked as type
+    'agent_type_1'.
+    '''
+    if network_agents:
+        network_agents = deepcopy(network_agents)
+    elif agent_type:
+        network_agents = [{'agent_type': agent_type}]
+    else:
+        return []
+
+    # Calculate the thresholds
+    total = sum(x.get('weight', 1) for x in network_agents)
+    acc = 0
+    for v in network_agents:
+        upper = acc + (v.get('weight', 1)/total)
+        v['threshold'] = [acc, upper]
+        acc = upper
+    return network_agents
+
+
+def _serialize_distribution(network_agents):
+    d = _convert_agent_types(network_agents,
+                             to_string=True)
+    '''
+    When serializing an agent distribution, remove the thresholds, in order
+    to avoid cluttering the YAML definition file.
+    '''
+    for v in d:
+        if 'threshold' in v:
+            del v['threshold']
+    return d
+
+
+def _validate_states(states, topology):
+    '''Validate states to avoid ignoring states during initialization'''
+    states = states or []
+    if isinstance(states, dict):
+        for x in states:
+            assert x in topology.node
+    else:
+        assert len(states) <= len(topology)
+    return states
+
+
+def _convert_agent_types(ind, to_string=False):
+    '''Convenience method to allow specifying agents by class or class name.'''
+    d = deepcopy(ind)
+    for v in d:
+        agent_type = v['agent_type']
+        if to_string and not isinstance(agent_type, str):
+            v['agent_type'] = str(agent_type.__name__)
+        elif not to_string and isinstance(agent_type, str):
+            v['agent_type'] = agent_types[agent_type]
+    return d
+
+
+def _agent_from_distribution(distribution, value=-1):
+    """Used in the initialization of agents given an agent distribution."""
+    if value < 0:
+        value = random.random()
+    for d in distribution:
+        threshold = d['threshold']
+        if value >= threshold[0] and value < threshold[1]:
+            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))
 
 
 from .BassModel import *

soil/environment.py

@@ -1,16 +1,16 @@
 import os
 import sqlite3
 import time
-import weakref
 import csv
 import random
 import simpy
 from copy import deepcopy
+from networkx.readwrite import json_graph
 
 import networkx as nx
 import nxsim
 
-from . import utils
+from . import utils, agents
 
 
 class SoilEnvironment(nxsim.NetworkEnvironment):
@@ -22,21 +22,18 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
                  default_state=None,
                  interval=1,
                  seed=None,
-                 dump=False,
-                 simulation=None,
+                 dry_run=False,
+                 dir_path=None,
                  *args, **kwargs):
         self.name = name or 'UnnamedEnvironment'
         if isinstance(states, list):
             states = dict(enumerate(states))
         self.states = deepcopy(states) if states else {}
         self.default_state = deepcopy(default_state) or {}
-        self.sim = weakref.ref(simulation)
-        if 'topology' not in kwargs and simulation:
-            kwargs['topology'] = self.sim().topology.copy()
         super().__init__(*args, **kwargs)
         self._env_agents = {}
+        self.dry_run = dry_run
         self.interval = interval
-        self.dump = dump
         # Add environment agents first, so their events get
         # executed before network agents
         self['SEED'] = seed or time.time()
@@ -44,10 +41,11 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         self.process(self.save_state())
         self.environment_agents = environment_agents or []
         self.network_agents = network_agents or []
-        if self.dump:
-            self._db_path = os.path.join(self.get_path(), '{}.db.sqlite'.format(self.name))
-        else:
+        self.dir_path = dir_path
+        if self.dry_run:
             self._db_path = ":memory:"
+        else:
+            self._db_path = os.path.join(self.get_path(), '{}.db.sqlite'.format(self.name))
         self.create_db(self._db_path)
 
     def create_db(self, db_path=None):
@@ -93,7 +91,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         for ix in self.G.nodes():
             i = ix
             node = self.G.node[i]
-            agent, state = utils.agent_from_distribution(network_agents)
+            agent, state = agents._agent_from_distribution(network_agents)
             self.set_agent(i, agent_type=agent, state=state)
 
     def set_agent(self, agent_id, agent_type, state=None):
@@ -200,7 +198,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         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
+        dir_path = dir_path or self.dir_path
         if not os.path.exists(dir_path):
             os.makedirs(dir_path)
         return dir_path
@@ -227,6 +225,19 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
                                   self.name+".gexf")
         nx.write_gexf(G, graph_path, version="1.2draft")
 
+    def dump(self, dir_path=None, formats=None):
+        if not formats:
+            return
+        functions = {
+            'csv': self.dump_csv,
+            'gexf': self.dump_gexf
+        }
+        for f in formats:
+            if f in functions:
+                functions[f](dir_path)
+            else:
+                raise ValueError('Unknown format: {}'.format(f))
+
     def state_to_tuples(self, now=None):
         if now is None:
             now = self.now
@@ -289,3 +300,23 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
                 G.add_node(agent.id, **attributes)
 
         return G
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state['G'] = json_graph.node_link_data(self.G)
+        state['network_agents'] = agents.serialize_distribution(self.network_agents)
+        state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
+                                                                 to_string=True)
+        del state['_queue']
+        import inspect
+        for k, v in state.items():
+            if inspect.isgeneratorfunction(v):
+                print(k, v, type(v))
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__ = state
+        self.G = json_graph.node_link_graph(state['G'])
+        self.network_agents = self.calculate_distribution(self._convert_agent_types(self.network_agents))
+        self.environment_agents = self._convert_agent_types(self.environment_agents)
+        return state

soil/simulation.py

@@ -5,14 +5,14 @@ import sys
 import yaml
 import networkx as nx
 from networkx.readwrite import json_graph
-
-from copy import deepcopy
+from multiprocessing import Pool
+from functools import partial
 
 import pickle
 
 from nxsim import NetworkSimulation
 
-from . import agents, utils, environment, basestring
+from . import utils, environment, basestring, agents
 from .utils import logger
 
 
@@ -46,7 +46,7 @@ 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, dump=False,
+                 default_state=None, interval=1, dump=None, dry_run=False,
                  dir_path=None, num_trials=1, max_time=100,
                  agent_module=None, load_module=None, seed=None,
                  environment_agents=None, environment_params=None):
@@ -57,7 +57,6 @@ 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
@@ -69,94 +68,64 @@ class SoilSimulation(NetworkSimulation):
         self.interval = interval
         self.seed = str(seed) or str(time.time())
         self.dump = dump
+        self.dry_run = dry_run
         self.environment_params = environment_params or {}
 
         if load_module:
-            path = sys.path + [self.dir_path]
+            path = sys.path + [self.dir_path, os.getcwd()]
             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)
+        self.environment_agents = agents._convert_agent_types(environment_agents)
 
-        distro = self.calculate_distribution(network_agents,
-                                             agent_type)
-        self.network_agents = self._convert_agent_types(distro)
+        distro = agents.calculate_distribution(network_agents,
+                                               agent_type)
+        self.network_agents = agents._convert_agent_types(distro)
 
-        self.states = self.validate_states(states,
-                                           self.topology)
+        self.states = agents._validate_states(states,
+                                              self.topology)
 
-    def calculate_distribution(self,
-                               network_agents=None,
-                               agent_type=None):
-        if network_agents:
-            network_agents = deepcopy(network_agents)
-        elif agent_type:
-            network_agents = [{'agent_type': agent_type}]
-        else:
-            return []
+    def run_simulation(self, *args, **kwargs):
+        return self.run(*args, **kwargs)
 
-        # Calculate the thresholds
-        total = sum(x.get('weight', 1) for x in network_agents)
-        acc = 0
-        for v in network_agents:
-            upper = acc + (v.get('weight', 1)/total)
-            v['threshold'] = [acc, upper]
-            acc = upper
-        return network_agents
+    def run(self, *args, **kwargs):
+        return list(self.run_simulation_gen(*args, **kwargs))
 
-    def serialize_distribution(self):
-        d = self._convert_agent_types(self.network_agents,
-                                      to_string=True)
-        for v in d:
-            if 'threshold' in v:
-                del v['threshold']
-        return d
-
-    def _convert_agent_types(self, ind, to_string=False):
-        d = deepcopy(ind)
-        for v in d:
-            agent_type = v['agent_type']
-            if to_string and not isinstance(agent_type, str):
-                v['agent_type'] = str(agent_type.__name__)
-            elif not to_string and isinstance(agent_type, str):
-                v['agent_type'] = agents.agent_types[agent_type]
-        return d
-
-    def validate_states(self, states, topology):
-        states = states or []
-        # Validate states to avoid ignoring states during
-        # initialization
-        if isinstance(states, dict):
-            for x in states:
-                assert x in self.topology.node
-        else:
-            assert len(states) <= len(self.topology)
-        return states
-
-    def run_simulation(self):
-        return self.run()
-
-    def run(self):
-        return list(self.run_simulation_gen())
-
-    def run_simulation_gen(self, *args, **kwargs):
+    def run_simulation_gen(self, *args, parallel=False, **kwargs):
+        p = Pool()
         with utils.timer('simulation'):
-            for i in range(self.num_trials):
-                res = self.run_trial(i)
-                if self.dump:
-                    res.dump_gexf(self.dir_path)
-                    res.dump_csv(self.dir_path)
-                yield res
-
-            if self.dump:
+            if parallel:
+                func = partial(self.run_trial, return_env=not parallel)
+                for i in p.imap_unordered(func, range(self.num_trials)):
+                    yield i
+            else:
+                for i in range(self.num_trials):
+                    yield self.run_trial(i)
+            if not self.dry_run:
                 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):
+    def get_env(self, trial_id=0, dump=False, dir_path=None):
+        env_name = '{}_trial_{}'.format(self.name, trial_id)
+        env = environment.SoilEnvironment(name=env_name,
+                                          topology=self.topology.copy(),
+                                          seed=self.seed+env_name,
+                                          initial_time=0,
+                                          dry_run=self.dry_run,
+                                          interval=self.interval,
+                                          network_agents=self.network_agents,
+                                          states=self.states,
+                                          default_state=self.default_state,
+                                          environment_agents=self.environment_agents,
+                                          dir_path=dir_path or self.dir_path,
+                                          **self.environment_params)
+        return env
+
+    def run_trial(self, trial_id=0, dump=False, dir_path=None, until=None, return_env=False):
         """Run a single trial of the simulation
 
         Parameters
@@ -164,25 +133,16 @@ class SoilSimulation(NetworkSimulation):
         trial_id : int
         """
         # Set-up trial environment and graph
-        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+env_name,
-                                          initial_time=0,
-                                          dump=self.dump,
-                                          interval=self.interval,
-                                          network_agents=self.network_agents,
-                                          states=self.states,
-                                          default_state=self.default_state,
-                                          environment_agents=self.environment_agents,
-                                          simulation=self,
-                                          **self.environment_params)
+        until = until or self.max_time
+        env = self.get_env(trial_id=trial_id, dump=dump, dir_path=dir_path)
         # Set up agents on nodes
-        logger.info('\tRunning')
-        with utils.timer('trial'):
-            env.run(until=self.max_time)
-        return env
+        with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)):
+            env.run(until)
+        if self.dump and not self.dry_run:
+            with utils.timer('Dumping simulation {} trial {}'.format(self.name, trial_id)):
+                env.dump(dir_path, formats=self.dump)
+        if return_env:
+            return env
 
     def to_dict(self):
         return self.__getstate__()
@@ -213,16 +173,16 @@ class SoilSimulation(NetworkSimulation):
     def __getstate__(self):
         state = self.__dict__.copy()
         state['topology'] = json_graph.node_link_data(self.topology)
-        state['network_agents'] = self.serialize_distribution()
-        state['environment_agents'] = self._convert_agent_types(self.environment_agents,
-                                                          to_string=True)
+        state['network_agents'] = agents._serialize_distribution(self.network_agents)
+        state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
+                                                                 to_string=True)
         return state
 
     def __setstate__(self, state):
         self.__dict__ = state
         self.topology = json_graph.node_link_graph(state['topology'])
-        self.network_agents = self._convert_agent_types(self.network_agents)
-        self.environment_agents = self._convert_agent_types(self.environment_agents)
+        self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents))
+        self.environment_agents = agents._convert_agent_types(self.environment_agents)
         return state
 
 
@@ -235,21 +195,18 @@ def from_config(config, G=None):
     return sim
 
 
-def run_from_config(*configs, dump=True, results_dir=None, timestamp=False):
-    if not results_dir:
-        results_dir = 'soil_output'
+def run_from_config(*configs, results_dir='soil_output', dump=None, timestamp=False,  **kwargs):
     for config_def in configs:
         for config, cpath in utils.load_config(config_def):
             name = config.get('name', 'unnamed')
             logger.info("Using config(s): {name}".format(name=name))
 
-            sim = SoilSimulation(**config)
             if timestamp:
-                sim_folder = '{}_{}'.format(sim.name,
+                sim_folder = '{}_{}'.format(name,
                                             time.strftime("%Y-%m-%d_%H:%M:%S"))
             else:
-                sim_folder = sim.name
-            sim.dir_path = os.path.join(results_dir, sim_folder)
-            sim.dump = dump
-            logger.info('Dumping results to {} : {}'.format(sim.dir_path, dump))
-            results = sim.run_simulation()
+                sim_folder = name
+            dir_path = os.path.join(results_dir, sim_folder)
+            sim = SoilSimulation(dir_path=dir_path, dump=dump, **config)
+            logger.info('Dumping results to {} : {}'.format(sim.dir_path, sim.dump))
+            results = sim.run_simulation(**kwargs)

soil/utils.py

@@ -11,7 +11,7 @@ import networkx as nx
 from contextlib import contextmanager
 
 
-logger = logging.getLogger(__name__)
+logger = logging.getLogger('soil')
 logger.setLevel(logging.INFO)
 
 
@@ -62,6 +62,7 @@ def load_config(config):
 @contextmanager
 def timer(name='task', pre="", function=logger.info, to_object=None):
     start = time()
+    function('{}Starting {} at {}.'.format(pre, name, start))
     yield start
     end = time()
     function('{}Finished {} in {} seconds'.format(pre, name, str(end-start)))
@@ -70,21 +71,6 @@ def timer(name='task', pre="", function=logger.info, to_object=None):
         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 = {}
-            if 'state' in d:
-                state = deepcopy(d['state'])
-            return d['agent_type'], state
-
-    raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
-
-
 def repr(v):
     if isinstance(v, bool):
         v = "true" if v else ""