Tag version 0.14.0

1.3.0rc1 is not compatible with salib

.gitlab-ci.yml

@@ -1,6 +1,6 @@
 stages:
-  - build
   - test
+  - build
 
 build:
   stage: build
@@ -11,12 +11,15 @@ build:
     - docker
   script:
     - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
-    - /kaniko/executor --context $CI_PROJECT_DIR --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
+    # The skip-tls-verify flag is there because our registry certificate is self signed
+    - /kaniko/executor --context $CI_PROJECT_DIR --skip-tls-verify --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
   only:
     - tags
 
 
 test:
+  except:
+    - tags  # Avoid running tests for tags, because they are already run for the branch
   tags:
     - docker
   image: python:3.7

CHANGELOG.md

@@ -0,0 +1,49 @@
+# Changelog
+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).
+
+## [0.14.0]
+### Added
+* Loading configuration from template definitions in the yaml, in preparation for SALib support.
+The definition of the variables and their possible values (i.e., a problem in SALib terms), as well as a sampler function, can be provided.
+Soil uses this definition and the template to generate a set of configurations.
+* Simulation group names, to link related simulations. For now, they are only used to group all simulations in the same group under the same folder.
+* Exporters unify exporting/dumping results and other files to disk. If `dry_run` is set to `True`, exporters will write to stdout instead of a file (useful for testing/debugging).
+* Distribution exporter, to write statistics about values and value_counts in every simulation. The results are dumped to two CSV files.
+
+### Changed
+* `dir_path` is now the directory for resources (modules, files)
+* Environments and simulations do not export or write anything by default. That task is delegated to Exporters
+
+### Removed
+* The output dir for environments and simulations (see Exporters)
+* DrawingAgent, because it wrote to disk and was not being used. We provide a partial alternative in the form of the GraphDrawing exporter. A complete alternative will be provided once the network at each state can be accessed by exporters.
+
+## Fixed
+* Modules with custom agents/environments failed to load when they were run from outside the directory of the definition file. Modules are now loaded from the directory of the simulation file in addition to the working directory
+* Memory databases (in history) can now be shared between threads.
+* Testing all examples, not just subdirectories
+
+## [0.13.8]
+### Changed
+* Moved TerroristNetworkModel to examples
+### Added
+* `get_agents` and `count_agents` methods now accept lists as inputs. They can be used to retrieve agents from node ids
+* `subgraph` in BaseAgent
+* `agents.select` method, to filter out agents
+* `skip_test` property in yaml definitions, to force skipping some examples
+* `agents.Geo`, with a search function based on postition
+* `BaseAgent.ego_search` to get nodes from the ego network of a node
+* `BaseAgent.degree` and `BaseAgent.betweenness`
+### Fixed
+
+## [0.13.7]
+### Changed
+* History now defaults to not backing up! This makes it more intuitive to load the history for examination, at the expense of rewriting something. That should not happen because History is only created in the Environment, and that has `backup=True`.
+### Added
+* Agent names are assigned based on their agent types
+* Agent logging uses the agent name.
+* FSM agents can now return a timeout in addition to a new state. e.g. `return self.idle, self.env.timeout(2)` will execute the *different_state* in 2 *units of time* (`t_step=now+2`).
+* Example of using timeouts in FSM (custom_timeouts)
+* `network_agents` entries may include an `ids` entry. If set, it should be a list of node ids that should be assigned that agent type. This complements the previous behavior of setting agent type with `weights`.

MANIFEST.in

@@ -1,4 +1,7 @@
 include requirements.txt
 include test-requirements.txt
 include README.rst
-graft soil
+graft soil
+global-exclude __pycache__
+global-exclude soil_output
+global-exclude *.py[co]

Makefile

@@ -1,4 +1,7 @@
-test:
+quick-test:
 	docker-compose exec dev python -m pytest -s -v
 
-.PHONY: test
+test:
+	docker run -t -v $$PWD:/usr/src/app -w /usr/src/app python:3.7 python setup.py test
+
+.PHONY: test

docs/soil_tutorial.rst

@@ -26,7 +26,7 @@ But before that, let's import the soil module and networkx.
     %autoreload 2
     
     %pylab inline
-    # To display plots in the notebooed_
+    # To display plots in the notebook_
 
 
 .. parsed-literal::
@@ -2531,7 +2531,7 @@ Dealing with bigger data
 
 .. parsed-literal::
 
-    267M	../rabbits/soil_output/rabbits_example/
+    267M	../rabbits/soil_output/rabbits_example/
 
 
 If we tried to load the entire history, we would probably run out of

examples/complete.yml

@@ -1,12 +1,11 @@
 ---
 name: simple
+group: tests
 dir_path: "/tmp/"
 num_trials: 3
-dry_run: True
 max_time: 100
 interval: 1
 seed: "CompleteSeed!"
-dump: false
 network_params:
   generator: complete_graph
   n: 10

examples/custom_generator/custom_generator.yml

@@ -0,0 +1,16 @@
+---
+name: custom-generator
+description: Using a custom generator for the network
+num_trials: 3
+max_time: 100
+interval: 1
+network_params:
+  generator: mymodule.mygenerator
+# These are custom parameters
+  n: 10
+  n_edges: 5  
+network_agents:
+  - agent_type: CounterModel
+    weight: 1
+    state:
+      id: 0

examples/custom_generator/mymodule.py

@@ -0,0 +1,27 @@
+from networkx import Graph
+import networkx as nx
+from random import choice
+
+def mygenerator(n=5, n_edges=5):
+    '''
+    Just a simple generator that creates a network with n nodes and
+    n_edges edges. Edges are assigned randomly, only avoiding self loops.
+    '''
+    G = nx.Graph()
+
+    for i in range(n):
+        G.add_node(i)
+    
+    for i in range(n_edges):
+        nodes = list(G.nodes)
+        n_in = choice(nodes)
+        nodes.remove(n_in)  # Avoid loops
+        n_out = choice(nodes)
+        G.add_edge(n_in, n_out)
+    return G
+    
+
+
+
+
+    

examples/custom_timeouts/custom_timeouts.py

@@ -0,0 +1,35 @@
+from soil.agents import FSM, state, default_state
+
+
+class Fibonacci(FSM):
+    '''Agent that only executes in t_steps that are Fibonacci numbers'''
+
+    defaults = {
+        'prev': 1
+    }
+
+    @default_state
+    @state
+    def counting(self):
+        self.log('Stopping at {}'.format(self.now))
+        prev, self['prev'] = self['prev'], max([self.now, self['prev']])
+        return None, self.env.timeout(prev)
+
+class Odds(FSM):
+    '''Agent that only executes in odd t_steps'''
+    @default_state
+    @state
+    def odds(self):
+        self.log('Stopping at {}'.format(self.now))
+        return None, self.env.timeout(1+self.now%2)
+
+if __name__ == '__main__':
+    import logging
+    logging.basicConfig(level=logging.INFO)
+    from soil import Simulation
+    s = Simulation(network_agents=[{'ids': [0], 'agent_type': Fibonacci},
+                                   {'ids': [1], 'agent_type': Odds}],
+                   network_params={"generator": "complete_graph", "n": 2},
+                   max_time=100,
+                   )
+    s.run(dry_run=True)

examples/newsspread/NewsSpread.yml

@@ -6,7 +6,7 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_all_dumb
 network_agents:
 - agent_type: DumbViewer
@@ -30,7 +30,7 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_half_herd
 network_agents:
 - agent_type: DumbViewer
@@ -62,7 +62,7 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_all_herd
 network_agents:
 - agent_type: HerdViewer
@@ -89,7 +89,7 @@ environment_params:
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_wise_herd
 network_agents:
 - agent_type: HerdViewer
@@ -115,7 +115,7 @@ environment_params:
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_all_wise
 network_agents:
 - agent_type: WiseViewer

examples/programmatic/.gitignore

@@ -0,0 +1 @@
+Programmatic*

examples/programmatic/programmatic.py

@@ -0,0 +1,38 @@
+'''
+Example of a fully programmatic simulation, without definition files.
+'''
+from soil import Simulation, agents
+from networkx import Graph
+import logging
+
+
+def mygenerator():
+    # Add only a node
+    G = Graph()
+    G.add_node(1)
+    return G
+
+
+class MyAgent(agents.FSM):
+
+    @agents.default_state
+    @agents.state
+    def neutral(self):
+        self.info('I am running')
+
+
+s = Simulation(name='Programmatic',
+               network_params={'generator': mygenerator},
+               num_trials=1,
+               max_time=100,
+               agent_type=MyAgent,
+               dry_run=True)
+
+
+logging.basicConfig(level=logging.INFO)
+envs = s.run()
+
+s.dump_yaml()
+
+for env in envs:
+    env.dump_csv()

examples/template.yml

@@ -0,0 +1,29 @@
+---
+vars:
+  bounds:
+    x1: [0, 1]
+    x2: [1, 2]
+  fixed:
+    x3: ["a", "b", "c"]
+sampler: "SALib.sample.morris.sample"
+samples: 10
+template: |
+  group: simple
+  num_trials: 1
+  interval: 1
+  max_time: 2
+  seed: "CompleteSeed!"
+  dump: false
+  network_params:
+    generator: complete_graph
+    n: 10
+  network_agents:
+    - agent_type: CounterModel
+      weight: {{ x1 }}
+      state:
+        id: 0
+    - agent_type: AggregatedCounter
+      weight: {{ 1 - x1 }}
+  environment_params:
+    name: {{ x3 }}
+  skip_test: true

examples/terrorism/TerroristNetworkModel.py

@@ -0,0 +1,208 @@
+import random
+import networkx as nx
+from soil.agents import Geo, NetworkAgent, FSM, state, default_state
+from soil import Environment
+
+
+class TerroristSpreadModel(FSM, Geo):
+    """
+    Settings:
+        information_spread_intensity
+
+        terrorist_additional_influence
+
+        min_vulnerability (optional else zero)
+
+        max_vulnerability
+
+        prob_interaction
+    """
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+
+        self.information_spread_intensity = environment.environment_params['information_spread_intensity']
+        self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
+        self.prob_interaction = environment.environment_params['prob_interaction']
+
+        if self['id'] == self.civilian.id:       # Civilian
+            self.mean_belief = random.uniform(0.00, 0.5)
+        elif self['id'] == self.terrorist.id:     # Terrorist
+            self.mean_belief = random.uniform(0.8, 1.00)
+        elif self['id'] == self.leader.id:     # Leader
+            self.mean_belief = 1.00
+        else:
+            raise Exception('Invalid state id: {}'.format(self['id']))
+
+        if 'min_vulnerability' in environment.environment_params:
+            self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
+        else :
+            self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
+
+
+    @state
+    def civilian(self):
+        neighbours = list(self.get_neighboring_agents(agent_type=TerroristSpreadModel))
+        if len(neighbours) > 0:
+            # Only interact with some of the neighbors
+            interactions = list(n for n in neighbours if random.random() <= self.prob_interaction)
+            influence = sum( self.degree(i) for i in interactions )
+            mean_belief = sum( i.mean_belief * self.degree(i) / influence for i in interactions )
+            mean_belief = mean_belief * self.information_spread_intensity + self.mean_belief * ( 1 - self.information_spread_intensity )
+            self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
+        
+        if self.mean_belief >= 0.8:
+            return self.terrorist
+
+    @state
+    def leader(self):
+        self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
+        for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
+            if self.betweenness(neighbour) > self.betweenness(self):
+                return self.terrorist
+
+    @state
+    def terrorist(self):
+        neighbours = self.get_agents(state_id=[self.terrorist.id, self.leader.id],
+                                     agent_type=TerroristSpreadModel,
+                                     limit_neighbors=True)
+        if len(neighbours) > 0:
+            influence = sum( self.degree(n) for n in neighbours )
+            mean_belief = sum( n.mean_belief * self.degree(n) / influence for n in neighbours )
+            mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
+            self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
+
+        # Check if there are any leaders in the group
+        leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
+        if not leaders:
+            # Check if this is the potential leader
+            # Stop once it's found. Otherwise, set self as leader
+            for neighbour in neighbours:
+                if self.betweenness(self) < self.betweenness(neighbour):
+                    return
+            return self.leader
+
+
+class TrainingAreaModel(FSM, Geo):
+    """
+    Settings:
+        training_influence
+
+        min_vulnerability
+
+    Requires TerroristSpreadModel.
+    """
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.training_influence = environment.environment_params['training_influence']
+        if 'min_vulnerability' in environment.environment_params:
+            self.min_vulnerability = environment.environment_params['min_vulnerability']
+        else: self.min_vulnerability = 0
+
+    @default_state
+    @state
+    def terrorist(self):
+        for neighbour in self.get_neighboring_agents(agent_type=TerroristSpreadModel):
+            if neighbour.vulnerability > self.min_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )        
+
+
+class HavenModel(FSM, Geo):
+    """
+    Settings:
+        haven_influence
+
+        min_vulnerability
+
+        max_vulnerability
+
+    Requires TerroristSpreadModel.
+    """
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.haven_influence = environment.environment_params['haven_influence']
+        if 'min_vulnerability' in environment.environment_params:
+            self.min_vulnerability = environment.environment_params['min_vulnerability']
+        else: self.min_vulnerability = 0
+        self.max_vulnerability = environment.environment_params['max_vulnerability']
+
+    def get_occupants(self, **kwargs):
+        return self.get_neighboring_agents(agent_type=TerroristSpreadModel, **kwargs)
+
+    @state
+    def civilian(self):
+        civilians = self.get_occupants(state_id=self.civilian.id)
+        if not civilians:
+            return self.terrorist
+
+        for neighbour in self.get_occupants():
+            if neighbour.vulnerability > self.min_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
+        return self.civilian
+
+    @state
+    def terrorist(self):
+        for neighbour in self.get_occupants():
+            if neighbour.vulnerability < self.max_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
+        return self.terrorist
+
+
+class TerroristNetworkModel(TerroristSpreadModel):
+    """
+    Settings:
+        sphere_influence
+
+        vision_range
+
+        weight_social_distance
+
+        weight_link_distance
+    """
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+
+        self.vision_range = environment.environment_params['vision_range']
+        self.sphere_influence = environment.environment_params['sphere_influence']
+        self.weight_social_distance = environment.environment_params['weight_social_distance']
+        self.weight_link_distance = environment.environment_params['weight_link_distance']
+
+    @state
+    def terrorist(self):
+        self.update_relationships()
+        return super().terrorist()
+
+    @state
+    def leader(self):
+        self.update_relationships()
+        return super().leader()
+
+    def update_relationships(self):
+        if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
+            close_ups = set(self.geo_search(radius=self.vision_range, agent_type=TerroristNetworkModel))
+            step_neighbours = set(self.ego_search(self.sphere_influence, agent_type=TerroristNetworkModel, center=False))
+            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_type=TerroristNetworkModel))
+            search = (close_ups | step_neighbours) - neighbours
+            for agent in self.get_agents(search):
+                social_distance = 1 / self.shortest_path_length(agent.id)
+                spatial_proximity = ( 1 - self.get_distance(agent.id) )
+                prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
+                if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
+                    self.add_edge(agent)
+                    break
+
+    def get_distance(self, target):
+        source_x, source_y = nx.get_node_attributes(self.global_topology, 'pos')[self.id]
+        target_x, target_y = nx.get_node_attributes(self.global_topology, 'pos')[target]
+        dx = abs( source_x - target_x )
+        dy = abs( source_y - target_y )
+        return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
+
+    def shortest_path_length(self, target):
+        try:
+            return nx.shortest_path_length(self.global_topology, self.id, target)
+        except nx.NetworkXNoPath:
+            return float('inf')

examples/terrorism/TerroristNetworkModel.yml

@@ -60,3 +60,4 @@ visualization_params:
   background_image: 'map_4800x2860.jpg'
   background_opacity: '0.9'
   background_filter_color: 'blue'
+skip_test: true  # This simulation takes too long for automated tests.

requirements.txt

@@ -1,7 +1,10 @@
-nxsim
+nxsim>=0.1.2
 simpy
 networkx>=2.0
 numpy
 matplotlib
-pyyaml
-pandas
+pyyaml>=5.1
+pandas>=0.23
+scipy==1.2.1 # scipy 1.3.0rc1 is not compatible with salib
+SALib>=1.3
+Jinja2

soil/VERSION

@@ -1 +1 @@
-0.13.4
+0.14.0

soil/__init__.py

@@ -14,7 +14,8 @@ except NameError:
 from . import agents
 from .simulation import *
 from .environment import Environment
-from . import utils
+from .history import History
+from . import serialization
 from . import analysis
 
 def main():
@@ -43,6 +44,8 @@ def main():
                         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.')
+    parser.add_argument('-e', '--exporter', action='append',
+                        help='Export environment and/or simulations using this exporter')
 
     args = parser.parse_args()
 
@@ -54,17 +57,20 @@ def main():
     logging.info('Loading config file: {}'.format(args.file))
 
     try:
-        dump = []
-        if not args.dry_run:
-            if args.csv:
-                dump.append('csv')
-            if args.graph:
-                dump.append('gexf')
+        exporters = list(args.exporter or [])
+        if args.csv:
+            exporters.append('CSV')
+        if args.graph:
+            exporters.append('Gexf')
+        exp_params = {}
+        if args.dry_run:
+            exp_params['copy_to'] = sys.stdout
         simulation.run_from_config(args.file,
                                    dry_run=args.dry_run,
-                                   dump=dump,
+                                   exporters=exporters,
                                    parallel=(not args.synchronous),
-                                   results_dir=args.output)
+                                   outdir=args.output,
+                                   exporter_params=exp_params)
     except Exception:
         if args.pdb:
             pdb.post_mortem()

soil/agents/CounterModel.py

@@ -22,11 +22,17 @@ class AggregatedCounter(BaseAgent):
     in each step and adds it to its state.
     """
 
+    defaults = {
+        'times': 0,
+        'neighbors': 0,
+        'total': 0
+    }
+
     def step(self):
         # Outside effects
-        total = len(list(self.get_all_agents()))
+        self['times'] += 1
         neighbors = len(list(self.get_neighboring_agents()))
-        self['times'] = self.get('times', 0) + 1
-        self['neighbors'] = self.get('neighbors', 0) + neighbors
-        self['total'] = total = self.get('total', 0) + total
+        self['neighbors'] += neighbors
+        total = len(list(self.get_all_agents()))
+        self['total'] += total
         self.debug('Running for step: {}. Total: {}'.format(self.now, total))

soil/agents/DrawingAgent.py

@@ -1,18 +0,0 @@
-from . import BaseAgent
-
-import os.path
-import matplotlib
-import matplotlib.pyplot as plt
-import networkx as nx
-
-
-class DrawingAgent(BaseAgent):
-    """
-    Agent that draws the state of the network.
-    """
-
-    def step(self):
-        # 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.get_path(), "graph-"+str(self.env.now)+".png"))

soil/agents/__init__.py

@@ -10,11 +10,18 @@ import logging
 from collections import OrderedDict
 from copy import deepcopy
 from functools import partial
+from scipy.spatial import cKDTree as KDTree
 import json
 
 from functools import wraps
 
-from .. import utils, history
+from .. import serialization, history
+
+
+def as_node(agent):
+    if isinstance(agent, BaseAgent):
+        return agent.id
+    return agent
 
 
 class BaseAgent(nxsim.BaseAgent):
@@ -25,13 +32,13 @@ class BaseAgent(nxsim.BaseAgent):
     defaults = {}
 
     def __init__(self, environment, agent_id, state=None,
-                 name='network_process', interval=None, **state_params):
+                 name=None, interval=None, **state_params):
         # 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
-        self.name = name
+        self.name = name or '{}[{}]'.format(type(self).__name__, self.id)
         self.state_params = state_params
 
         # Register agent to environment
@@ -46,8 +53,7 @@ class BaseAgent(nxsim.BaseAgent):
 
         if not hasattr(self, 'level'):
             self.level = logging.DEBUG
-        self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name,
-                                                             self.id))
+        self.logger = logging.getLogger(self.env.name)
         self.logger.setLevel(self.level)
 
         # initialize every time an instance of the agent is created
@@ -134,47 +140,25 @@ class BaseAgent(nxsim.BaseAgent):
     def step(self):
         pass
 
-    def count_agents(self, state_id=None, limit_neighbors=False):
+    def count_agents(self, **kwargs):
+        return len(list(self.get_agents(**kwargs)))
+
+    def count_neighboring_agents(self, state_id=None, **kwargs):
+        return len(super().get_neighboring_agents(state_id=state_id, **kwargs))
+
+    def get_neighboring_agents(self, state_id=None, **kwargs):
+        return self.get_agents(limit_neighbors=True, state_id=state_id, **kwargs)
+
+    def get_agents(self, agents=None, limit_neighbors=False, **kwargs):
         if limit_neighbors:
-            agents = self.global_topology.neighbors(self.id)
+            agents = super().get_agents(limit_neighbors=limit_neighbors)
         else:
-            agents = self.global_topology.nodes()
-        count = 0
-        for agent in agents:
-            if state_id and state_id != self.global_topology.node[agent]['agent']['id']:
-                continue
-            count += 1
-        return count
-
-    def count_neighboring_agents(self, state_id=None):
-        return len(super().get_agents(state_id, limit_neighbors=True))
-
-    def get_agents(self, state_id=None, agent_type=None, limit_neighbors=False, iterator=False, **kwargs):
-        agents = self.env.agents
-        if limit_neighbors:
-            agents = super().get_agents(state_id, limit_neighbors)
-
-        def matches_all(agent):
-            if state_id is not None:
-                if agent.state.get('id', None) != state_id:
-                    return False
-            if agent_type is not None:
-                if type(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 iterator:
-            return f
-        return list(f)
+            agents = self.env.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 = "\t@{:>5}:\t{}".format(self.now, message)
+        message = "\t{:10}@{:>5}:\t{}".format(self.name, self.now, message)
         for k, v in kwargs:
             message += " {k}={v} ".format(k, v)
         extra = {}
@@ -208,31 +192,76 @@ class BaseAgent(nxsim.BaseAgent):
         self._state = state['_state']
         self.env = state['environment']
 
+    def add_edge(self, node1, node2, **attrs):
+        node1 = as_node(node1)
+        node2 = as_node(node2)
 
-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.
-    '''
+        for n in [node1, node2]:
+            if n not in self.global_topology.nodes(data=False):
+                raise ValueError('"{}" not in the graph'.format(n))
+        return self.global_topology.add_edge(node1, node2, **attrs)
 
-    @wraps(func)
-    def func_wrapper(self):
-        next_state = func(self)
-        when = None
-        if next_state is None:
+    def subgraph(self, center=True, **kwargs):
+        include = [self] if center else []
+        return self.global_topology.subgraph(n.id for n in self.get_agents(**kwargs)+include)
+
+
+class NetworkAgent(BaseAgent):
+
+    def add_edge(self, other, **kwargs):
+        return super(NetworkAgent, self).add_edge(node1=self.id, node2=other, **kwargs)
+
+    def ego_search(self, steps=1, center=False, node=None, **kwargs):
+        '''Get a list of nodes in the ego network of *node* of radius *steps*'''
+        node = as_node(node if node is not None else self)
+        G = self.subgraph(**kwargs)
+        return nx.ego_graph(G, node, center=center, radius=steps).nodes()
+
+    def degree(self, node, force=False):
+        node = as_node(node)
+        if force or (not hasattr(self.env, '_degree')) or getattr(self.env, '_last_step', 0) < self.now:
+            self.env._degree = nx.degree_centrality(self.global_topology)
+            self.env._last_step = self.now
+        return self.env._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:
+            self.env._betweenness = nx.betweenness_centrality(self.global_topology)
+            self.env._last_step = self.now
+        return self.env._betweenness[node]
+
+
+def state(name=None):
+    def decorator(func, name=None):
+        '''
+        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 environment.
+        '''
+
+        @wraps(func)
+        def func_wrapper(self):
+            next_state = func(self)
+            when = None
+            if next_state is None:
+                return when
+            try:
+                next_state, when = next_state
+            except (ValueError, TypeError):
+                pass
+            if next_state:
+                self.set_state(next_state)
             return when
-        try:
-            next_state, when = next_state
-        except (ValueError, TypeError):
-            pass
-        if next_state:
-            self.set_state(next_state)
-        return when
 
-    func_wrapper.id = func.__name__
-    func_wrapper.is_default = False
-    return func_wrapper
+        func_wrapper.id = name or func.__name__
+        func_wrapper.is_default = False
+        return func_wrapper
+
+    if callable(name):
+        return decorator(name)
+    else:
+        return partial(decorator, name=name)
 
 
 def default_state(func):
@@ -280,7 +309,7 @@ class FSM(BaseAgent, metaclass=MetaFSM):
             raise Exception('{} has no valid state id or default state'.format(self))
         if next_state not in self.states:
             raise Exception('{} is not a valid id for {}'.format(next_state, self))
-        self.states[next_state](self)
+        return self.states[next_state](self)
 
     def set_state(self, state):
         if hasattr(state, 'id'):
@@ -306,6 +335,9 @@ def prob(prob=1):
     return r < prob
 
 
+STATIC_THRESHOLD = (-1, -1)
+
+
 def calculate_distribution(network_agents=None,
                            agent_type=None):
     '''
@@ -337,12 +369,15 @@ def calculate_distribution(network_agents=None,
     elif agent_type:
         network_agents = [{'agent_type': agent_type}]
     else:
-        return []
+        raise ValueError('Specify a distribution or a default agent type')
 
     # Calculate the thresholds
     total = sum(x.get('weight', 1) for x in network_agents)
     acc = 0
     for v in network_agents:
+        if 'ids' in v:
+            v['threshold'] = STATIC_THRESHOLD
+            continue
         upper = acc + (v.get('weight', 1)/total)
         v['threshold'] = [acc, upper]
         acc = upper
@@ -353,7 +388,7 @@ def serialize_type(agent_type, known_modules=[], **kwargs):
     if isinstance(agent_type, str):
         return agent_type
     known_modules += ['soil.agents']
-    return utils.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class
+    return serialization.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class
 
 
 def serialize_distribution(network_agents, known_modules=[]):
@@ -374,7 +409,7 @@ def deserialize_type(agent_type, known_modules=[]):
     if not isinstance(agent_type, str):
         return agent_type
     known = known_modules + ['soil.agents', 'soil.agents.custom' ]
-    agent_type = utils.deserializer(agent_type, known_modules=known)
+    agent_type = serialization.deserializer(agent_type, known_modules=known)
     return agent_type
 
 
@@ -403,21 +438,76 @@ def _convert_agent_types(ind, to_string=False, **kwargs):
     return deserialize_distribution(ind, **kwargs)
 
 
-def _agent_from_distribution(distribution, value=-1):
+def _agent_from_distribution(distribution, value=-1, agent_id=None):
     """Used in the initialization of agents given an agent distribution."""
     if value < 0:
         value = random.random()
-    for d in distribution:
+    for d in sorted(distribution, key=lambda x: x['threshold']):
         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
+        # 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 \
+                (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))
 
 
+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)]
+
+
+def select(agents, state_id=None, agent_type=None, ignore=None, iterator=False, **kwargs):
+
+    if state_id is not None:
+        try:
+            state_id = tuple(state_id)
+        except TypeError:
+            state_id = tuple([state_id])
+    if agent_type is not None:
+        try:
+            agent_type = tuple(agent_type)
+        except TypeError:
+            agent_type = tuple([agent_type])
+
+    def matches_all(agent):
+        if state_id is not None:
+            if agent.state.get('id', None) not in state_id:
+                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 iterator:
+        return f
+    return list(f)
+
+
 from .BassModel import *
 from .BigMarketModel import *
 from .IndependentCascadeModel import *
@@ -425,4 +515,3 @@ from .ModelM2 import *
 from .SentimentCorrelationModel import *
 from .SISaModel import *
 from .CounterModel import *
-from .DrawingAgent import *

soil/analysis.py

@@ -4,7 +4,7 @@ import glob
 import yaml
 from os.path import join
 
-from . import utils, history
+from . import serialization, history
 
 
 def read_data(*args, group=False, **kwargs):
@@ -34,7 +34,7 @@ def _read_data(pattern, *args, from_csv=False, process_args=None, **kwargs):
 
 
 def read_sql(db, *args, **kwargs):
-    h = history.History(db, backup=False)
+    h = history.History(db_path=db, backup=False)
     df = h.read_sql(*args, **kwargs)
     return df
 
@@ -56,7 +56,7 @@ def read_csv(filename, keys=None, convert_types=False, **kwargs):
 
 
 def convert_row(row):
-    row['value'] = utils.deserialize(row['value_type'], row['value'])
+    row['value'] = serialization.deserialize(row['value_type'], row['value'])
     return row
 
 

soil/environment.py

@@ -4,23 +4,23 @@ import time
 import csv
 import random
 import simpy
+import yaml
 import tempfile
 import pandas as pd
 from copy import deepcopy
+from collections import Counter
 from networkx.readwrite import json_graph
 
 import networkx as nx
 import nxsim
 
-from . import utils, agents, analysis, history
+from . import serialization, agents, analysis, history, utils
 
 # These properties will be copied when pickling/unpickling the environment
 _CONFIG_PROPS = [ 'name',
                  'states',
                  'default_state',
                  'interval',
-                 'dry_run',
-                 'dir_path',
                  ]
 
 class Environment(nxsim.NetworkEnvironment):
@@ -41,8 +41,6 @@ class Environment(nxsim.NetworkEnvironment):
                  default_state=None,
                  interval=1,
                  seed=None,
-                 dry_run=False,
-                 dir_path=None,
                  topology=None,
                  *args, **kwargs):
         self.name = name or 'UnnamedEnvironment'
@@ -54,13 +52,9 @@ class Environment(nxsim.NetworkEnvironment):
             topology = nx.Graph()
         super().__init__(*args, topology=topology, **kwargs)
         self._env_agents = {}
-        self.dry_run = dry_run
         self.interval = interval
-        self.dir_path = dir_path or tempfile.mkdtemp('soil-env')
-        if not dry_run:
-            self.get_path()
-        self._history = history.History(name=self.name if not dry_run else None,
-                                        dir_path=self.dir_path)
+        self._history = history.History(name=self.name,
+                                        backup=True)
         # Add environment agents first, so their events get
         # executed before network agents
         self.environment_agents = environment_agents or []
@@ -99,8 +93,7 @@ class Environment(nxsim.NetworkEnvironment):
 
     @network_agents.setter
     def network_agents(self, network_agents):
-        if not network_agents:
-            return
+        self._network_agents = network_agents
         for ix in self.G.nodes():
             self.init_agent(ix, agent_distribution=network_agents)
 
@@ -111,7 +104,7 @@ class Environment(nxsim.NetworkEnvironment):
 
         agent_type = None
         if 'agent_type' in self.states.get(agent_id, {}):
-            agent_type = self.states[agent_id]
+            agent_type = self.states[agent_id]['agent_type']
         elif 'agent_type' in node:
             agent_type = node['agent_type']
         elif 'agent_type' in self.default_state:
@@ -119,8 +112,11 @@ class Environment(nxsim.NetworkEnvironment):
 
         if agent_type:
             agent_type = agents.deserialize_type(agent_type)
+        elif agent_distribution:
+            agent_type, state = agents._agent_from_distribution(agent_distribution, agent_id=agent_id)
         else:
-            agent_type, state = agents._agent_from_distribution(agent_distribution)
+            serialization.logger.debug('Skipping node {}'.format(agent_id))
+            return
         return self.set_agent(agent_id, agent_type, state)
 
     def set_agent(self, agent_id, agent_type, state=None):
@@ -130,10 +126,12 @@ class Environment(nxsim.NetworkEnvironment):
         defstate.update(node.get('state', {}))
         if state:
             defstate.update(state)
-        state = defstate
-        a = agent_type(environment=self,
-                       agent_id=agent_id,
-                       state=state)
+        a = None
+        if agent_type:
+            state = defstate
+            a = agent_type(environment=self,
+                           agent_id=agent_id,
+                           state=state)
         node['agent'] = a
         return a
 
@@ -144,22 +142,23 @@ class Environment(nxsim.NetworkEnvironment):
         a['visible'] = True
         return a
 
-    def add_edge(self, agent1, agent2, attrs=None):
+    def add_edge(self, agent1, agent2, start=None, **attrs):
         if hasattr(agent1, 'id'):
             agent1 = agent1.id
         if hasattr(agent2, 'id'):
             agent2 = agent2.id
-        return self.G.add_edge(agent1, agent2)
+        start = start or self.now
+        return self.G.add_edge(agent1, agent2, **attrs)
 
     def run(self, *args, **kwargs):
         self._save_state()
+        self.log_stats()
         super().run(*args, **kwargs)
         self._history.flush_cache()
+        self.log_stats()
 
     def _save_state(self, now=None):
-        # for agent in self.agents:
-        #     agent.save_state()
-        utils.logger.debug('Saving state @{}'.format(self.now))
+        serialization.logger.debug('Saving state @{}'.format(self.now))
         self._history.save_records(self.state_to_tuples(now=now))
 
     def save_state(self):
@@ -170,7 +169,7 @@ class Environment(nxsim.NetworkEnvironment):
         self._save_state()
         while self.peek() != simpy.core.Infinity:
             delay = max(self.peek() - self.now, self.interval)
-            utils.logger.debug('Step: {}'.format(self.now))
+            serialization.logger.debug('Step: {}'.format(self.now))
             ev = self.event()
             ev._ok = True
             # Schedule the event with minimum priority so
@@ -212,35 +211,23 @@ class Environment(nxsim.NetworkEnvironment):
         '''
         return self[key] if key in self else default
 
-    def get_path(self, dir_path=None):
-        dir_path = dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            try:
-                os.makedirs(dir_path)
-            except FileExistsError:
-                pass
-        return dir_path
-
     def get_agent(self, agent_id):
         return self.G.node[agent_id]['agent']
 
-    def get_agents(self):
-        return list(self.agents)
+    def get_agents(self, nodes=None):
+        if nodes is None:
+            return list(self.agents)
+        return [self.G.node[i]['agent'] for i in nodes]
 
-    def dump_csv(self, dir_path=None):
-        csv_name = os.path.join(self.get_path(dir_path),
-                                '{}.environment.csv'.format(self.name))
-
-        with open(csv_name, 'w') as f:
+    def dump_csv(self, f):
+        with utils.open_or_reuse(f, 'w') as f:
             cr = csv.writer(f)
             cr.writerow(('agent_id', 't_step', 'key', 'value'))
             for i in self.history_to_tuples():
                 cr.writerow(i)
 
-    def dump_gexf(self, dir_path=None):
+    def dump_gexf(self, f):
         G = self.history_to_graph()
-        graph_path = os.path.join(self.get_path(dir_path),
-                                  self.name+".gexf")
         # Workaround for geometric models
         # See soil/soil#4
         for node in G.nodes():
@@ -248,9 +235,9 @@ class Environment(nxsim.NetworkEnvironment):
                 G.node[node]['viz'] = {"position": {"x": G.node[node]['pos'][0], "y": G.node[node]['pos'][1], "z": 0.0}}
                 del (G.node[node]['pos'])
 
-        nx.write_gexf(G, graph_path, version="1.2draft")
+        nx.write_gexf(G, f, version="1.2draft")
 
-    def dump(self, dir_path=None, formats=None):
+    def dump(self, *args, formats=None, **kwargs):
         if not formats:
             return
         functions = {
@@ -259,10 +246,13 @@ class Environment(nxsim.NetworkEnvironment):
         }
         for f in formats:
             if f in functions:
-                functions[f](dir_path)
+                functions[f](*args, **kwargs)
             else:
                 raise ValueError('Unknown format: {}'.format(f))
 
+    def dump_sqlite(self, f):
+        return self._history.dump(f)
+
     def state_to_tuples(self, now=None):
         if now is None:
             now = self.now
@@ -326,6 +316,25 @@ class Environment(nxsim.NetworkEnvironment):
                 G.add_node(agent.id, **attributes)
 
         return G
+
+    def stats(self):
+        stats = {}
+        stats['network'] = {}
+        stats['network']['n_nodes'] = self.G.number_of_nodes()
+        stats['network']['n_edges'] = self.G.number_of_edges()
+        c = Counter()
+        c.update(a.__class__.__name__ for a in self.network_agents)
+        stats['agents'] = {}
+        stats['agents']['model_count'] = dict(c)
+        c2 = Counter()
+        c2.update(a['id'] for a in self.network_agents)
+        stats['agents']['state_count'] = dict(c2)
+        stats['params'] = self.environment_params
+        return stats
+
+    def log_stats(self):
+        stats = self.stats()
+        serialization.logger.info('Environment stats: \n{}'.format(yaml.dump(stats, default_flow_style=False)))
     
     def __getstate__(self):
         state = {}

soil/exporters.py

@@ -0,0 +1,175 @@
+import os
+import time
+from io import BytesIO
+
+import matplotlib.pyplot as plt
+import networkx as nx
+import pandas as pd
+
+from .serialization import deserialize
+from .utils import open_or_reuse, logger, timer
+
+
+from . import utils
+
+
+def for_sim(simulation, names, *args, **kwargs):
+    '''Return the set of exporters for a simulation, given the exporter names'''
+    exporters = []
+    for name in names:
+        mod = deserialize(name, known_modules=['soil.exporters'])
+        exporters.append(mod(simulation, *args, **kwargs))
+    return exporters
+
+
+class DryRunner(BytesIO):
+    def __init__(self, fname, *args, copy_to=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.__fname = fname
+        self.__copy_to = copy_to
+
+    def write(self, txt):
+        if self.__copy_to:
+            self.__copy_to.write('{}:::{}'.format(self.__fname, txt))
+        try:
+            super().write(txt)
+        except TypeError:
+            super().write(bytes(txt, 'utf-8'))
+
+    def close(self):
+        logger.info('**Not** written to {} (dry run mode):\n\n{}\n\n'.format(self.__fname,
+                                                                       self.getvalue().decode()))
+        super().close()
+
+
+class Exporter:
+    '''
+    Interface for all exporters. It is not necessary, but it is useful
+    if you don't plan to implement all the methods.
+    '''
+
+    def __init__(self, simulation, outdir=None, dry_run=None, copy_to=None):
+        self.sim = simulation
+        outdir = outdir or os.getcwd()
+        self.outdir = os.path.join(outdir,
+                                   simulation.group or '',
+                                   simulation.name)
+        self.dry_run = dry_run
+        self.copy_to = copy_to
+
+    def start(self):
+        '''Method to call when the simulation starts'''
+
+    def end(self):
+        '''Method to call when the simulation ends'''
+
+    def trial_end(self, env):
+        '''Method to call when a trial ends'''
+
+    def output(self, f, mode='w', **kwargs):
+        if self.dry_run:
+            f = DryRunner(f, copy_to=self.copy_to)
+        else:
+            try:
+                if not os.path.isabs(f):
+                    f = os.path.join(self.outdir, f)
+            except TypeError:
+                pass
+        return open_or_reuse(f, mode=mode, **kwargs)
+
+
+class Default(Exporter):
+    '''Default exporter. Writes CSV and sqlite results, as well as the simulation YAML'''
+
+    def start(self):
+        if not self.dry_run:
+            logger.info('Dumping results to %s', self.outdir)
+            self.sim.dump_yaml(outdir=self.outdir)
+        else:
+            logger.info('NOT dumping results')
+
+    def trial_end(self, env):
+        if not self.dry_run:
+            with timer('Dumping simulation {} trial {}'.format(self.sim.name,
+                                                               env.name)):
+                with self.output('{}.sqlite'.format(env.name), mode='wb') as f:
+                    env.dump_sqlite(f)
+
+
+class CSV(Exporter):
+    def trial_end(self, env):
+        if not self.dry_run:
+            with timer('[CSV] Dumping simulation {} trial {}'.format(self.sim.name,
+                                                               env.name)):
+                with self.output('{}.csv'.format(env.name)) as f:
+                    env.dump_csv(f)
+
+
+class Gexf(Exporter):
+    def trial_end(self, env):
+        if not self.dry_run:
+            with timer('[CSV] Dumping simulation {} trial {}'.format(self.sim.name,
+                                                                     env.name)):
+                with self.output('{}.gexf'.format(env.name), mode='wb') as f:
+                    env.dump_gexf(f)
+
+
+class Dummy(Exporter):
+
+    def start(self):
+        with self.output('dummy', 'w') as f:
+            f.write('simulation started @ {}\n'.format(time.time()))
+
+    def trial_end(self, env):
+        with self.output('dummy', 'w') as f:
+            for i in env.history_to_tuples():
+                f.write(','.join(map(str, i)))
+                f.write('\n')
+
+    def end(self):
+        with self.output('dummy', 'a') as f:
+            f.write('simulation ended @ {}\n'.format(time.time()))
+
+
+class Distribution(Exporter):
+    '''
+    Write the distribution of agent states at the end of each trial,
+    the mean value, and its deviation.
+    '''
+
+    def start(self):
+        self.means = []
+        self.counts = []
+
+    def trial_end(self, env):
+        df = env[None, None, None].df()
+        ix = df.index[-1]
+        attrs = df.columns.levels[0]
+        vc = {}
+        stats = {}
+        for a in attrs:
+            t = df.loc[(ix, a)]
+            try:
+                self.means.append(('mean', a, t.mean()))
+            except TypeError:
+                for name, count in t.value_counts().iteritems():
+                    self.counts.append(('count', a, name, count))
+
+    def end(self):
+        dfm = pd.DataFrame(self.means, columns=['metric', 'key', 'value'])
+        dfc = pd.DataFrame(self.counts, columns=['metric', 'key', 'value', 'count'])
+        dfm = dfm.groupby(by=['key']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
+        dfc = dfc.groupby(by=['key', 'value']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
+        with self.output('counts.csv') as f:
+            dfc.to_csv(f)
+        with self.output('metrics.csv') as f:
+            dfm.to_csv(f)
+
+class GraphDrawing(Exporter):
+
+    def trial_end(self, env):
+        # Outside effects
+        f = plt.figure()
+        nx.draw(env.G, node_size=10, width=0.2, pos=nx.spring_layout(env.G, scale=100), ax=f.add_subplot(111))
+        with open('graph-{}.png'.format(env.name)) as f:
+            f.savefig(f)

soil/history.py

@@ -3,9 +3,14 @@ import os
 import pandas as pd
 import sqlite3
 import copy
+import logging
+import tempfile
+
+logger = logging.getLogger(__name__)
+
 from collections import UserDict, namedtuple
 
-from . import utils
+from . import serialization
 
 
 class History:
@@ -13,21 +18,24 @@ class History:
     Store and retrieve values from a sqlite database.
     """
 
-    def __init__(self, db_path=None, name=None, dir_path=None, backup=True):
-        if db_path is None and name:
-            db_path = os.path.join(dir_path or os.getcwd(),
-                                   '{}.db.sqlite'.format(name))
-        if db_path:
-            if backup and os.path.exists(db_path):
-                newname = db_path + '.backup{}.sqlite'.format(time.time())
-                os.rename(db_path, newname)
-        else:
-            db_path = ":memory:"
+    def __init__(self, name=None, db_path=None, backup=False):
+        self._db = None
+
+        if db_path is None:
+            if not name:
+                name = time.time()
+            _, db_path = tempfile.mkstemp(suffix='{}.sqlite'.format(name))
+
+        if backup and os.path.exists(db_path):
+            newname = db_path + '.backup{}.sqlite'.format(time.time())
+            os.rename(db_path, newname)
+
         self.db_path = db_path
 
         self.db = db_path
 
         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 text)''')
             self.db.execute('''CREATE TABLE IF NOT EXISTS value_types (key text, value_type text)''')
             self.db.execute('''CREATE UNIQUE INDEX IF NOT EXISTS idx_history ON history (agent_id, t_step, key);''')
@@ -44,12 +52,21 @@ class History:
 
     @db.setter
     def db(self, db_path=None):
+        self._close()
         db_path = db_path or self.db_path
         if isinstance(db_path, str):
+            logger.debug('Connecting to database {}'.format(db_path))
             self._db = sqlite3.connect(db_path)
         else:
             self._db = db_path
 
+    def _close(self):
+        if self._db is None:
+            return
+        self.flush_cache()
+        self._db.close()
+        self._db = None
+
     @property
     def dtypes(self):
         self.read_types()
@@ -88,9 +105,9 @@ class History:
         if key not in self._dtypes:
             self.read_types()
             if key not in self._dtypes:
-                name = utils.name(value)
-                serializer = utils.serializer(name)
-                deserializer = utils.deserializer(name)
+                name = serialization.name(value)
+                serializer = serialization.serializer(name)
+                deserializer = serialization.deserializer(name)
                 self._dtypes[key] = (name, serializer, deserializer)
                 with self.db:
                     self.db.execute("replace into value_types (key, value_type) values (?, ?)", (key, name))
@@ -104,12 +121,12 @@ class History:
             raise ValueError("Unknown datatype for {} and {}".format(key, value))
         return self._dtypes[key][2](value)
 
-
     def flush_cache(self):
         '''
         Use a cache to save state changes to avoid opening a session for every change.
         The cache will be flushed at the end of the simulation, and when history is accessed.
         '''
+        logger.debug('Flushing cache {}'.format(self.db_path))
         with self.db:
             for rec in 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))
@@ -128,8 +145,8 @@ class History:
         with self.db:
             res = self.db.execute("select key, value_type from value_types ").fetchall()
         for k, v in res:
-            serializer = utils.serializer(v)
-            deserializer = utils.deserializer(v)
+            serializer = serialization.serializer(v)
+            deserializer = serialization.deserializer(v)
             self._dtypes[k] = (v, serializer, deserializer)
 
     def __getitem__(self, key):
@@ -147,8 +164,6 @@ class History:
             return r.value()
         return r
 
-
-
     def read_sql(self, keys=None, agent_ids=None, t_steps=None, convert_types=False, limit=-1):
 
         self.read_types()
@@ -207,16 +222,22 @@ class History:
         if t_steps:
             df_p = df_p.reindex(t_steps, method='ffill')
         return df_p.ffill()
-    
+
     def __getstate__(self):
         state = dict(**self.__dict__)
         del state['_db']
         del state['_dtypes']
         return state
-    
+
     def __setstate__(self, state):
         self.__dict__ = state
         self._dtypes = {}
+        self._db = None
+
+    def dump(self, f):
+        self._close()
+        for line in open(self.db_path, 'rb'):
+            f.write(line)
 
 
 class Records():
@@ -267,10 +288,13 @@ class Records():
                 i = self._df[f.key][str(f.agent_id)]
                 ix = i.index.get_loc(f.t_step, method='ffill')
                 return i.iloc[ix]
-            except KeyError:
+            except KeyError as ex:
                 return self.dtypes[f.key][2]()
         return list(self)
 
+    def df(self):
+        return self._df
+
     def __getitem__(self, k):
         n = copy.copy(self)
         n.filter(k)
@@ -286,6 +310,5 @@ class Records():
             return str(self.value())
         return '<Records for [{}]>'.format(self._filter)
 
-
 Key = namedtuple('Key', ['agent_id', 't_step', 'key'])
 Record = namedtuple('Record', 'agent_id t_step key value')

soil/serialization.py

@@ -0,0 +1,201 @@
+import os
+import logging
+import ast
+import sys
+import importlib
+from glob import glob
+from itertools import product, chain
+
+import yaml
+import networkx as nx
+
+from jinja2 import Template
+
+
+logger = logging.getLogger('soil')
+logger.setLevel(logging.INFO)
+
+
+def load_network(network_params, dir_path=None):
+    if network_params is None:
+        return nx.Graph()
+    path = network_params.get('path', None)
+    if path:
+        if dir_path and not os.path.isabs(path):
+            path = os.path.join(dir_path, path)
+        extension = os.path.splitext(path)[1][1:]
+        kwargs = {}
+        if extension == 'gexf':
+            kwargs['version'] = '1.2draft'
+            kwargs['node_type'] = int
+        try:
+            method = getattr(nx.readwrite, 'read_' + extension)
+        except AttributeError:
+            raise AttributeError('Unknown format')
+        return method(path, **kwargs)
+
+    net_args = network_params.copy()
+    if 'generator' not in net_args:
+        return nx.Graph()
+
+    net_gen = net_args.pop('generator')
+
+    if dir_path not in sys.path:
+        sys.path.append(dir_path)
+
+    method = deserializer(net_gen,
+                          known_modules=['networkx.generators',])
+
+    return method(**net_args)
+
+
+def load_file(infile):
+    with open(infile, 'r') as f:
+        return list(chain.from_iterable(map(expand_template, load_string(f))))
+
+
+def load_string(string):
+    yield from yaml.load_all(string, Loader=yaml.FullLoader)
+
+
+def expand_template(config):
+    if 'template' not in config:
+        yield config
+        return
+    if 'vars' not in config:
+        raise ValueError(('You must provide a definition of variables'
+                          ' for the template.'))
+
+    template = Template(config['template'])
+
+    sampler_name = config.get('sampler', 'SALib.sample.morris.sample')
+    n_samples = int(config.get('samples', 100))
+    sampler = deserializer(sampler_name)
+    bounds = config['vars']['bounds']
+
+    problem = {
+        'num_vars': len(bounds),
+        'names': list(bounds.keys()),
+        'bounds': list(v for v in bounds.values())
+    }
+    samples = sampler(problem, n_samples)
+
+    lists = config['vars'].get('lists', {})
+    names = list(lists.keys())
+    values = list(lists.values())
+    combs = list(product(*values))
+
+    allnames = names + problem['names']
+    allvalues = [(list(i[0])+list(i[1])) for i in product(combs, samples)]
+    params = list(map(lambda x: dict(zip(allnames, x)), allvalues))
+
+
+    blank_str = template.render({k: 0 for k in allnames})
+    blank = list(load_string(blank_str))
+    if len(blank) > 1:
+        raise ValueError('Templates must not return more than one configuration')
+    if 'name' in blank[0]:
+        raise ValueError('Templates cannot be named, use group instead')
+
+    confs = []
+    for ps in params:
+        string = template.render(ps)
+        for c in load_string(string):
+            yield c
+
+
+def load_files(*patterns, **kwargs):
+    for pattern in patterns:
+        for i in glob(pattern, **kwargs):
+            for config in load_file(i):
+                path = os.path.abspath(i)
+                if 'dir_path' not in config:
+                    config['dir_path'] = os.path.dirname(path)
+                yield config, path
+
+
+def load_config(config):
+    if isinstance(config, dict):
+        yield config, None
+    else:
+        yield from load_files(config)
+
+
+builtins = importlib.import_module('builtins')
+
+def name(value, known_modules=[]):
+    '''Return a name that can be imported, to serialize/deserialize an object'''
+    if value is None:
+        return 'None'
+    if not isinstance(value, type):  # Get the class name first
+        value = type(value)
+    tname = value.__name__
+    if hasattr(builtins, tname):
+        return tname
+    modname = value.__module__
+    if modname == '__main__':
+        return tname
+    if known_modules and modname in known_modules:
+        return tname
+    for kmod in known_modules:
+        if not kmod:
+            continue
+        module = importlib.import_module(kmod)
+        if hasattr(module, tname):
+            return tname
+    return '{}.{}'.format(modname, tname)
+
+
+def serializer(type_):
+    if type_ != 'str' and hasattr(builtins, type_):
+        return repr
+    return lambda x: x
+
+
+def serialize(v, known_modules=[]):
+    '''Get a text representation of an object.'''
+    tname = name(v, known_modules=known_modules)
+    func = serializer(tname)
+    return func(v), tname
+
+def deserializer(type_, known_modules=[]):
+    if type(type_) != str:  # Already deserialized
+        return type_
+    if type_ == 'str':
+        return lambda x='': x
+    if type_ == 'None':
+        return lambda x=None: None
+    if hasattr(builtins, type_):  # Check if it's a builtin type
+        cls = getattr(builtins, type_)
+        return lambda x=None: ast.literal_eval(x) if x is not None else cls()
+    # Otherwise, see if we can find the module and the class
+    modules = known_modules or []
+    options = []
+
+    for mod in modules:
+        if mod:
+            options.append((mod, type_))
+
+    if '.' in type_:  # Fully qualified module
+        module, type_ = type_.rsplit(".", 1)
+        options.append ((module, type_))
+
+    errors = []
+    for modname, tname in options:
+        try:
+            module = importlib.import_module(modname)
+            cls = getattr(module, tname)
+            return getattr(cls, 'deserialize', cls)
+        except (ModuleNotFoundError, AttributeError) as ex:
+            errors.append((modname, tname, ex))
+    raise Exception('Could not find type {}. Tried: {}'.format(type_, errors))
+
+
+def deserialize(type_, value=None, **kwargs):
+    '''Get an object from a text representation'''
+    if not isinstance(type_, str):
+        return type_
+    des = deserializer(type_, **kwargs)
+    if value is None:
+        return des
+    return des(value)

soil/simulation.py

@@ -13,9 +13,10 @@ import pickle
 
 from nxsim import NetworkSimulation
 
-from . import utils, basestring, agents
+from . import serialization, utils, basestring, agents
 from .environment import Environment
 from .utils import logger
+from .exporters import for_sim as exporters_for_sim
 
 
 class Simulation(NetworkSimulation):
@@ -50,6 +51,8 @@ class Simulation(NetworkSimulation):
     ---------
     name : str, optional
         name of the Simulation
+    group : str, optional
+        a group name can be used to link simulations
     topology : networkx.Graph instance, optional
     network_params : dict
         parameters used to create a topology with networkx, if no topology is given
@@ -60,8 +63,8 @@ class Simulation(NetworkSimulation):
     states : list, optional
         List of initial states corresponding to the nodes in the topology. Basic form is a list of integers
         whose value indicates the state
-    dir_path : str, optional
-        Directory path where to save pickled objects
+    dir_path: str, optional
+        Directory path to load simulation assets (files, modules...)
     seed : str, optional
         Seed to use for the random generator
     num_trials : int, optional
@@ -80,37 +83,37 @@ class Simulation(NetworkSimulation):
 
     """
 
-    def __init__(self, name=None, topology=None, network_params=None,
+    def __init__(self, name=None, group=None, topology=None, network_params=None,
                  network_agents=None, agent_type=None, states=None,
-                 default_state=None, interval=1, dump=None, dry_run=False,
-                 dir_path=None, num_trials=1, max_time=100,
-                 load_module=None, seed=None,
-                 environment_agents=None, environment_params=None,
-                 environment_class=None, **kwargs):
-
-        if topology is None:
-            topology = utils.load_network(network_params,
-                                          dir_path=dir_path)
-        elif isinstance(topology, basestring) or isinstance(topology, dict):
-            topology = json_graph.node_link_graph(topology)
+                 default_state=None, interval=1, num_trials=1,
+                 max_time=100, load_module=None, seed=None,
+                 dir_path=None, environment_agents=None,
+                 environment_params=None, environment_class=None,
+                 **kwargs):
 
+        self.seed = str(seed) or str(time.time())
         self.load_module = load_module
-        self.topology = nx.Graph(topology)
         self.network_params = network_params
-        self.name = name or 'UnnamedSimulation'
+        self.name = name or 'Unnamed_' + time.strftime("%Y-%m-%d_%H:%M:%S")
+        self.group = group or None
         self.num_trials = num_trials
         self.max_time = max_time
         self.default_state = default_state or {}
         self.dir_path = dir_path or os.getcwd()
         self.interval = interval
-        self.seed = str(seed) or str(time.time())
-        self.dump = dump
-        self.dry_run = dry_run
 
-        sys.path += [self.dir_path, os.getcwd()]
+        sys.path += list(x for x in [os.getcwd(), self.dir_path] if x not in sys.path)
+
+        if topology is None:
+            topology = serialization.load_network(network_params,
+                                                  dir_path=self.dir_path)
+        elif isinstance(topology, basestring) or isinstance(topology, dict):
+            topology = json_graph.node_link_graph(topology)
+        self.topology = nx.Graph(topology)
+
 
         self.environment_params = environment_params or {}
-        self.environment_class = utils.deserialize(environment_class,
+        self.environment_class = serialization.deserialize(environment_class,
                                                    known_modules=['soil.environment', ]) or Environment
 
         environment_agents = environment_agents or []
@@ -129,52 +132,69 @@ class Simulation(NetworkSimulation):
         return self.run(*args, **kwargs)
 
     def run(self, *args, **kwargs):
-        return list(self.run_simulation_gen(*args, **kwargs))
+        '''Run the simulation and return the list of resulting environments'''
+        return list(self._run_simulation_gen(*args, **kwargs))
+
+    def _run_sync_or_async(self, parallel=False, *args, **kwargs):
+        if parallel:
+            p = Pool()
+            func = partial(self.run_trial_exceptions,
+                           *args,
+                           **kwargs)
+            for i in p.imap_unordered(func, range(self.num_trials)):
+                if isinstance(i, Exception):
+                    logger.error('Trial failed:\n\t%s', i.message)
+                    continue
+                yield i
+        else:
+            for i in range(self.num_trials):
+                yield self.run_trial(i,
+                                     *args,
+                                     **kwargs)
+
+    def _run_simulation_gen(self, *args, parallel=False, dry_run=False,
+                            exporters=None, outdir=None, exporter_params={}, **kwargs):
+        logger.info('Using exporters: %s', exporters or [])
+        logger.info('Output directory: %s', outdir)
+        exporters = exporters_for_sim(self,
+                                      exporters or [],
+                                      dry_run=dry_run,
+                                      outdir=outdir,
+                                      **exporter_params)
 
-    def run_simulation_gen(self, *args, parallel=False, dry_run=False,
-                           **kwargs):
-        p = Pool()
         with utils.timer('simulation {}'.format(self.name)):
-            if parallel:
-                func = partial(self.run_trial_exceptions, dry_run=dry_run or self.dry_run,
-                                                         return_env=True,
-                                                         **kwargs)
-                for i in p.imap_unordered(func, range(self.num_trials)):
-                    if isinstance(i, Exception):
-                        logger.error('Trial failed:\n\t{}'.format(i.message))
-                        continue
-                    yield i
-            else:
-                for i in range(self.num_trials):
-                    yield self.run_trial(i, dry_run = dry_run or self.dry_run, **kwargs)
-            if not (dry_run or 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')
+            for exporter in exporters:
+                exporter.start()
+
+            for env in self._run_sync_or_async(*args, parallel=parallel,
+                                               **kwargs):
+                for exporter in exporters:
+                    exporter.trial_end(env)
+                yield env
+
+            for exporter in exporters:
+                exporter.end()
 
     def get_env(self, trial_id = 0, **kwargs):
-        opts=self.environment_params.copy()
-        env_name='{}_trial_{}'.format(self.name, trial_id)
+        '''Create an environment for a trial of the simulation'''
+        opts = self.environment_params.copy()
+        env_name = '{}_trial_{}'.format(self.name, trial_id)
         opts.update({
             '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': self.dir_path,
         })
         opts.update(kwargs)
-        env=self.environment_class(**opts)
+        env = self.environment_class(**opts)
         return env
 
-    def run_trial(self, trial_id = 0, until = None, return_env = True, **opts):
+    def run_trial(self, trial_id=0, until=None, **opts):
         """Run a single trial of the simulation
 
         Parameters
@@ -182,16 +202,12 @@ class Simulation(NetworkSimulation):
         trial_id : int
         """
         # Set-up trial environment and graph
-        until=until or self.max_time
-        env=self.get_env(trial_id = trial_id, **opts)
+        until = until or self.max_time
+        env = self.get_env(trial_id = trial_id, **opts)
         # Set up agents on nodes
         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(formats = self.dump)
-        if return_env:
-            return env
+        return env
     def run_trial_exceptions(self, *args, **kwargs):
         '''
         A wrapper for run_trial that catches exceptions and returns them.
@@ -210,38 +226,39 @@ class Simulation(NetworkSimulation):
     def to_yaml(self):
         return yaml.dump(self.to_dict())
 
-    def dump_yaml(self, dir_path = None, file_name = None):
-        dir_path=dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            os.makedirs(dir_path)
-        if not file_name:
-            file_name=os.path.join(dir_path,
-                                     '{}.dumped.yml'.format(self.name))
-        with open(file_name, 'w') as f:
+
+    def dump_yaml(self, f=None, outdir=None):
+        if not f and not outdir:
+            raise ValueError('specify a file or an output directory')
+
+        if not f:
+            f = os.path.join(outdir, '{}.dumped.yml'.format(self.name))
+
+        with utils.open_or_reuse(f, 'w') as f:
             f.write(self.to_yaml())
 
-    def dump_pickle(self, dir_path = None, pickle_name = None):
-        dir_path=dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            os.makedirs(dir_path)
-        if not pickle_name:
-            pickle_name=os.path.join(dir_path,
-                                       '{}.simulation.pickle'.format(self.name))
-        with open(pickle_name, 'wb') as f:
+    def dump_pickle(self, f=None, outdir=None):
+        if not outdir and not f:
+            raise ValueError('specify a file or an output directory')
+
+        if not f:
+            f = os.path.join(outdir,
+                             '{}.simulation.pickle'.format(self.name))
+        with utils.open_or_reuse(f, 'wb') as f:
             pickle.dump(self, f)
 
     def __getstate__(self):
         state={}
         for k, v in self.__dict__.items():
             if k[0] != '_':
-                state[k]=v
-        state['topology']=json_graph.node_link_data(self.topology)
-        state['network_agents']=agents.serialize_distribution(self.network_agents,
-                                                                known_modules = [])
-        state['environment_agents']=agents.serialize_distribution(self.environment_agents,
-                                                                    known_modules = [])
-        state['environment_class']=utils.serialize(self.environment_class,
-                                                     known_modules=['soil.environment'])[1]  # func, name
+                state[k] = v
+                state['topology'] = json_graph.node_link_data(self.topology)
+                state['network_agents'] = agents.serialize_distribution(self.network_agents,
+                                                                        known_modules = [])
+                state['environment_agents'] = agents.serialize_distribution(self.environment_agents,
+                                                                            known_modules = [])
+                state['environment_class'] = serialization.serialize(self.environment_class,
+                                                                     known_modules=['soil.environment'])[1]  # func, name
         if state['load_module'] is None:
             del state['load_module']
         return state
@@ -255,13 +272,20 @@ class Simulation(NetworkSimulation):
         self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents))
         self.environment_agents = agents._convert_agent_types(self.environment_agents,
                                                               known_modules=[self.load_module])
-        self.environment_class = utils.deserialize(self.environment_class,
+        self.environment_class = serialization.deserialize(self.environment_class,
                                                    known_modules=[self.load_module, 'soil.environment', ])  # func, name
         return state
 
 
-def from_config(config):
-    config = list(utils.load_config(config))
+def all_from_config(config):
+    configs = list(serialization.load_config(config))
+    for config, _ in configs:
+        sim = Simulation(**config)
+        yield sim
+
+
+def from_config(conf_or_path):
+    config = list(serialization.load_config(conf_or_path))
     if len(config) > 1:
         raise AttributeError('Provide only one configuration')
     config = config[0][0]
@@ -269,21 +293,14 @@ def from_config(config):
     return sim
 
 
-def run_from_config(*configs, results_dir='soil_output', dump=None, timestamp=False,  **kwargs):
+def run_from_config(*configs, **kwargs):
     for config_def in configs:
         # logger.info("Found {} config(s)".format(len(ls)))
-        for config, _ in utils.load_config(config_def):
+        for config, path in serialization.load_config(config_def):
             name = config.get('name', 'unnamed')
             logger.info("Using config(s): {name}".format(name=name))
 
-            if timestamp:
-                sim_folder = '{}_{}'.format(name,
-                                            time.strftime("%Y-%m-%d_%H:%M:%S"))
-            else:
-                sim_folder = name
-            dir_path = os.path.join(results_dir, sim_folder)
-            if dump is not None:
-                config['dump'] = dump
-            sim = Simulation(dir_path=dir_path, **config)
-            logger.info('Dumping results to {} : {}'.format(sim.dir_path, sim.dump))
+            dir_path = config.pop('dir_path', os.path.dirname(path))
+            sim = Simulation(dir_path=dir_path,
+                             **config)
             sim.run_simulation(**kwargs)

soil/utils.py

@@ -1,66 +1,13 @@
-import os
-import ast
-import yaml
 import logging
-import importlib
 import time
-from glob import glob
-from random import random
-from copy import deepcopy
-
-import networkx as nx
+import os
 
 from contextlib import contextmanager
 
-
 logger = logging.getLogger('soil')
 logger.setLevel(logging.INFO)
 
 
-def load_network(network_params, dir_path=None):
-    if network_params is None:
-        return nx.Graph()
-    path = network_params.get('path', None)
-    if path:
-        if dir_path and not os.path.isabs(path):
-            path = os.path.join(dir_path, path)
-        extension = os.path.splitext(path)[1][1:]
-        kwargs = {}
-        if extension == 'gexf':
-            kwargs['version'] = '1.2draft'
-            kwargs['node_type'] = int
-        try:
-            method = getattr(nx.readwrite, 'read_' + extension)
-        except AttributeError:
-            raise AttributeError('Unknown format')
-        return method(path, **kwargs)
-
-    net_args = network_params.copy()
-    net_type = net_args.pop('generator')
-
-    method = getattr(nx.generators, net_type)
-    return method(**net_args)
-
-
-def load_file(infile):
-    with open(infile, 'r') as f:
-        return list(yaml.load_all(f))
-
-
-def load_files(*patterns):
-    for pattern in patterns:
-        for i in glob(pattern):
-            for config in load_file(i):
-                yield config, os.path.abspath(i)
-
-
-def load_config(config):
-    if isinstance(config, dict):
-        yield config, None
-    else:
-        yield from load_files(config)
-
-
 @contextmanager
 def timer(name='task', pre="", function=logger.info, to_object=None):
     start = time.time()
@@ -76,79 +23,15 @@ def timer(name='task', pre="", function=logger.info, to_object=None):
         to_object.end = end
 
 
-builtins = importlib.import_module('builtins')
-
-def name(value, known_modules=[]):
-    '''Return a name that can be imported, to serialize/deserialize an object'''
-    if value is None:
-        return 'None'
-    if not isinstance(value, type):  # Get the class name first
-        value = type(value)
-    tname = value.__name__
-    if hasattr(builtins, tname):
-        return tname
-    modname = value.__module__
-    if modname == '__main__':
-        return tname
-    if known_modules and modname in known_modules:
-        return tname
-    for kmod in known_modules:
-        if not kmod:
-            continue
-        module = importlib.import_module(kmod)
-        if hasattr(module, tname):
-            return tname
-    return '{}.{}'.format(modname, tname)
+def safe_open(path, *args, **kwargs):
+    outdir = os.path.dirname(path)
+    if outdir and not os.path.exists(outdir):
+        os.makedirs(outdir)
+    return open(path, *args, **kwargs)
 
 
-def serializer(type_):
-    if type_ != 'str' and hasattr(builtins, type_):
-        return repr
-    return lambda x: x
-
-
-def serialize(v, known_modules=[]):
-    '''Get a text representation of an object.'''
-    tname = name(v, known_modules=known_modules)
-    func = serializer(tname)
-    return func(v), tname
-
-def deserializer(type_, known_modules=[]):
-    if type_ == 'str':
-        return lambda x='': x
-    if type_ == 'None':
-        return lambda x=None: None
-    if hasattr(builtins, type_):  # Check if it's a builtin type
-        cls = getattr(builtins, type_)
-        return lambda x=None: ast.literal_eval(x) if x is not None else cls()
-    # Otherwise, see if we can find the module and the class
-    modules = known_modules or []
-    options = []
-
-    for mod in modules:
-        if mod:
-            options.append((mod, type_))
-
-    if '.' in type_:  # Fully qualified module
-        module, type_ = type_.rsplit(".", 1)
-        options.append ((module, type_))
-
-    errors = []
-    for modname, tname in options:
-        try:
-            module = importlib.import_module(modname)
-            cls = getattr(module, tname)
-            return getattr(cls, 'deserialize', cls)
-        except (ImportError, AttributeError) as ex:
-            errors.append((modname, tname, ex))
-    raise Exception('Could not find type {}. Tried: {}'.format(type_, errors))
-
-
-def deserialize(type_, value=None, **kwargs):
-    '''Get an object from a text representation'''
-    if not isinstance(type_, str):
-        return type_
-    des = deserializer(type_, **kwargs)
-    if value is None:
-        return des
-    return des(value)
+def open_or_reuse(f, *args, **kwargs):
+    try:
+        return safe_open(f, *args, **kwargs)
+    except TypeError:
+        return f

soil/web/TerroristNetworkModel.py

@@ -1,255 +0,0 @@
-import random
-import networkx as nx
-from soil.agents import BaseAgent, FSM, state, default_state
-from scipy.spatial import cKDTree as KDTree
-
-global betweenness_centrality_global
-global degree_centrality_global
-
-betweenness_centrality_global = None
-degree_centrality_global = None
-
-class TerroristSpreadModel(FSM):
-    """
-    Settings:
-        information_spread_intensity
-
-        terrorist_additional_influence
-
-        min_vulnerability (optional else zero)
-
-        max_vulnerability
-
-        prob_interaction
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        global betweenness_centrality_global
-        global degree_centrality_global
-        
-        if betweenness_centrality_global == None:
-            betweenness_centrality_global = nx.betweenness_centrality(self.global_topology)
-        if degree_centrality_global == None:
-            degree_centrality_global = nx.degree_centrality(self.global_topology)
-
-        self.information_spread_intensity = environment.environment_params['information_spread_intensity']
-        self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
-        self.prob_interaction = environment.environment_params['prob_interaction']
-
-        if self['id'] == self.civilian.id:       # Civilian
-            self.initial_belief = random.uniform(0.00, 0.5)
-        elif self['id'] == self.terrorist.id:     # Terrorist
-            self.initial_belief = random.uniform(0.8, 1.00)
-        elif self['id'] == self.leader.id:     # Leader
-            self.initial_belief = 1.00
-        else:
-            raise Exception('Invalid state id: {}'.format(self['id']))
-
-        if 'min_vulnerability' in environment.environment_params:
-            self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
-        else :
-            self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
-
-        self.mean_belief = self.initial_belief
-        self.betweenness_centrality = betweenness_centrality_global[self.id]
-        self.degree_centrality = degree_centrality_global[self.id]
-
-        # self.state['radicalism'] = self.mean_belief
-
-    def count_neighboring_agents(self, state_id=None):
-        if isinstance(state_id, list):
-            return len(self.get_neighboring_agents(state_id))
-        else:
-            return len(super().get_agents(state_id, limit_neighbors=True))
-
-    def get_neighboring_agents(self, state_id=None):
-        if isinstance(state_id, list):
-            _list = []
-            for i in state_id:
-                _list += super().get_agents(i, limit_neighbors=True)
-            return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
-        else:
-            _list = super().get_agents(state_id, limit_neighbors=True) 
-            return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
-
-    @state
-    def civilian(self):
-        if self.count_neighboring_agents() > 0:
-            neighbours = []
-            for neighbour in self.get_neighboring_agents():
-                if random.random() < self.prob_interaction:
-                    neighbours.append(neighbour)
-            influence = sum( neighbour.degree_centrality for neighbour in neighbours )
-            mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
-            self.initial_belief = self.mean_belief
-            mean_belief = mean_belief * self.information_spread_intensity + self.initial_belief * ( 1 - self.information_spread_intensity )
-            self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
-        
-        if self.mean_belief >= 0.8:
-            return self.terrorist
-
-    @state
-    def leader(self):
-        self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
-        if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
-            for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
-                if neighbour.betweenness_centrality > self.betweenness_centrality:
-                    return self.terrorist
-
-    @state
-    def terrorist(self):
-        if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
-            neighbours = self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id])
-            influence = sum( neighbour.degree_centrality for neighbour in neighbours )
-            mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
-            self.initial_belief = self.mean_belief
-            self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
-            self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
-
-        if self.count_neighboring_agents(state_id=self.leader.id) == 0 and self.count_neighboring_agents(state_id=self.terrorist.id) > 0:
-            max_betweenness_centrality = self
-            for neighbour in self.get_neighboring_agents(state_id=self.terrorist.id):
-                if neighbour.betweenness_centrality > max_betweenness_centrality.betweenness_centrality:
-                    max_betweenness_centrality = neighbour
-            if max_betweenness_centrality == self:
-                return self.leader
-
-    def add_edge(self, G, source, target):
-        G.add_edge(source.id, target.id, start=self.env._now)
-
-    def link_search(self, G, node, radius):
-        pos = nx.get_node_attributes(G, 'pos')
-        nodes, coords = list(zip(*pos.items()))
-        kdtree = KDTree(coords)  # Cannot provide generator.
-        edge_indexes = kdtree.query_pairs(radius, 2)
-        _list = [ edge[int(not edge.index(node))] for edge in edge_indexes if node in edge ]
-        return [ G.nodes()[index]['agent'] for index in _list ]
-
-    def social_search(self, G, node, steps):
-        nodes = list(nx.ego_graph(G, node, radius=steps).nodes())
-        nodes.remove(node)
-        return [ G.nodes()[index]['agent'] for index in nodes ]
-
-
-class TrainingAreaModel(FSM):
-    """
-    Settings:
-        training_influence
-
-        min_vulnerability
-
-    Requires TerroristSpreadModel.
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.training_influence = environment.environment_params['training_influence']
-        if 'min_vulnerability' in environment.environment_params:
-            self.min_vulnerability = environment.environment_params['min_vulnerability']
-        else: self.min_vulnerability = 0
-
-    @default_state
-    @state
-    def terrorist(self):
-        for neighbour in self.get_neighboring_agents():
-            if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability > self.min_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )        
-
-
-class HavenModel(FSM):
-    """
-    Settings:
-        haven_influence
-
-        min_vulnerability
-
-        max_vulnerability
-
-    Requires TerroristSpreadModel.
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.haven_influence = environment.environment_params['haven_influence']
-        if 'min_vulnerability' in environment.environment_params:
-            self.min_vulnerability = environment.environment_params['min_vulnerability']
-        else: self.min_vulnerability = 0
-        self.max_vulnerability = environment.environment_params['max_vulnerability']
-
-    @state
-    def civilian(self):
-        for neighbour_agent in self.get_neighboring_agents():
-            if isinstance(neighbour_agent, TerroristSpreadModel) and neighbour_agent['id'] == neighbour_agent.civilian.id:
-                for neighbour in self.get_neighboring_agents():
-                    if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability > self.min_vulnerability:
-                        neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
-                return self.civilian
-        return self.terrorist
-
-    @state
-    def terrorist(self):
-        for neighbour in self.get_neighboring_agents():
-            if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability < self.max_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
-        return self.terrorist
-
-        
-class TerroristNetworkModel(TerroristSpreadModel):
-    """
-    Settings:
-        sphere_influence
-
-        vision_range
-
-        weight_social_distance
-
-        weight_link_distance
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.vision_range = environment.environment_params['vision_range']
-        self.sphere_influence = environment.environment_params['sphere_influence']
-        self.weight_social_distance = environment.environment_params['weight_social_distance']
-        self.weight_link_distance = environment.environment_params['weight_link_distance']
-
-    @state
-    def terrorist(self):
-        self.update_relationships()
-        return super().terrorist()
-
-    @state
-    def leader(self):
-        self.update_relationships()
-        return super().leader()
-
-    def update_relationships(self):
-        if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
-            close_ups = self.link_search(self.global_topology, self.id, self.vision_range)
-            step_neighbours = self.social_search(self.global_topology, self.id, self.sphere_influence)
-            search = list(set(close_ups).union(step_neighbours))
-            neighbours = self.get_neighboring_agents()
-            search = [item for item in search if not item in neighbours and isinstance(item, TerroristNetworkModel)]
-            for agent in search:
-                social_distance = 1 / self.shortest_path_length(self.global_topology, self.id, agent.id)
-                spatial_proximity = ( 1 - self.get_distance(self.global_topology, self.id, agent.id) )
-                prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
-                if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
-                    self.add_edge(self.global_topology, self, agent)
-                    break
-
-    def get_distance(self, G, source, target):
-        source_x, source_y = nx.get_node_attributes(G, 'pos')[source]
-        target_x, target_y = nx.get_node_attributes(G, 'pos')[target]
-        dx = abs( source_x - target_x )
-        dy = abs( source_y - target_y )
-        return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
-
-    def shortest_path_length(self, G, source, target):
-        try:
-            return nx.shortest_path_length(G, source, target)
-        except nx.NetworkXNoPath:
-            return float('inf')

soil/web/__init__.py

@@ -180,7 +180,7 @@ class SocketHandler(tornado.websocket.WebSocketHandler):
         with self.logging(self.simulation_name):
             try:
                 config = dict(**self.config)
-                config['dir_path'] = os.path.join(self.application.dir_path, config['name'])
+                config['outdir'] = os.path.join(self.application.outdir, config['name'])
                 config['dump'] = self.application.dump
                 self.trials = yield self.nonblocking(config)
 
@@ -232,12 +232,12 @@ class ModularServer(tornado.web.Application):
     settings = {'debug': True,
                 'template_path': ROOT + '/templates'}
 
-    def __init__(self, dump=False, dir_path='output', name='SOIL', verbose=True, *args, **kwargs):
+    def __init__(self, dump=False, outdir='output', name='SOIL', verbose=True, *args, **kwargs):
         
         self.verbose = verbose
         self.name = name
         self.dump = dump
-        self.dir_path = dir_path
+        self.outdir = outdir
 
         # Initializing the application itself:
         super().__init__(self.handlers, **self.settings)

tests/test_analysis.py

@@ -39,7 +39,6 @@ class TestAnalysis(TestCase):
         agent should be able to update its state."""
         config = {
             'name': 'analysis',
-            'dry_run': True,
             'seed': 'seed',
             'network_params': {
                 'generator': 'complete_graph',
@@ -53,7 +52,7 @@ class TestAnalysis(TestCase):
             }
         }
         s = simulation.from_config(config)
-        self.env = s.run_simulation()[0]
+        self.env = s.run_simulation(dry_run=True)[0]
 
     def test_saved(self):
         env = self.env
@@ -65,7 +64,7 @@ class TestAnalysis(TestCase):
 
     def test_count(self):
         env = self.env
-        df = analysis.read_sql(env._history._db)
+        df = analysis.read_sql(env._history.db_path)
         res = analysis.get_count(df, 'SEED', 'id')
         assert res['SEED']['seedanalysis_trial_0'].iloc[0] == 1
         assert res['SEED']['seedanalysis_trial_0'].iloc[-1] == 1

tests/test_examples.py

@@ -2,11 +2,13 @@ from unittest import TestCase
 import os
 from os.path import join
 
-from soil import utils, simulation
+from soil import serialization, simulation
 
 ROOT = os.path.abspath(os.path.dirname(__file__))
 EXAMPLES = join(ROOT, '..', 'examples')
 
+FORCE_TESTS = os.environ.get('FORCE_TESTS', '')
+
 
 class TestExamples(TestCase):
     pass
@@ -15,28 +17,32 @@ class TestExamples(TestCase):
 def make_example_test(path, config):
     def wrapped(self):
         root = os.getcwd()
-        os.chdir(os.path.dirname(path))
-        s = simulation.from_config(config)
-        iterations = s.max_time * s.num_trials
-        if iterations > 1000:
-            self.skipTest('This example would probably take too long')
-        envs = s.run_simulation(dry_run=True)
-        assert envs
-        for env in envs:
-            assert env
-            try:
-                n = config['network_params']['n']
-                assert len(list(env.network_agents)) == n
-                assert env.now > 2  # It has run
-                assert env.now <= config['max_time']  # But not further than allowed
-            except KeyError:
-                pass
-        os.chdir(root)
+        for s in simulation.all_from_config(path):
+            iterations = s.max_time * s.num_trials
+            if iterations > 1000:
+                s.max_time = 100
+                s.num_trials = 1
+            if config.get('skip_test', False) and not FORCE_TESTS:
+                self.skipTest('Example ignored.')
+            envs = s.run_simulation(dry_run=True)
+            assert envs
+            for env in envs:
+                assert env
+                try:
+                    n = config['network_params']['n']
+                    assert len(list(env.network_agents)) == n
+                    assert env.now > 2  # It has run
+                    assert env.now <= config['max_time']  # But not further than allowed
+                except KeyError:
+                    pass
     return wrapped
 
 
 def add_example_tests():
-    for config, path in utils.load_config(join(EXAMPLES, '**', '*.yml')):
+    for config, path in serialization.load_files(
+            join(EXAMPLES, '*', '*.yml'),
+            join(EXAMPLES, '*.yml'),
+    ):
         p = make_example_test(path=path, config=config)
         fname = os.path.basename(path)
         p.__name__ = 'test_example_file_%s' % fname

tests/test_exporters.py

@@ -0,0 +1,110 @@
+import os
+import io
+import tempfile
+import shutil
+from time import time
+
+from unittest import TestCase
+from soil import exporters
+from soil.utils import safe_open
+from soil import simulation
+
+
+class Dummy(exporters.Exporter):
+    started = False
+    trials = 0
+    ended = False
+    total_time = 0
+
+    def start(self):
+        self.__class__.started = True
+
+    def trial_end(self, env):
+        assert env
+        self.__class__.trials += 1
+        self.__class__.total_time += env.now
+
+    def end(self):
+        self.__class__.ended = True
+
+
+class Exporters(TestCase):
+    def test_basic(self):
+        config = {
+            'name': 'exporter_sim',
+            'network_params': {},
+            'agent_type': 'CounterModel',
+            'max_time': 2,
+            'num_trials': 5,
+            'environment_params': {}
+        }
+        s = simulation.from_config(config)
+        s.run_simulation(exporters=[Dummy], dry_run=True)
+        assert Dummy.started
+        assert Dummy.ended
+        assert Dummy.trials == 5
+        assert Dummy.total_time == 2*5
+
+    def test_distribution(self):
+        '''The distribution exporter should write the number of agents in each state'''
+        config = {
+            'name': 'exporter_sim',
+            'network_params': {
+                'generator': 'complete_graph',
+                'n': 4
+            },
+            'agent_type': 'CounterModel',
+            'max_time': 2,
+            'num_trials': 5,
+            'environment_params': {}
+        }
+        output = io.StringIO()
+        s = simulation.from_config(config)
+        s.run_simulation(exporters=[exporters.Distribution], dry_run=True, exporter_params={'copy_to': output})
+        result = output.getvalue()
+        assert 'count' in result
+        assert 'SEED,Noneexporter_sim_trial_3,1,,1,1,1,1' in result
+
+    def test_writing(self):
+        '''Try to write CSV, GEXF, sqlite and YAML (without dry_run)'''
+        n_trials = 5
+        config = {
+            'name': 'exporter_sim',
+            'network_params': {
+                'generator': 'complete_graph',
+                'n': 4
+            },
+            'agent_type': 'CounterModel',
+            'max_time': 2,
+            'num_trials': n_trials,
+            'environment_params': {}
+        }
+        output = io.StringIO()
+        s = simulation.from_config(config)
+        tmpdir = tempfile.mkdtemp()
+        envs = s.run_simulation(exporters=[
+            exporters.Default,
+            exporters.CSV,
+            exporters.Gexf,
+            exporters.Distribution,
+        ],
+                                outdir=tmpdir,
+                                exporter_params={'copy_to': output})
+        result = output.getvalue()
+
+        simdir = os.path.join(tmpdir, s.group or '', s.name)
+        with open(os.path.join(simdir, '{}.dumped.yml'.format(s.name))) as f:
+            result = f.read()
+            assert result
+
+        try:
+            for e in envs:
+                with open(os.path.join(simdir, '{}.gexf'.format(e.name))) as f:
+                    result = f.read()
+                    assert result
+
+                with open(os.path.join(simdir, '{}.csv'.format(e.name))) as f:
+                    result = f.read()
+                    assert result
+        finally:
+            shutil.rmtree(tmpdir)

tests/test_history.py

@@ -120,18 +120,18 @@ class TestHistory(TestCase):
         assert os.path.exists(db_path)
 
         # Recover the data
-        recovered = history.History(db_path=db_path, backup=False)
+        recovered = history.History(db_path=db_path)
         assert recovered['a_1', 0, 'id'] == 'v'
         assert recovered['a_1', 4, 'id'] == 'e'
 
-        # Using the same name should create a backup copy
+        # 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 not len(newhistory[None, None, None])
+        assert len(newhistory[None, None, None]) == 0
 
     def test_history_tuples(self):
         """

tests/test_main.py

@@ -1,13 +1,15 @@
 from unittest import TestCase
 
 import os
+import io
 import yaml
 import pickle
 import networkx as nx
 from functools import partial
 
 from os.path import join
-from soil import simulation, Environment, agents, utils, history
+from soil import (simulation, Environment, agents, serialization,
+                  history, utils)
 
 
 ROOT = os.path.abspath(os.path.dirname(__file__))
@@ -27,22 +29,20 @@ class TestMain(TestCase):
         Raise an exception otherwise.
         """
         config = {
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             }
         }
-        G = utils.load_network(config['network_params'])
+        G = serialization.load_network(config['network_params'])
         assert G
         assert len(G) == 2
         with self.assertRaises(AttributeError):
             config = {
-            'dry_run': True,
                 'network_params': {
                     'path': join(ROOT, 'unknown.extension')
                 }
             }
-            G = utils.load_network(config['network_params'])
+            G = serialization.load_network(config['network_params'])
             print(G)
 
     def test_generate_barabasi(self):
@@ -51,22 +51,20 @@ class TestMain(TestCase):
         should be used to generate a network
         """
         config = {
-            'dry_run': True,
             'network_params': {
                 'generator': 'barabasi_albert_graph'
             }
         }
         with self.assertRaises(TypeError):
-            G = utils.load_network(config['network_params'])
+            G = serialization.load_network(config['network_params'])
         config['network_params']['n'] = 100
         config['network_params']['m'] = 10
-        G = utils.load_network(config['network_params'])
+        G = serialization.load_network(config['network_params'])
         assert len(G) == 100
 
     def test_empty_simulation(self):
         """A simulation with a base behaviour should do nothing"""
         config = {
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -83,7 +81,6 @@ class TestMain(TestCase):
         agent should be able to update its state."""
         config = {
             'name': 'CounterAgent',
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -107,7 +104,6 @@ class TestMain(TestCase):
         """
         config = {
             'name': 'CounterAgent',
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -133,7 +129,6 @@ class TestMain(TestCase):
     def test_custom_agent(self):
         """Allow for search of neighbors with a certain state_id"""
         config = {
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -153,12 +148,11 @@ class TestMain(TestCase):
 
     def test_torvalds_example(self):
         """A complete example from a documentation should work."""
-        config = utils.load_file(join(EXAMPLES, 'torvalds.yml'))[0]
+        config = serialization.load_file(join(EXAMPLES, 'torvalds.yml'))[0]
         config['network_params']['path'] = join(EXAMPLES,
                                                 config['network_params']['path'])
         s = simulation.from_config(config)
-        s.dry_run = True
-        env = s.run_simulation()[0]
+        env = s.run_simulation(dry_run=True)[0]
         for a in env.network_agents:
             skill_level = a.state['skill_level']
             if a.id == 'Torvalds':
@@ -180,9 +174,8 @@ class TestMain(TestCase):
         should be equivalent to the configuration file used
         """
         with utils.timer('loading'):
-            config = utils.load_file(join(EXAMPLES, 'complete.yml'))[0]
+            config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
             s = simulation.from_config(config)
-            s.dry_run = True
         with utils.timer('serializing'):
             serial = s.to_yaml()
         with utils.timer('recovering'):
@@ -196,9 +189,8 @@ class TestMain(TestCase):
         The configuration should not change after running
          the simulation.
         """
-        config = utils.load_file(join(EXAMPLES, 'complete.yml'))[0]
+        config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
         s = simulation.from_config(config)
-        s.dry_run = True
         for i in range(5):
             s.run_simulation(dry_run=True)
             nconfig = s.to_dict()
@@ -206,7 +198,7 @@ class TestMain(TestCase):
             assert config == nconfig
 
     def test_row_conversion(self):
-        env = Environment(dry_run=True)
+        env = Environment()
         env['test'] = 'test_value'
 
         res = list(env.history_to_tuples())
@@ -225,8 +217,9 @@ class TestMain(TestCase):
         from geometric models. We should work around it.
         """
         G = nx.random_geometric_graph(20, 0.1)
-        env = Environment(topology=G, dry_run=True)
-        env.dump_gexf('/tmp/dump-gexf')
+        env = Environment(topology=G)
+        f = io.BytesIO()
+        env.dump_gexf(f)
 
     def test_save_graph(self):
         '''
@@ -236,7 +229,7 @@ class TestMain(TestCase):
         '''
         G = nx.cycle_graph(5)
         distribution = agents.calculate_distribution(None, agents.BaseAgent)
-        env = Environment(topology=G, network_agents=distribution, dry_run=True)
+        env = Environment(topology=G, network_agents=distribution)
         env[0, 0, 'testvalue'] = 'start'
         env[0, 10, 'testvalue'] = 'finish'
         nG = env.history_to_graph()
@@ -245,11 +238,11 @@ class TestMain(TestCase):
         assert ('finish', 10, None) in values
 
     def test_serialize_class(self):
-        ser, name = utils.serialize(agents.BaseAgent)
+        ser, name = serialization.serialize(agents.BaseAgent)
         assert name == 'soil.agents.BaseAgent'
         assert ser == agents.BaseAgent
 
-        ser, name = utils.serialize(CustomAgent)
+        ser, name = serialization.serialize(CustomAgent)
         assert name == 'test_main.CustomAgent'
         assert ser == CustomAgent
         pickle.dumps(ser)
@@ -257,9 +250,9 @@ class TestMain(TestCase):
     def test_serialize_builtin_types(self):
 
         for i in [1, None, True, False, {}, [], list(), dict()]:
-            ser, name = utils.serialize(i)
+            ser, name = serialization.serialize(i)
             assert type(ser) == str
-            des = utils.deserialize(name, ser)
+            des = serialization.deserialize(name, ser)
             assert i == des
 
     def test_serialize_agent_type(self):
@@ -312,11 +305,35 @@ class TestMain(TestCase):
         recovered = pickle.loads(pickled)
 
         assert recovered.env.name == 'Test'
-        assert recovered['key'] == 'test'
+        assert list(recovered.env._history.to_tuples())
         assert recovered['key', 0] == 'test'
+        assert recovered['key'] == 'test'
 
     def test_history(self):
         '''Test storing in and retrieving from history (sqlite)'''
         h = history.History()
         h.save_record(agent_id=0, t_step=0, key="test", value="hello")
         assert h[0, 0, "test"] == "hello"
+
+    def test_subgraph(self):
+        '''An agent should be able to subgraph the global topology'''
+        G = nx.Graph()
+        G.add_node(3)
+        G.add_edge(1, 2)
+        distro = agents.calculate_distribution(agent_type=agents.NetworkAgent)
+        env = Environment(name='Test', topology=G, network_agents=distro)
+        lst = list(env.network_agents)
+
+        a2 = env.get_agent(2)
+        a3 = env.get_agent(3)
+        assert len(a2.subgraph(limit_neighbors=True)) == 2
+        assert len(a3.subgraph(limit_neighbors=True)) == 1
+        assert len(a3.subgraph(limit_neighbors=True, center=False)) == 0
+        assert len(a3.subgraph(agent_type=agents.NetworkAgent)) == 3
+
+    def test_templates(self):
+        '''Loading a template should result in several configs'''
+        configs = serialization.load_file(join(EXAMPLES, 'template.yml'))
+        assert len(configs) > 0
+
+