WIP: all tests pass

CHANGELOG.md

@@ -3,13 +3,16 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [UNRELEASED]
+## [0.3 UNRELEASED]
+### Added
+* Simple debugging capabilities, with a custom `pdb.Debugger` subclass that exposes commands to list agents and their status and set breakpoints on states (for FSM agents)
 ### Changed
-* Configuration schema is very different now. Check `soil.config` for more information. We are using Pydantic for (de)serialization.
+* Configuration schema is very different now. Check `soil.config` for more information. We are also using Pydantic for (de)serialization.
 * There may be more than one topology/network in the simulation
 * Agents are split into groups now. Each group may be assigned a given set of agents or an agent distribution, and a network topology to be assigned to.
 ### Removed
 * Any `tsih` and `History` integration in the main classes. To record the state of environments/agents, just use a datacollector. In some cases this may be slower or consume more memory than the previous system. However, few cases actually used the full potential of the history, and it came at the cost of unnecessary complexity and worse performance for the majority of cases.
+
 ## [0.20.7]
 ### Changed
 * Creating a `time.When` from another `time.When` does not nest them anymore (it returns the argument)

README.md

@@ -5,6 +5,42 @@ Learn how to run your own simulations with our [documentation](http://soilsim.re
 
 Follow our [tutorial](examples/tutorial/soil_tutorial.ipynb) to develop your own agent models.
 
+
+# Changes in version 0.3
+
+Version 0.3 came packed with many changes to provide much better integration with MESA.
+For a long time, we tried to keep soil backwards-compatible, but it turned out to be a big endeavour and the resulting code was less readable.
+This translates to harder maintenance and a worse experience for newcomers. 
+In the end, we decided to make some breaking changes.
+
+If you have an older Soil simulation, you have two options:
+
+* Update the necessary configuration files and code. You may use the examples in the `examples` folder for reference, as well as the documentation.
+* Keep using a previous `soil` version.
+
+## Mesa compatibility
+
+Soil is in the process of becoming fully compatible with MESA.
+The idea is to provide a set of modular classes and functions that extend the functionality of mesa, whilst staying compatible.
+In the end, it should be possible to add regular mesa agents to a soil simulation, or use a soil agent within a mesa simulation/model.
+
+This is a non-exhaustive list of tasks to achieve compatibility:
+
+- [ ] Integrate `soil.Simulation` with mesa's runners:
+  - [ ] `soil.Simulation` could mimic/become a `mesa.batchrunner`
+- [ ] Integrate `soil.Environment` with `mesa.Model`:
+  - [x] `Soil.Environment` inherits from `mesa.Model`
+  - [x] `Soil.Environment` includes a Mesa-like Scheduler (see the `soil.time` module.
+  - [ ] Allow for `mesa.Model` to be used in a simulation.
+- [ ] Integrate `soil.Agent` with `mesa.Agent`:
+  - [x] Rename agent.id to unique_id?
+  - [x] mesa agents can be used in soil simulations (see `examples/mesa`)
+- [ ] Provide examples
+  - [ ] Using mesa modules in a soil simulation
+  - [ ] Using soil modules in a mesa simulation
+- [ ] Document the new APIs and usage
+
+
 ## Citation 
 
 
@@ -31,25 +67,6 @@ If you use Soil in your research, don't forget to cite this paper:
 
 ```
 
-## Mesa compatibility
-
-Soil is in the process of becoming fully compatible with MESA.
-As of this writing, 
-
-This is a non-exhaustive list of tasks to achieve compatibility:
-
-* Environments.agents and mesa.Agent.agents are not the same. env is a property, and it only takes into account network and environment agents. Might rename environment_agents to other_agents or sth like that
-
-- [ ] Integrate `soil.Simulation` with mesa's runners:
-  - [ ] `soil.Simulation` could mimic/become a `mesa.batchrunner`
-- [ ] Integrate `soil.Environment` with `mesa.Model`:
-  - [x] `Soil.Environment` inherits from `mesa.Model`
-  - [x] `Soil.Environment` includes a Mesa-like Scheduler (see the `soil.time` module.
-- [ ] Integrate `soil.Agent` with `mesa.Agent`:
-  - [x] Rename agent.id to unique_id?
-  - [x] mesa agents can be used in soil simulations (see `examples/mesa`)
-- [ ] Document the new APIs and usage
-
 @Copyright GSI - Universidad Politécnica de Madrid 2017-2021
 
 [![SOIL](logo_gsi.png)](https://www.gsi.upm.es)

docs/configuration.rst

@@ -13,7 +13,7 @@ Here's an example (``example.yml``).
 
 
 This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
-The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil.
+The 100 nodes in the network will be SISaModel agents (``network_agents.agent_class``), which is an agent behavior that is included in Soil.
 10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
 All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
 The state of the agents will be updated every 2 seconds (``interval``).
@@ -116,7 +116,7 @@ Agents
 ======
 
 Agents are a way of modelling behavior.
-Agents can be characterized with two variables: agent type (``agent_type``) and state.
+Agents can be characterized with two variables: agent type (``agent_class``) and state.
 The agent type is a ``soil.Agent`` class, which contains the code that encapsulates the behavior of the agent.
 The state is a set of variables, which may change during the simulation, and that the code may use to control the behavior.
 All agents provide a ``step`` method either explicitly or implicitly (by inheriting it from a superclass), which controls how the agent will behave in each step of the simulation.
@@ -142,7 +142,7 @@ Hence, every node in the network will be associated to an agent of that type.
 
 .. code:: yaml
 
-   agent_type: SISaModel
+   agent_class: SISaModel
 
 It is also possible to add more than one type of agent to the simulation.
 
@@ -152,9 +152,9 @@ For instance, with following configuration, it is five times more likely for a n
 .. code:: yaml
 
     network_agents:
-          - agent_type: SISaModel
+          - agent_class: SISaModel
             weight: 1
-          - agent_type: CounterModel
+          - agent_class: CounterModel
             weight: 5
 
 The third option is to specify the type of agent on the node itself, e.g.:
@@ -165,10 +165,10 @@ The third option is to specify the type of agent on the node itself, e.g.:
    topology:
        nodes:
            - id: first
-   agent_type: BaseAgent
+   agent_class: BaseAgent
    states:
        first:
-         agent_type: SISaModel
+         agent_class: SISaModel
           
 
 This would also work with a randomly generated network:
@@ -179,9 +179,9 @@ This would also work with a randomly generated network:
    network:
        generator: complete
        n: 5
-   agent_type: BaseAgent
+   agent_class: BaseAgent
    states:
-       - agent_type: SISaModel
+       - agent_class: SISaModel
 
               
 
@@ -192,11 +192,11 @@ e.g., to populate the network with SISaModel, roughly 10% of them with a discont
 .. code:: yaml
 
     network_agents:
-        - agent_type: SISaModel
+        - agent_class: SISaModel
             weight: 9
             state:
             id: neutral
-        - agent_type: SISaModel
+        - agent_class: SISaModel
             weight: 1
             state:
             id: discontent
@@ -206,7 +206,7 @@ For instance, to add a state for the two nodes in this configuration:
 
 .. code:: yaml
 
-   agent_type: SISaModel
+   agent_class: SISaModel
    network:
       generator: complete_graph
       n: 2
@@ -231,10 +231,10 @@ These agents are programmed in much the same way as network agents, the only dif
 .. code::
 
    environment_agents:
-       - agent_type: MyAgent
+       - agent_class: MyAgent
          state:
            mood: happy
-       - agent_type: DummyAgent
+       - agent_class: DummyAgent
 
 
 You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance.

docs/example.yml

@@ -8,15 +8,15 @@ network_params:
     n: 100
     m: 2
 network_agents:
-    - agent_type: SISaModel
+    - agent_class: SISaModel
       weight: 1
       state:
         id: content
-    - agent_type: SISaModel
+    - agent_class: SISaModel
       weight: 1
       state:
         id: discontent
-    - agent_type: SISaModel
+    - agent_class: SISaModel
       weight: 8
       state:
         id: neutral

docs/quickstart.yml

@@ -3,11 +3,11 @@ name: quickstart
 num_trials: 1
 max_time: 1000
 network_agents:
-  - agent_type: SISaModel
+  - agent_class: SISaModel
     state:
       id: neutral
     weight: 1
-  - agent_type: SISaModel
+  - agent_class: SISaModel
     state:
       id: content
     weight: 2

docs/soil-vs.rst

@@ -0,0 +1,12 @@
+### MESA
+
+Starting with version 0.3, Soil has been redesigned to complement Mesa, while remaining compatible with it.
+That means that every component in Soil (i.e., Models, Environments, etc.) can be mixed with existing mesa components.
+In fact, there are examples that show how that integration may be used, in the `examples/mesa` folder in the repository.
+
+Here are some reasons to use Soil instead of plain mesa:
+
+- Less boilerplate for common scenarios (by some definitions of common)
+- Functions to automatically populate a topology with an agent distribution (i.e., different ratios of agent class and state)
+- The `soil.Simulation` class allows you to run multiple instances of the same experiment (i.e., multiple trials with the same parameters but a different randomness seed)
+- Reporting functions that aggregate multiple

docs/soil_tutorial.rst

@@ -211,11 +211,11 @@ nodes in that network. Notice how node 0 is the only one with a TV.
     sim = soil.Simulation(topology=G,
                                          num_trials=1,
                                          max_time=MAX_TIME,
-                                         environment_agents=[{'agent_type': NewsEnvironmentAgent,
+                                         environment_agents=[{'agent_class': NewsEnvironmentAgent,
                                                              'state': {
                                                                  'event_time': EVENT_TIME
                                                              }}],
-                                         network_agents=[{'agent_type': NewsSpread,
+                                         network_agents=[{'agent_class': NewsSpread,
                                                           'weight': 1}],
                                          states={0: {'has_tv': True}},
                                          default_state={'has_tv': False},
@@ -285,14 +285,14 @@ For this demo, we will use a python dictionary:
         },
         'network_agents': [
             {
-                'agent_type': NewsSpread,
+                'agent_class': NewsSpread,
                 'weight': 1,
                 'state': {
                     'has_tv': False
                 }
             },
             {
-                'agent_type': NewsSpread,
+                'agent_class': NewsSpread,
                 'weight': 2,
                 'state': {
                     'has_tv': True
@@ -300,7 +300,7 @@ For this demo, we will use a python dictionary:
             }
         ],
         'environment_agents':[
-            {'agent_type': NewsEnvironmentAgent,
+            {'agent_class': NewsEnvironmentAgent,
              'state': {
                  'event_time': 10
              }

examples/NewsSpread.ipynb

@@ -98,11 +98,11 @@
       "max_time: 30\r\n",
       "name: Sim_all_dumb\r\n",
       "network_agents:\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -122,19 +122,19 @@
       "max_time: 30\r\n",
       "name: Sim_half_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -154,12 +154,12 @@
       "max_time: 30\r\n",
       "name: Sim_all_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
@@ -181,12 +181,12 @@
       "max_time: 30\r\n",
       "name: Sim_wise_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -207,12 +207,12 @@
       "max_time: 30\r\n",
       "name: Sim_all_wise\r\n",
       "network_agents:\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",

examples/Untitled.ipynb

@@ -141,10 +141,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -1758,10 +1758,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -3363,10 +3363,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -4977,10 +4977,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -6591,10 +6591,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -8211,10 +8211,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -9828,10 +9828,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -11448,10 +11448,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -13062,10 +13062,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -14679,10 +14679,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -16296,10 +16296,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -17916,10 +17916,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -19521,10 +19521,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -21144,10 +21144,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -22767,10 +22767,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -24375,10 +24375,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -25992,10 +25992,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -27603,10 +27603,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -29220,10 +29220,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -30819,10 +30819,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -32439,10 +32439,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -34056,10 +34056,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -35676,10 +35676,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -37293,10 +37293,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -38913,10 +38913,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -40518,10 +40518,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -42129,10 +42129,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -43746,10 +43746,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -45357,10 +45357,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -46974,10 +46974,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -48588,10 +48588,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -50202,10 +50202,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -51819,10 +51819,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -53436,10 +53436,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -55041,10 +55041,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -56655,10 +56655,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -58257,10 +58257,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -59877,10 +59877,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -61494,10 +61494,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -63108,10 +63108,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -64713,10 +64713,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -66330,10 +66330,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -67947,10 +67947,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -69561,10 +69561,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -71178,10 +71178,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -72801,10 +72801,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -74418,10 +74418,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -76035,10 +76035,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -77643,10 +77643,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",
@@ -79260,10 +79260,10 @@
        "   'load_module': 'newsspread',\n",
        "   'max_time': 30,\n",
        "   'name': 'Sim_all_dumb',\n",
-       "   'network_agents': [{'agent_type': 'DumbViewer',\n",
+       "   'network_agents': [{'agent_class': 'DumbViewer',\n",
        "     'state': {'has_tv': False},\n",
        "     'weight': 1},\n",
-       "    {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
+       "    {'agent_class': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n",
        "   'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n",
        "   'num_trials': 50,\n",
        "   'seed': 'None',\n",

examples/complete.yml

@@ -1,14 +1,16 @@
 ---
 version: '2'
-general:
+name: simple
-  id: simple
+group: tests
-  group: tests
+dir_path: "/tmp/"
-  dir_path: "/tmp/"
+num_trials: 3
-  num_trials: 3
+max_steps: 100
-  max_time: 100
+interval: 1
-  interval: 1
+seed: "CompleteSeed!"
-  seed: "CompleteSeed!"
+model_class: Environment
-topologies:
+model_params:
+  am_i_complete: true
+  topologies:
     default:
       params:
         generator: complete_graph
@@ -17,29 +19,36 @@ topologies:
       params:
         generator: complete_graph
         n: 2
-environment:
+  environment:
-  environment_class: Environment
+  agents:
-  params:
-    am_i_complete: true
-agents:
-# Agents are split several groups, each with its own definition
-  default: # This is a special group. Its values will be used as default values for the rest of the groups
     agent_class: CounterModel
     topology: default
     state:
       times: 1
-  environment:
       # In this group we are not specifying any topology
     fixed:
       - name: 'Environment Agent 1'
-        agent_class: CounterModel
+        agent_class: BaseAgent
+        group: environment
+        topology: null
+        hidden: true
         state:
           times: 10
-  general_counters:
+      - agent_class: CounterModel
-    topology: default
+        id: 0
+        group: other_counters
+        topology: another_graph
+        state:
+          times: 1
+          total: 0
+      - agent_class: CounterModel
+        topology: another_graph
+        group: other_counters
+        id: 1
     distribution:
       - agent_class: CounterModel
         weight: 1
+        group: general_counters
         state:
           times: 3
       - agent_class: AggregatedCounter
@@ -50,16 +59,3 @@ agents:
         n: 2
         state:
           times: 5
-
-  other_counters:
-    topology: another_graph
-    fixed:
-      - agent_class: CounterModel
-        id: 0
-        state:
-          times: 1
-          total: 0
-      - agent_class: CounterModel
-        id: 1
-        # If not specified, it will use the state set in the default
-        # state:

examples/complete_opt2.yml

@@ -0,0 +1,63 @@
+---
+version: '2'
+id: simple
+group: tests
+dir_path: "/tmp/"
+num_trials: 3
+max_steps: 100
+interval: 1
+seed: "CompleteSeed!"
+model_class: "soil.Environment"
+model_params:
+  topologies:
+    default:
+      params:
+        generator: complete_graph
+        n: 10
+    another_graph:
+      params:
+        generator: complete_graph
+        n: 2
+  agents:
+    # The values here will be used as default values for any agent
+    agent_class: CounterModel
+    topology: default
+    state:
+      times: 1
+    # This specifies a distribution of agents, each with a `weight` or an explicit number of agents
+    distribution:
+      - agent_class: CounterModel
+        weight: 1
+        # This is inherited from the default settings
+        #topology: default
+        state:
+          times: 3
+      - agent_class: AggregatedCounter
+        topology: default
+        weight: 0.2
+    fixed:
+      - name: 'Environment Agent 1'
+        # All the other agents will assigned to the 'default' group
+        group: environment
+        # Do not count this agent towards total limits
+        hidden: true
+        agent_class: soil.BaseAgent
+        topology: null
+        state:
+          times: 10
+      - agent_class: CounterModel
+        topology: another_graph
+        id: 0
+        state:
+          times: 1
+          total: 0
+      - agent_class: CounterModel
+        topology: another_graph
+        id: 1
+    override:
+      # 2 agents that match this filter will be updated to match the state {times: 5}
+      - filter:
+          agent_class: AggregatedCounter
+        n: 2
+        state:
+            times: 5

examples/custom_generator/custom_generator.yml

@@ -2,7 +2,7 @@
 name: custom-generator
 description: Using a custom generator for the network
 num_trials: 3
-max_time: 100
+max_steps: 100
 interval: 1
 network_params:
   generator: mymodule.mygenerator
@@ -10,7 +10,7 @@ network_params:
   n: 10
   n_edges: 5  
 network_agents:
-  - agent_type: CounterModel
+  - agent_class: CounterModel
     weight: 1
     state:
       state_id: 0

examples/custom_generator/mymodule.py

@@ -1,6 +1,6 @@
 from networkx import Graph
+import random
 import networkx as nx
-from random import choice
 
 def mygenerator(n=5, n_edges=5):
     '''
@@ -14,9 +14,9 @@ def mygenerator(n=5, n_edges=5):
     
     for i in range(n_edges):
         nodes = list(G.nodes)
-        n_in = choice(nodes)
+        n_in = random.choice(nodes)
         nodes.remove(n_in)  # Avoid loops
-        n_out = choice(nodes)
+        n_out = random.choice(nodes)
         G.add_edge(n_in, n_out)
     return G
     

examples/custom_timeouts/custom_timeouts.py

@@ -27,8 +27,8 @@ if __name__ == '__main__':
     import logging
     logging.basicConfig(level=logging.INFO)
     from soil import Simulation
-    s = Simulation(network_agents=[{'ids': [0], 'agent_type': Fibonacci},
+    s = Simulation(network_agents=[{'ids': [0], 'agent_class': Fibonacci},
-                                   {'ids': [1], 'agent_type': Odds}],
+                                   {'ids': [1], 'agent_class': Odds}],
                    network_params={"generator": "complete_graph", "n": 2},
                    max_time=100,
                    )

examples/mesa/mesa.yml

@@ -3,18 +3,22 @@ name: mesa_sim
 group: tests
 dir_path: "/tmp"
 num_trials: 3
-max_time: 100
+max_steps: 100
 interval: 1
 seed: '1'
-network_params:
+model_class: social_wealth.MoneyEnv
+model_params:
+  topologies:
+    default: 
+      params:
         generator: social_wealth.graph_generator
         n: 5
-network_agents:
+  agents:
-  - agent_type: social_wealth.SocialMoneyAgent
+    distribution:
+      - agent_class: social_wealth.SocialMoneyAgent
+        topology: default
         weight: 1
-environment_class: social_wealth.MoneyEnv
+  mesa_agent_class: social_wealth.MoneyAgent
-environment_params:
-  mesa_agent_type: social_wealth.MoneyAgent
   N: 10
   width: 50
   height: 50

examples/mesa/server.py

@@ -70,7 +70,7 @@ model_params = {
         1,
         description="Choose how many agents to include in the model",
     ),
-    "network_agents": [{"agent_type": SocialMoneyAgent}],
+    "network_agents": [{"agent_class": SocialMoneyAgent}],
     "height": UserSettableParameter(
         "slider",
         "height",

examples/mesa/social_wealth.py

@@ -99,7 +99,7 @@ if __name__ == '__main__':
     G = graph_generator()
     fixed_params = {"topology": G,
                     "width": 10,
-                    "network_agents": [{"agent_type": SocialMoneyAgent,
+                    "network_agents": [{"agent_class": SocialMoneyAgent,
                                        'weight': 1}],
                     "height": 10}
 

examples/newsspread/NewsSpread.ipynb

@@ -89,11 +89,11 @@
       "max_time: 30\r\n",
       "name: Sim_all_dumb\r\n",
       "network_agents:\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -113,19 +113,19 @@
       "max_time: 30\r\n",
       "name: Sim_half_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -145,12 +145,12 @@
       "max_time: 30\r\n",
       "name: Sim_all_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
@@ -172,12 +172,12 @@
       "max_time: 30\r\n",
       "name: Sim_wise_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -198,12 +198,12 @@
       "max_time: 30\r\n",
       "name: Sim_all_wise\r\n",
       "network_agents:\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",

examples/newsspread/NewsSpread.yml

@@ -5,14 +5,14 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 300
+max_steps: 300
 name: Sim_all_dumb
 network_agents:
-- agent_type: newsspread.DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: false
   weight: 1
-- agent_type: newsspread.DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: true
   weight: 1
@@ -28,22 +28,22 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 300
+max_steps: 300
 name: Sim_half_herd
 network_agents:
-- agent_type: newsspread.DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: false
   weight: 1
-- agent_type: newsspread.DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: true
   weight: 1
-- agent_type: newsspread.HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: false
   weight: 1
-- agent_type: newsspread.HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
   weight: 1
@@ -59,15 +59,15 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 300
+max_steps: 300
 name: Sim_all_herd
 network_agents:
-- agent_type: newsspread.HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
     state_id: neutral
   weight: 1
-- agent_type: newsspread.HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
     state_id: neutral
@@ -85,15 +85,15 @@ environment_params:
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_time: 300
+max_steps: 300
 name: Sim_wise_herd
 network_agents:
-- agent_type: newsspread.HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
     state_id: neutral
   weight: 1
-- agent_type: newsspread.WiseViewer
+- agent_class: newsspread.WiseViewer
   state:
     has_tv: true
   weight: 1
@@ -110,15 +110,15 @@ environment_params:
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_time: 300
+max_steps: 300
 name: Sim_all_wise
 network_agents:
-- agent_type: newsspread.WiseViewer
+- agent_class: newsspread.WiseViewer
   state:
     has_tv: true
     state_id: neutral
   weight: 1
-- agent_type: newsspread.WiseViewer
+- agent_class: newsspread.WiseViewer
   state:
     has_tv: true
   weight: 1

examples/newsspread/newsspread.py

@@ -16,13 +16,13 @@ class DumbViewer(FSM, NetworkAgent):
     @state
     def neutral(self):
         if self['has_tv']:
-            if prob(self.env['prob_tv_spread']):
+            if self.prob(self.model['prob_tv_spread']):
                 return self.infected
 
     @state
     def infected(self):
         for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
-            if prob(self.env['prob_neighbor_spread']):
+            if self.prob(self.model['prob_neighbor_spread']):
                 neighbor.infect()
 
     def infect(self):
@@ -44,9 +44,9 @@ class HerdViewer(DumbViewer):
         '''Notice again that this is NOT a state. See DumbViewer.infect for reference'''
         infected = self.count_neighboring_agents(state_id=self.infected.id)
         total = self.count_neighboring_agents()
-        prob_infect = self.env['prob_neighbor_spread'] * infected/total
+        prob_infect = self.model['prob_neighbor_spread'] * infected/total
         self.debug('prob_infect', prob_infect)
-        if prob(prob_infect):
+        if self.prob(prob_infect):
             self.set_state(self.infected)
 
 
@@ -63,9 +63,9 @@ class WiseViewer(HerdViewer):
 
     @state
     def cured(self):
-        prob_cure = self.env['prob_neighbor_cure']
+        prob_cure = self.model['prob_neighbor_cure']
         for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
-            if prob(prob_cure):
+            if self.prob(prob_cure):
                 try:
                     neighbor.cure()
                 except AttributeError:
@@ -80,7 +80,7 @@ class WiseViewer(HerdViewer):
                     1.0)
         infected = max(self.count_neighboring_agents(self.infected.id),
                        1.0)
-        prob_cure = self.env['prob_neighbor_cure'] * (cured/infected)
+        prob_cure = self.model['prob_neighbor_cure'] * (cured/infected)
-        if prob(prob_cure):
+        if self.prob(prob_cure):
             return self.cured
         return self.set_state(super().infected)

examples/programmatic/programmatic.py

@@ -27,7 +27,7 @@ s = Simulation(name='Programmatic',
                network_params={'generator': mygenerator},
                num_trials=1,
                max_time=100,
-               agent_type=MyAgent,
+               agent_class=MyAgent,
                dry_run=True)
 
 

examples/pubcrawl/pubcrawl.py

@@ -1,6 +1,5 @@
 from soil.agents import FSM, NetworkAgent, state, default_state
 from soil import Environment
-from random import random, shuffle
 from itertools import islice
 import logging
 
@@ -61,12 +60,10 @@ class Patron(FSM, NetworkAgent):
     '''
     level = logging.DEBUG
 
-    defaults = {
+    pub = None
-        'pub': None,
+    drunk = False
-        'drunk': False,
+    pints = 0
-        'pints': 0,
+    max_pints = 3
-        'max_pints': 3,
-    }
 
     @default_state
     @state
@@ -90,9 +87,9 @@ class Patron(FSM, NetworkAgent):
             return self.sober_in_pub
         self.debug('I am looking for a pub')
         group = list(self.get_neighboring_agents())
-        for pub in self.env.available_pubs():
+        for pub in self.model.available_pubs():
             self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group)))
-            if self.env.enter(pub, self, *group):
+            if self.model.enter(pub, self, *group):
                 self.info('We\'re all {} getting in {}!'.format(len(group), pub))
                 return self.sober_in_pub
 
@@ -128,8 +125,8 @@ class Patron(FSM, NetworkAgent):
         Try to become friends with another agent. The chances of
         success depend on both agents' openness.
         '''
-        if force or self['openness'] > random():
+        if force or self['openness'] > self.random.random():
-            self.env.add_edge(self, other_agent)
+            self.model.add_edge(self, other_agent)
             self.info('Made some friend {}'.format(other_agent))
             return True
         return False
@@ -138,7 +135,7 @@ class Patron(FSM, NetworkAgent):
         ''' Look for random agents around me and try to befriend them'''
         befriended = False
         k = int(10*self['openness'])
-        shuffle(others)
+        self.random.shuffle(others)
         for friend in islice(others, k):  # random.choice >= 3.7
             if friend == self:
                 continue
@@ -151,7 +148,7 @@ class Patron(FSM, NetworkAgent):
         return befriended
 
 
-class Police(FSM, NetworkAgent):
+class Police(FSM):
     '''Simple agent to take drunk people out of pubs.'''
     level = logging.INFO
 

examples/pubcrawl/pubcrawl.yml

@@ -1,25 +1,25 @@
 ---
 name: pubcrawl
 num_trials: 3
-max_time: 10
+max_steps: 10
 dump: false
 network_params:
   # Generate 100 empty nodes. They will be assigned a network agent
   generator: empty_graph
   n: 30
 network_agents:
-  - agent_type: pubcrawl.Patron
+  - agent_class: pubcrawl.Patron
     description: Extroverted patron
     state:
       openness: 1.0
     weight: 9
-  - agent_type: pubcrawl.Patron
+  - agent_class: pubcrawl.Patron
     description: Introverted patron
     state:
       openness: 0.1
     weight: 1
 environment_agents:
-  - agent_type: pubcrawl.Police
+  - agent_class: pubcrawl.Police
 environment_class: pubcrawl.CityPubs
 environment_params:
   altercations: 0

examples/rabbits/README.md

@@ -0,0 +1,4 @@
+There are two similar implementations of this simulation.
+
+- `basic`. Using simple primites
+- `improved`. Using more advanced features such as the `time` module to avoid unnecessary computations (i.e., skip steps), and generator functions.

examples/rabbits/basic/rabbit_agents.py

@@ -0,0 +1,130 @@
+from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
+from soil.time import Delta
+from enum import Enum
+from collections import Counter
+import logging
+import math
+
+
+class RabbitModel(FSM, NetworkAgent):
+
+    sexual_maturity = 30
+    life_expectancy = 300
+
+    @default_state
+    @state
+    def newborn(self):
+        self.info('I am a newborn.')
+        self.age = 0
+        self.offspring = 0
+        return self.youngling
+
+    @state
+    def youngling(self):
+        self.age += 1
+        if self.age >= self.sexual_maturity:
+            self.info(f'I am fertile! My age is {self.age}')
+            return self.fertile
+
+    @state
+    def fertile(self):
+        raise Exception("Each subclass should define its fertile state")
+
+    @state
+    def dead(self):
+        self.die()
+
+
+class Male(RabbitModel):
+    max_females = 5
+    mating_prob = 0.001
+
+    @state
+    def fertile(self):
+        self.age += 1
+
+        if self.age > self.life_expectancy:
+            return self.dead
+
+        # Males try to mate
+        for f in self.model.agents(agent_class=Female,
+                                   state_id=Female.fertile.id,
+                                   limit=self.max_females):
+            self.debug('FOUND A FEMALE: ', repr(f), self.mating_prob)
+            if self.prob(self['mating_prob']):
+                f.impregnate(self)
+                break  # Take a break
+
+
+class Female(RabbitModel):
+    gestation = 100
+
+    @state
+    def fertile(self):
+        # Just wait for a Male
+        self.age += 1
+        if self.age > self.life_expectancy:
+            return self.dead
+
+    def impregnate(self, male):
+        self.info(f'{repr(male)} impregnating female {repr(self)}')
+        self.mate = male
+        self.pregnancy = -1
+        self.set_state(self.pregnant, when=self.now)
+        self.number_of_babies = int(8+4*self.random.random())
+        self.debug('I am pregnant')
+
+    @state
+    def pregnant(self):
+        self.age += 1
+        self.pregnancy += 1
+
+        if self.prob(self.age / self.life_expectancy):
+            return self.die()
+
+        if self.pregnancy >= self.gestation:
+            self.info('Having {} babies'.format(self.number_of_babies))
+            for i in range(self.number_of_babies):
+                state = {}
+                agent_class = self.random.choice([Male, Female])
+                child = self.model.add_node(agent_class=agent_class,
+                                            topology=self.topology,
+                                            **state)
+                child.add_edge(self)
+                try:
+                    child.add_edge(self.mate)
+                    self.model.agents[self.mate].offspring += 1
+                except ValueError:
+                    self.debug('The father has passed away')
+
+                self.offspring += 1
+            self.mate = None
+            return self.fertile
+
+    @state
+    def dead(self):
+        super().dead()
+        if 'pregnancy' in self and self['pregnancy'] > -1:
+            self.info('A mother has died carrying a baby!!')
+
+
+class RandomAccident(BaseAgent):
+
+    level = logging.INFO
+
+    def step(self):
+        rabbits_alive = self.model.topology.number_of_nodes()
+
+        if not rabbits_alive:
+            return self.die()
+
+        prob_death = self.model.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
+        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
+        for i in self.iter_agents(agent_class=RabbitModel):
+            if i.state.id == i.dead.id:
+                continue
+            if self.prob(prob_death):
+                self.info('I killed a rabbit: {}'.format(i.id))
+                rabbits_alive -= 1
+                i.set_state(i.dead)
+        self.debug('Rabbits alive: {}'.format(rabbits_alive))

examples/rabbits/basic/rabbits.yml

@@ -0,0 +1,41 @@
+---
+version: '2'
+name: rabbits_basic
+num_trials: 1
+seed: MySeed
+description: null
+group: null
+interval: 1.0
+max_time: 100
+model_class: soil.environment.Environment
+model_params:
+  agents:
+    topology: default
+    agent_class: rabbit_agents.RabbitModel
+    distribution:
+    - agent_class: rabbit_agents.Male
+      topology: default
+      weight: 1
+    - agent_class: rabbit_agents.Female
+      topology: default
+      weight: 1
+    fixed:
+    - agent_class: rabbit_agents.RandomAccident
+      topology: null
+      hidden: true
+      state:
+        group: environment
+    state:
+      group: network
+      mating_prob: 0.1
+  prob_death: 0.001
+  topologies:
+    default:
+      topology:
+        directed: true
+        links: []
+        nodes:
+        - id: 1
+        - id: 0
+extra:
+  visualization_params: {}

examples/rabbits/improved/rabbit_agents.py

@@ -0,0 +1,130 @@
+from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
+from soil.time import Delta, When, NEVER
+from enum import Enum
+import logging
+import math
+
+
+class RabbitModel(FSM, NetworkAgent):
+
+    mating_prob = 0.005
+    offspring = 0
+    birth = None
+
+    sexual_maturity = 3
+    life_expectancy = 30
+
+    @default_state
+    @state
+    def newborn(self):
+        self.birth = self.now
+        self.info(f'I am a newborn.')
+        self.model['rabbits_alive'] = self.model.get('rabbits_alive', 0) + 1
+
+        # Here we can skip the `youngling` state by using a coroutine/generator. 
+        while self.age < self.sexual_maturity:
+            interval = self.sexual_maturity - self.age
+            yield Delta(interval)
+
+        self.info(f'I am fertile! My age is {self.age}')
+        return self.fertile
+
+    @property
+    def age(self):
+        return self.now - self.birth
+
+    @state
+    def fertile(self):
+        raise Exception("Each subclass should define its fertile state")
+
+    def step(self):
+        super().step()
+        if self.prob(self.age / self.life_expectancy):
+            return self.die()
+
+
+class Male(RabbitModel):
+
+    max_females = 5
+
+    @state
+    def fertile(self):
+        # Males try to mate
+        for f in self.model.agents(agent_class=Female,
+                                   state_id=Female.fertile.id,
+                                   limit=self.max_females):
+            self.debug('Found a female:', repr(f))
+            if self.prob(self['mating_prob']):
+                f.impregnate(self)
+                break  # Take a break, don't try to impregnate the rest
+
+            
+class Female(RabbitModel):
+    due_date = None
+    age_of_pregnancy = None
+    gestation = 10
+    mate = None
+
+    @state
+    def fertile(self):
+        return self.fertile, NEVER
+
+    @state
+    def pregnant(self):
+        self.info('I am pregnant')
+        if self.age > self.life_expectancy:
+            return self.dead
+
+        self.due_date = self.now + self.gestation
+
+        number_of_babies = int(8+4*self.random.random())
+
+        while self.now < self.due_date:
+            yield When(self.due_date)
+
+        self.info('Having {} babies'.format(number_of_babies))
+        for i in range(number_of_babies):
+            agent_class = self.random.choice([Male, Female])
+            child = self.model.add_node(agent_class=agent_class,
+                                        topology=self.topology)
+            self.model.add_edge(self, child)
+            self.model.add_edge(self.mate, child)
+            self.offspring += 1
+            self.model.agents[self.mate].offspring += 1
+        self.mate = None
+        self.due_date = None
+        return self.fertile
+
+    @state
+    def dead(self):
+        super().dead()
+        if self.due_date is not None:
+            self.info('A mother has died carrying a baby!!')
+
+    def impregnate(self, male):
+        self.info(f'{repr(male)} impregnating female {repr(self)}')
+        self.mate = male
+        self.set_state(self.pregnant, when=self.now)
+
+
+class RandomAccident(BaseAgent):
+
+    level = logging.INFO
+
+    def step(self):
+        rabbits_total = self.model.topology.number_of_nodes()
+        if 'rabbits_alive' not in self.model:
+            self.model['rabbits_alive'] = 0
+        rabbits_alive = self.model.get('rabbits_alive', rabbits_total)
+        prob_death = self.model.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
+        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
+        for i in self.model.network_agents:
+            if i.state.id == i.dead.id:
+                continue
+            if self.prob(prob_death):
+                self.info('I killed a rabbit: {}'.format(i.id))
+                rabbits_alive = self.model['rabbits_alive'] = rabbits_alive -1
+                i.set_state(i.dead)
+        self.debug('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
+        if self.model.count_agents(state_id=RabbitModel.dead.id) == self.model.topology.number_of_nodes():
+            self.die()

examples/rabbits/improved/rabbits.yml

@@ -0,0 +1,41 @@
+---
+version: '2'
+name: rabbits_improved
+num_trials: 1
+seed: MySeed
+description: null
+group: null
+interval: 1.0
+max_time: 100
+model_class: soil.environment.Environment
+model_params:
+  agents:
+    topology: default
+    agent_class: rabbit_agents.RabbitModel
+    distribution:
+    - agent_class: rabbit_agents.Male
+      topology: default
+      weight: 1
+    - agent_class: rabbit_agents.Female
+      topology: default
+      weight: 1
+    fixed:
+    - agent_class: rabbit_agents.RandomAccident
+      topology: null
+      hidden: true
+      state:
+        group: environment
+    state:
+      group: network
+      mating_prob: 0.1
+  prob_death: 0.001
+  topologies:
+    default:
+      topology:
+        directed: true
+        links: []
+        nodes:
+        - id: 1
+        - id: 0
+extra:
+  visualization_params: {}

examples/rabbits/rabbit_agents.py

@@ -1,135 +0,0 @@
-from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
-from enum import Enum
-from random import random, choice
-import logging
-import math
-
-
-class Genders(Enum):
-    male = 'male'
-    female = 'female'
-
-
-class RabbitModel(FSM, NetworkAgent):
-
-    defaults = {
-        'age': 0,
-        'gender': Genders.male.value,
-        'mating_prob': 0.001,
-        'offspring': 0,
-    }
-
-    sexual_maturity = 3 #4*30
-    life_expectancy = 365 * 3
-    gestation = 33
-    pregnancy = -1
-    max_females = 5
-
-    @default_state
-    @state
-    def newborn(self):
-        self.debug(f'I am a newborn at age {self["age"]}')
-        self['age'] += 1
-
-        if self['age'] >= self.sexual_maturity:
-            self.debug('I am fertile!')
-            return self.fertile
-    @state
-    def fertile(self):
-        raise Exception("Each subclass should define its fertile state")
-
-    @state
-    def dead(self):
-        self.info('Agent {} is dying'.format(self.id))
-        self.die()
-
-
-class Male(RabbitModel):
-
-    @state
-    def fertile(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        if self['gender'] == Genders.female.value:
-            return
-
-        # Males try to mate
-        for f in self.get_agents(state_id=Female.fertile.id,
-                                 agent_type=Female,
-                                 limit_neighbors=False,
-                                 limit=self.max_females):
-            r = random()
-            if r < self['mating_prob']:
-                self.impregnate(f)
-                break # Take a break
-    def impregnate(self, whom):
-        whom['pregnancy'] = 0
-        whom['mate'] = self.id
-        whom.set_state(whom.pregnant)
-        self.debug('{} impregnating: {}. {}'.format(self.id, whom.id, whom.state))
-
-class Female(RabbitModel):
-    @state
-    def fertile(self):
-        # Just wait for a Male
-        pass
-
-    @state
-    def pregnant(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        self['pregnancy'] += 1
-        self.debug('Pregnancy: {}'.format(self['pregnancy']))
-        if self['pregnancy'] >= self.gestation:
-            number_of_babies = int(8+4*random())
-            self.info('Having {} babies'.format(number_of_babies))
-            for i in range(number_of_babies):
-                state = {}
-                state['gender'] = choice(list(Genders)).value
-                child = self.env.add_node(self.__class__, state)
-                self.env.add_edge(self.id, child.id)
-                self.env.add_edge(self['mate'], child.id)
-                # self.add_edge()
-                self.debug('A BABY IS COMING TO LIFE')
-                self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.topology.number_of_nodes())+1
-                self.debug('Rabbits alive: {}'.format(self.env['rabbits_alive']))
-                self['offspring'] += 1
-                self.env.get_agent(self['mate'])['offspring'] += 1
-            del self['mate']
-            self['pregnancy'] = -1
-            return self.fertile
-
-    @state
-    def dead(self):
-        super().dead()
-        if 'pregnancy' in self and self['pregnancy'] > -1:
-            self.info('A mother has died carrying a baby!!')
-
-
-class RandomAccident(BaseAgent):
-
-    level = logging.DEBUG
-
-    def step(self):
-        rabbits_total = self.env.topology.number_of_nodes()
-        if 'rabbits_alive' not in self.env:
-            self.env['rabbits_alive'] = 0
-        rabbits_alive = self.env.get('rabbits_alive', rabbits_total)
-        prob_death = self.env.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
-        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
-        for i in self.env.network_agents:
-            if i.state['id'] == i.dead.id:
-                continue
-            r = random()
-            if r < prob_death:
-                self.debug('I killed a rabbit: {}'.format(i.id))
-                rabbits_alive = self.env['rabbits_alive'] = rabbits_alive -1
-                self.log('Rabbits alive: {}'.format(self.env['rabbits_alive']))
-                i.set_state(i.dead)
-        self.log('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
-        if self.env.count_agents(state_id=RabbitModel.dead.id) == self.env.topology.number_of_nodes():
-            self.die()

examples/rabbits/rabbits.yml

@@ -1,20 +0,0 @@
----
-name: rabbits_example
-max_time: 100
-interval: 1
-seed: MySeed
-agent_type: rabbit_agents.RabbitModel
-environment_agents:
-    - agent_type: rabbit_agents.RandomAccident
-environment_params:
-  prob_death: 0.001
-default_state:
-  mating_prob: 0.1
-topology:
-  nodes:
-    - id: 1
-      agent_type: rabbit_agents.Male
-    - id: 0
-      agent_type: rabbit_agents.Female
-  directed: true
-  links: []

examples/random_delays/random_delays.py

@@ -4,7 +4,6 @@ Example of a fully programmatic simulation, without definition files.
 '''
 from soil import Simulation, agents
 from soil.time import Delta
-from random import expovariate
 import logging
 
 
@@ -20,7 +19,7 @@ class MyAgent(agents.FSM):
     @agents.state
     def ping(self):
         self.info('Ping')
-        return self.pong, Delta(expovariate(1/16))
+        return self.pong, Delta(self.random.expovariate(1/16))
 
     @agents.state
     def pong(self):
@@ -29,15 +28,15 @@ class MyAgent(agents.FSM):
         self.info(str(self.pong_counts))
         if self.pong_counts < 1:
             return self.die()
-        return None, Delta(expovariate(1/16))
+        return None, Delta(self.random.expovariate(1/16))
 
 
 s = Simulation(name='Programmatic',
-               network_agents=[{'agent_type': MyAgent, 'id': 0}],
+               network_agents=[{'agent_class': MyAgent, 'id': 0}],
                topology={'nodes': [{'id': 0}], 'links': []},
                num_trials=1,
                max_time=100,
-               agent_type=MyAgent,
+               agent_class=MyAgent,
                dry_run=True)
 
 

examples/template.yml

@@ -6,20 +6,20 @@ template:
   group: simple
   num_trials: 1
   interval: 1
-  max_time: 2
+  max_steps: 2
   seed: "CompleteSeed!"
   dump: false
+  model_params:
     network_params:
       generator: complete_graph
       n: 10
     network_agents:
-    - agent_type: CounterModel
+      - agent_class: CounterModel
         weight: "{{ x1 }}"
         state:
           state_id: 0
-    - agent_type: AggregatedCounter
+      - agent_class: AggregatedCounter
         weight: "{{ 1 - x1 }}"
-  environment_params:
     name: "{{ x3 }}"
   skip_test: true
 vars:

examples/terrorism/TerroristNetworkModel.py

@@ -1,4 +1,3 @@
-import random
 import networkx as nx
 from soil.agents import Geo, NetworkAgent, FSM, state, default_state
 from soil import Environment
@@ -26,26 +25,26 @@ class TerroristSpreadModel(FSM, Geo):
         self.prob_interaction = model.environment_params['prob_interaction']
 
         if self['id'] == self.civilian.id:       # Civilian
-            self.mean_belief = random.uniform(0.00, 0.5)
+            self.mean_belief = self.random.uniform(0.00, 0.5)
         elif self['id'] == self.terrorist.id:     # Terrorist
-            self.mean_belief = random.uniform(0.8, 1.00)
+            self.mean_belief = self.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 model.environment_params:
-            self.vulnerability = random.uniform( model.environment_params['min_vulnerability'], model.environment_params['max_vulnerability'] )
+            self.vulnerability = self.random.uniform( model.environment_params['min_vulnerability'], model.environment_params['max_vulnerability'] )
         else :
-            self.vulnerability = random.uniform( 0, model.environment_params['max_vulnerability'] )
+            self.vulnerability = self.random.uniform( 0, model.environment_params['max_vulnerability'] )
 
 
     @state
     def civilian(self):
-        neighbours = list(self.get_neighboring_agents(agent_type=TerroristSpreadModel))
+        neighbours = list(self.get_neighboring_agents(agent_class=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)
+            interactions = list(n for n in neighbours if self.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 )
@@ -64,7 +63,7 @@ class TerroristSpreadModel(FSM, Geo):
     @state
     def terrorist(self):
         neighbours = self.get_agents(state_id=[self.terrorist.id, self.leader.id],
-                                     agent_type=TerroristSpreadModel,
+                                     agent_class=TerroristSpreadModel,
                                      limit_neighbors=True)
         if len(neighbours) > 0:
             influence = sum( self.degree(n) for n in neighbours )
@@ -82,6 +81,26 @@ class TerroristSpreadModel(FSM, Geo):
                     return
             return self.leader
 
+    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.model, '_degree')) or getattr(self.model, '_last_step', 0) < self.now:
+            self.model._degree = nx.degree_centrality(self.G)
+            self.model._last_step = self.now
+        return self.model._degree[node]
+
+    def betweenness(self, node, force=False):
+        node = as_node(node)
+        if force or (not hasattr(self.model, '_betweenness')) or getattr(self.model, '_last_step', 0) < self.now:
+            self.model._betweenness = nx.betweenness_centrality(self.G)
+            self.model._last_step = self.now
+        return self.model._betweenness[node]
+
 
 class TrainingAreaModel(FSM, Geo):
     """
@@ -103,7 +122,7 @@ class TrainingAreaModel(FSM, Geo):
     @default_state
     @state
     def terrorist(self):
-        for neighbour in self.get_neighboring_agents(agent_type=TerroristSpreadModel):
+        for neighbour in self.get_neighboring_agents(agent_class=TerroristSpreadModel):
             if neighbour.vulnerability > self.min_vulnerability:
                 neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )        
 
@@ -129,7 +148,7 @@ class HavenModel(FSM, Geo):
         self.max_vulnerability = model.environment_params['max_vulnerability']
 
     def get_occupants(self, **kwargs):
-        return self.get_neighboring_agents(agent_type=TerroristSpreadModel, **kwargs)
+        return self.get_neighboring_agents(agent_class=TerroristSpreadModel, **kwargs)
 
     @state
     def civilian(self):
@@ -182,27 +201,27 @@ class TerroristNetworkModel(TerroristSpreadModel):
 
     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))
+            close_ups = set(self.geo_search(radius=self.vision_range, agent_class=TerroristNetworkModel))
-            step_neighbours = set(self.ego_search(self.sphere_influence, agent_type=TerroristNetworkModel, center=False))
+            step_neighbours = set(self.ego_search(self.sphere_influence, agent_class=TerroristNetworkModel, center=False))
-            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_type=TerroristNetworkModel))
+            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_class=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:
+                if agent['id'] == agent.civilian.id and self.random.random() < prob_new_interaction:
                     self.add_edge(agent)
                     break
 
     def get_distance(self, target):
-        source_x, source_y = nx.get_node_attributes(self.topology, 'pos')[self.id]
+        source_x, source_y = nx.get_node_attributes(self.G, 'pos')[self.id]
-        target_x, target_y = nx.get_node_attributes(self.topology, 'pos')[target]
+        target_x, target_y = nx.get_node_attributes(self.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, target):
         try:
-            return nx.shortest_path_length(self.topology, self.id, target)
+            return nx.shortest_path_length(self.G, self.id, target)
         except nx.NetworkXNoPath:
             return float('inf')

examples/terrorism/TerroristNetworkModel.yml

@@ -1,31 +1,31 @@
 name: TerroristNetworkModel_sim
-max_time: 150 
+max_steps: 150 
 num_trials: 1
-network_params:
+model_params:
+  network_params:
     generator: random_geometric_graph
     radius: 0.2
     # generator: geographical_threshold_graph
     # theta: 20
     n: 100
-network_agents:
+  network_agents:
-  - agent_type: TerroristNetworkModel.TerroristNetworkModel
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
       weight: 0.8
       state:
         id: civilian  # Civilians
-  - agent_type: TerroristNetworkModel.TerroristNetworkModel
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
       weight: 0.1
       state:
         id: leader    # Leaders
-  - agent_type: TerroristNetworkModel.TrainingAreaModel
+    - agent_class: TerroristNetworkModel.TrainingAreaModel
       weight: 0.05
       state:
         id: terrorist # Terrorism
-  - agent_type: TerroristNetworkModel.HavenModel
+    - agent_class: TerroristNetworkModel.HavenModel
       weight: 0.05
       state:
         id: civilian  # Civilian
 
-environment_params:
   # TerroristSpreadModel
   information_spread_intensity: 0.7
   terrorist_additional_influence: 0.035

examples/torvalds.yml

@@ -1,13 +1,14 @@
 ---
 name: torvalds_example
-max_time: 10
+max_steps: 10
 interval: 2
-agent_type: CounterModel
+model_params:
-default_state:
+  agent_class: CounterModel
+  default_state:
     skill_level: 'beginner'
-network_params:
+  network_params:
     path: 'torvalds.edgelist'
-states:
+  states:
     Torvalds:
       skill_level: 'God'
     balkian:

examples/tutorial/soil_tutorial.html

@@ -12330,11 +12330,11 @@ Notice how node 0 is the only one with a TV.</p>
 <span class="n">sim</span> <span class="o">=</span> <span class="n">soil</span><span class="o">.</span><span class="n">Simulation</span><span class="p">(</span><span class="n">topology</span><span class="o">=</span><span class="n">G</span><span class="p">,</span>
                                      <span class="n">num_trials</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                      <span class="n">max_time</span><span class="o">=</span><span class="n">MAX_TIME</span><span class="p">,</span>
-                                     <span class="n">environment_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
+                                     <span class="n">environment_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
                                                          <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                                                              <span class="s1">&#39;event_time&#39;</span><span class="p">:</span> <span class="n">EVENT_TIME</span>
                                                          <span class="p">}}],</span>
-                                     <span class="n">network_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+                                     <span class="n">network_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
                                                       <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}],</span>
                                      <span class="n">states</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">}},</span>
                                      <span class="n">default_state</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">},</span>
@@ -12468,14 +12468,14 @@ For this demo, we will use a python dictionary:</p>
     <span class="p">},</span>
     <span class="s1">&#39;network_agents&#39;</span><span class="p">:</span> <span class="p">[</span>
         <span class="p">{</span>
-            <span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+            <span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
             <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
             <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                 <span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">False</span>
             <span class="p">}</span>
         <span class="p">},</span>
         <span class="p">{</span>
-            <span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+            <span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
             <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
             <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                 <span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">True</span>
@@ -12483,7 +12483,7 @@ For this demo, we will use a python dictionary:</p>
         <span class="p">}</span>
     <span class="p">],</span>
     <span class="s1">&#39;environment_agents&#39;</span><span class="p">:[</span>
-        <span class="p">{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
+        <span class="p">{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
          <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
              <span class="s1">&#39;event_time&#39;</span><span class="p">:</span> <span class="mi">10</span>
          <span class="p">}</span>

examples/tutorial/soil_tutorial.ipynb

@@ -459,11 +459,11 @@
     "sim = soil.Simulation(topology=G,\n",
     "                      num_trials=1,\n",
     "                      max_time=MAX_TIME,\n",
-    "                      environment_agents=[{'agent_type': NewsEnvironmentAgent,\n",
+    "                      environment_agents=[{'agent_class': NewsEnvironmentAgent,\n",
     "                                           'state': {\n",
     "                                           'event_time': EVENT_TIME\n",
     "                                         }}],\n",
-    "                      network_agents=[{'agent_type': NewsSpread,\n",
+    "                      network_agents=[{'agent_class': NewsSpread,\n",
     "                                      'weight': 1}],\n",
     "                      states={0: {'has_tv': True}},\n",
     "                      default_state={'has_tv': False},\n",
@@ -588,14 +588,14 @@
     "    },\n",
     "    'network_agents': [\n",
     "        {\n",
-    "            'agent_type': NewsSpread,\n",
+    "            'agent_class': NewsSpread,\n",
     "            'weight': 1,\n",
     "            'state': {\n",
     "                'has_tv': False\n",
     "            }\n",
     "        },\n",
     "        {\n",
-    "            'agent_type': NewsSpread,\n",
+    "            'agent_class': NewsSpread,\n",
     "            'weight': 2,\n",
     "            'state': {\n",
     "                'has_tv': True\n",
@@ -603,7 +603,7 @@
     "        }\n",
     "    ],\n",
     "    'environment_agents':[\n",
-    "        {'agent_type': NewsEnvironmentAgent,\n",
+    "        {'agent_class': NewsEnvironmentAgent,\n",
     "         'state': {\n",
     "             'event_time': 10\n",
     "         }\n",

requirements.txt

@@ -2,8 +2,9 @@ networkx>=2.5
 numpy
 matplotlib
 pyyaml>=5.1
-pandas>=0.23
+pandas>=1
 SALib>=1.3
 Jinja2
-Mesa>=0.8.9
+Mesa>=1
 pydantic>=1.9
+sqlalchemy>=1.4

soil/__init__.py

@@ -1,8 +1,10 @@
+from __future__ import annotations
+
 import importlib
 import sys
 import os
-import pdb
 import logging
+import traceback
 
 from .version import __version__
 
@@ -16,11 +18,10 @@ from . import agents
 from .simulation import *
 from .environment import Environment
 from . import serialization
-from . import analysis
 from .utils import logger
 from .time import *
 
-def main():
+def main(cfg='simulation.yml', **kwargs):
     import argparse
     from . import simulation
 
@@ -29,7 +30,7 @@ def main():
     parser = argparse.ArgumentParser(description='Run a SOIL simulation')
     parser.add_argument('file', type=str,
                         nargs="?",
-                        default='simulation.yml',
+                        default=cfg,
                         help='Configuration file for the simulation (e.g., YAML or JSON)')
     parser.add_argument('--version', action='store_true',
                         help='Show version info and exit')
@@ -39,6 +40,8 @@ def main():
                         help='Do not store the results of the simulation to disk, show in terminal instead.')
     parser.add_argument('--pdb', action='store_true',
                         help='Use a pdb console in case of exception.')
+    parser.add_argument('--debug', action='store_true',
+                        help='Run a customized version of a pdb console to debug a simulation.')
     parser.add_argument('--graph', '-g', action='store_true',
                         help='Dump each trial\'s network topology as a GEXF graph. Defaults to false.')
     parser.add_argument('--csv', action='store_true',
@@ -51,9 +54,22 @@ def main():
                         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')
+    parser.add_argument('--only-convert', '--convert', action='store_true',
+                        help='Do not run the simulation, only convert the configuration file(s) and output them.')
+
+
+    parser.add_argument("--set",
+                        metavar="KEY=VALUE",
+                        action='append',
+                        help="Set a number of parameters that will be passed to the simulation."
+                        "(do not put spaces before or after the = sign). "
+                        "If a value contains spaces, you should define "
+                        "it with double quotes: "
+                        'foo="this is a sentence". Note that '
+                        "values are always treated as strings.")
 
     args = parser.parse_args()
-    logging.basicConfig(level=getattr(logging, (args.level or 'INFO').upper()))
+    logger.setLevel(getattr(logging, (args.level or 'INFO').upper()))
 
     if args.version:
         return
@@ -65,9 +81,10 @@ def main():
 
     logger.info('Loading config file: {}'.format(args.file))
 
-    if args.pdb:
+    if args.pdb or args.debug:
         args.synchronous = True
-
+    if args.debug:
+        os.environ['SOIL_DEBUG'] = 'true'
 
     try:
         exporters = list(args.exporter or ['default', ])
@@ -82,18 +99,48 @@ def main():
         if not os.path.exists(args.file):
             logger.error('Please, input a valid file')
             return
-        simulation.run_from_config(args.file,
+        for sim in simulation.iter_from_config(args.file):
-                                   dry_run=args.dry_run,
+            if args.set:
+                for s in args.set:
+                    k, v = s.split('=', 1)[:2]
+                    v = eval(v)
+                    tail, *head = k.rsplit('.', 1)[::-1]
+                    target = sim
+                    if head:
+                        for part in head[0].split('.'):
+                            try:
+                                target = getattr(target, part)
+                            except AttributeError:
+                                target = target[part]
+                    try:
+                        setattr(target, tail, v)
+                    except AttributeError:
+                        target[tail] = v
+
+                if args.only_convert:
+                    print(sim.to_yaml())
+                    continue
+
+            sim.run_simulation(dry_run=args.dry_run,
                                exporters=exporters,
                                parallel=(not args.synchronous),
                                outdir=args.output,
-                                   exporter_params=exp_params)
+                               exporter_params=exp_params,
-    except Exception:
+                               **kwargs)
+
+    except Exception as ex:
         if args.pdb:
-            pdb.post_mortem()
+            from .debugging import post_mortem
+            print(traceback.format_exc())
+            post_mortem()
         else:
             raise
 
-
+def easy(cfg, debug=False):
+    sim = simulation.from_config(cfg)
+    if debug or os.environ.get('SOIL_DEBUG'):
+        from .debugging import setup
+        setup(sys._getframe().f_back)
+    return sim
 if __name__ == '__main__':
     main()

soil/agents/BassModel.py

@@ -1,4 +1,3 @@
-import random
 from . import FSM, state, default_state
 
 
@@ -16,13 +15,13 @@ class BassModel(FSM):
     @default_state
     @state
     def innovation(self):
-        if random.random() < self.innovation_prob:
+        if self.prob(self.innovation_prob):
             self.sentimentCorrelation = 1
             return self.aware
         else:
             aware_neighbors = self.get_neighboring_agents(state_id=self.aware.id)
             num_neighbors_aware = len(aware_neighbors)
-            if random.random() < (self['imitation_prob']*num_neighbors_aware):
+            if self.prob((self['imitation_prob']*num_neighbors_aware)):
                 self.sentimentCorrelation = 1
                 return self.aware
 

soil/agents/BigMarketModel.py

@@ -1,4 +1,3 @@
-import random
 from . import FSM, state, default_state
 
 
@@ -39,10 +38,10 @@ class BigMarketModel(FSM):
     @state
     def enterprise(self):
 
-        if random.random() < self.tweet_probability:  # Tweets
+        if self.random.random() < self.tweet_probability:  # Tweets
             aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbour users
             for x in aware_neighbors:
-                if random.uniform(0,10) < 5:
+                if self.random.uniform(0,10) < 5:
                     x.sentiment_about[self.id] += 0.1  # Increments for enterprise
                 else:
                     x.sentiment_about[self.id] -= 0.1  # Decrements for enterprise
@@ -57,11 +56,11 @@ class BigMarketModel(FSM):
 
     @state
     def user(self):
-        if random.random() < self.tweet_probability:  # Tweets
+        if self.random.random() < self.tweet_probability:  # Tweets
-            if random.random() < self.tweet_relevant_probability:  # Tweets something relevant
+            if self.random.random() < self.tweet_relevant_probability:  # Tweets something relevant
                 # Tweet probability per enterprise
                 for i in range(len(self.enterprises)):
-                    random_num = random.random()
+                    random_num = self.random.random()
                     if random_num < self.tweet_probability_about[i]:
                         # The condition is fulfilled, sentiments are evaluated towards that enterprise
                         if self.sentiment_about[i] < 0:

soil/agents/CounterModel.py

@@ -7,15 +7,13 @@ class CounterModel(NetworkAgent):
     in each step and adds it to its state.
     """
 
-    defaults = {
+    times = 0
-        'times': 0,
+    neighbors = 0
-        'neighbors': 0,
+    total = 0
-        'total': 0
-    }
 
     def step(self):
         # Outside effects
-        total = len(list(self.env.agents))
+        total = len(list(self.model.schedule._agents))
         neighbors = len(list(self.get_neighboring_agents()))
         self['times'] = self.get('times', 0) + 1
         self['neighbors'] = neighbors
@@ -28,17 +26,15 @@ class AggregatedCounter(NetworkAgent):
     in each step and adds it to its state.
     """
 
-    defaults = {
+    times = 0
-        'times': 0,
+    neighbors = 0
-        'neighbors': 0,
+    total = 0
-        'total': 0
-    }
 
     def step(self):
         # Outside effects
         self['times'] += 1
         neighbors = len(list(self.get_neighboring_agents()))
         self['neighbors'] += neighbors
-        total = len(list(self.env.agents))
+        total = len(list(self.model.schedule.agents))
         self['total'] += total
         self.debug('Running for step: {}. Total: {}'.format(self.now, total))

soil/agents/IndependentCascadeModel.py

@@ -1,4 +1,3 @@
-import random
 from . import BaseAgent
 
 
@@ -23,7 +22,7 @@ class IndependentCascadeModel(BaseAgent):
     def behaviour(self):
         aware_neighbors_1_time_step = []
         # Outside effects
-        if random.random() < self.innovation_prob:
+        if self.prob(self.innovation_prob):
             if self.state['id'] == 0:
                 self.state['id'] = 1
                 self.state['sentimentCorrelation'] = 1
@@ -40,7 +39,7 @@ class IndependentCascadeModel(BaseAgent):
                 if x.state['time_awareness'] == (self.env.now-1):
                     aware_neighbors_1_time_step.append(x)
             num_neighbors_aware = len(aware_neighbors_1_time_step)
-            if random.random() < (self.imitation_prob*num_neighbors_aware):
+            if self.prob(self.imitation_prob*num_neighbors_aware):
                 self.state['id'] = 1
                 self.state['sentimentCorrelation'] = 1
             else:

soil/agents/ModelM2.py

@@ -1,4 +1,3 @@
-import random
 import numpy as np
 from . import BaseAgent
 
@@ -24,22 +23,25 @@ class SpreadModelM2(BaseAgent):
     def __init__(self, model=None, unique_id=0, state=()):
         super().__init__(model=environment, unique_id=unique_id, state=state)
 
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
+
+        # Use a single generator with the same seed as `self.random`
+        random = np.random.default_rng(seed=self._seed)
+        self.prob_neutral_making_denier = random.normal(environment.environment_params['prob_neutral_making_denier'],
                                                         environment.environment_params['standard_variance'])
 
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
+        self.prob_infect = random.normal(environment.environment_params['prob_infect'],
                                          environment.environment_params['standard_variance'])
 
-        self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
+        self.prob_cured_healing_infected = random.normal(environment.environment_params['prob_cured_healing_infected'],
                                                          environment.environment_params['standard_variance'])
-        self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
+        self.prob_cured_vaccinate_neutral = random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
                                                           environment.environment_params['standard_variance'])
 
-        self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
+        self.prob_vaccinated_healing_infected = random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
                                                               environment.environment_params['standard_variance'])
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
+        self.prob_vaccinated_vaccinate_neutral = random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
                                                                environment.environment_params['standard_variance'])
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
+        self.prob_generate_anti_rumor = random.normal(environment.environment_params['prob_generate_anti_rumor'],
                                                       environment.environment_params['standard_variance'])
 
     def step(self):
@@ -58,7 +60,7 @@ class SpreadModelM2(BaseAgent):
         # Infected
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         if len(infected_neighbors) > 0:
-            if random.random() < self.prob_neutral_making_denier:
+            if self.prob(self.prob_neutral_making_denier):
                 self.state['id'] = 3   # Vaccinated making denier
 
     def infected_behaviour(self):
@@ -66,7 +68,7 @@ class SpreadModelM2(BaseAgent):
         # Neutral
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
+            if self.prob(self.prob_infect):
                 neighbor.state['id'] = 1  # Infected
 
     def cured_behaviour(self):
@@ -74,13 +76,13 @@ class SpreadModelM2(BaseAgent):
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
+            if self.prob(self.prob_cured_vaccinate_neutral):
                 neighbor.state['id'] = 3  # Vaccinated
 
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
+            if self.prob(self.prob_cured_healing_infected):
                 neighbor.state['id'] = 2  # Cured
 
     def vaccinated_behaviour(self):
@@ -88,19 +90,19 @@ class SpreadModelM2(BaseAgent):
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
+            if self.prob(self.prob_cured_healing_infected):
                 neighbor.state['id'] = 2  # Cured
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
+            if self.prob(self.prob_cured_vaccinate_neutral):
                 neighbor.state['id'] = 3  # Vaccinated
 
         # Generate anti-rumor
         infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
+            if self.prob(self.prob_generate_anti_rumor):
                 neighbor.state['id'] = 2  # Cured
 
 
@@ -165,7 +167,7 @@ class ControlModelM2(BaseAgent):
         # Infected
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         if len(infected_neighbors) > 0:
-            if random.random() < self.prob_neutral_making_denier:
+            if self.random(self.prob_neutral_making_denier):
                 self.state['id'] = 3   # Vaccinated making denier
 
     def infected_behaviour(self):
@@ -173,7 +175,7 @@ class ControlModelM2(BaseAgent):
         # Neutral
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
+            if self.prob(self.prob_infect):
                 neighbor.state['id'] = 1  # Infected
         self.state['visible'] = False
 
@@ -183,13 +185,13 @@ class ControlModelM2(BaseAgent):
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
+            if self.prob(self.prob_cured_vaccinate_neutral):
                 neighbor.state['id'] = 3  # Vaccinated
 
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
+            if self.prob(self.prob_cured_healing_infected):
                 neighbor.state['id'] = 2  # Cured
 
     def vaccinated_behaviour(self):
@@ -198,19 +200,19 @@ class ControlModelM2(BaseAgent):
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
+            if self.prob(self.prob_cured_healing_infected):
                 neighbor.state['id'] = 2  # Cured
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
+            if self.prob(self.prob_cured_vaccinate_neutral):
                 neighbor.state['id'] = 3  # Vaccinated
 
         # Generate anti-rumor
         infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
+            if self.prob(self.prob_generate_anti_rumor):
                 neighbor.state['id'] = 2  # Cured
 
     def beacon_off_behaviour(self):
@@ -224,19 +226,19 @@ class ControlModelM2(BaseAgent):
         # Cure (M2 feature added)
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_generate_anti_rumor:
+            if self.prob(self.prob_generate_anti_rumor):
                 neighbor.state['id'] = 2  # Cured
             neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
             for neighbor in neutral_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
+                if self.prob(self.prob_generate_anti_rumor):
                     neighbor.state['id'] = 3  # Vaccinated
             infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
             for neighbor in infected_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
+                if self.prob(self.prob_generate_anti_rumor):
                     neighbor.state['id'] = 2  # Cured
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
+            if self.prob(self.prob_cured_vaccinate_neutral):
                 neighbor.state['id'] = 3  # Vaccinated

soil/agents/SISaModel.py

@@ -1,4 +1,3 @@
-import random
 import numpy as np
 from . import FSM, state
 
@@ -32,62 +31,64 @@ class SISaModel(FSM):
     def __init__(self, environment, unique_id=0, state=()):
         super().__init__(model=environment, unique_id=unique_id, state=state)
 
-        self.neutral_discontent_spon_prob = np.random.normal(self.env['neutral_discontent_spon_prob'],
+        random = np.random.default_rng(seed=self._seed)
+
+        self.neutral_discontent_spon_prob = random.normal(self.env['neutral_discontent_spon_prob'],
                                                              self.env['standard_variance'])
-        self.neutral_discontent_infected_prob = np.random.normal(self.env['neutral_discontent_infected_prob'],
+        self.neutral_discontent_infected_prob = random.normal(self.env['neutral_discontent_infected_prob'],
                                                                  self.env['standard_variance'])
-        self.neutral_content_spon_prob = np.random.normal(self.env['neutral_content_spon_prob'],
+        self.neutral_content_spon_prob = random.normal(self.env['neutral_content_spon_prob'],
                                                           self.env['standard_variance'])
-        self.neutral_content_infected_prob = np.random.normal(self.env['neutral_content_infected_prob'],
+        self.neutral_content_infected_prob = random.normal(self.env['neutral_content_infected_prob'],
                                                               self.env['standard_variance'])
 
-        self.discontent_neutral = np.random.normal(self.env['discontent_neutral'],
+        self.discontent_neutral = random.normal(self.env['discontent_neutral'],
                                                    self.env['standard_variance'])
-        self.discontent_content = np.random.normal(self.env['discontent_content'],
+        self.discontent_content = random.normal(self.env['discontent_content'],
                                                    self.env['variance_d_c'])
 
-        self.content_discontent = np.random.normal(self.env['content_discontent'],
+        self.content_discontent = random.normal(self.env['content_discontent'],
                                                    self.env['variance_c_d'])
-        self.content_neutral = np.random.normal(self.env['content_neutral'],
+        self.content_neutral = random.normal(self.env['content_neutral'],
                                                 self.env['standard_variance'])
 
     @state
     def neutral(self):
         # Spontaneous effects
-        if random.random() < self.neutral_discontent_spon_prob:
+        if self.prob(self.neutral_discontent_spon_prob):
             return self.discontent
-        if random.random() < self.neutral_content_spon_prob:
+        if self.prob(self.neutral_content_spon_prob):
             return self.content
 
         # Infected
         discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent)
-        if random.random() < discontent_neighbors * self.neutral_discontent_infected_prob:
+        if self.prob(scontent_neighbors * self.neutral_discontent_infected_prob):
             return self.discontent
         content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
-        if random.random() < content_neighbors * self.neutral_content_infected_prob:
+        if self.prob(s * self.neutral_content_infected_prob):
             return self.content
         return self.neutral
 
     @state
     def discontent(self):
         # Healing
-        if random.random() < self.discontent_neutral:
+        if self.prob(self.discontent_neutral):
             return self.neutral
 
         # Superinfected
         content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
-        if random.random() < content_neighbors * self.discontent_content:
+        if self.prob(s * self.discontent_content):
             return self.content
         return self.discontent
 
     @state
     def content(self):
         # Healing
-        if random.random() < self.content_neutral:
+        if self.prob(self.content_neutral):
             return self.neutral
 
         # Superinfected
         discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent.id)
-        if random.random() < discontent_neighbors * self.content_discontent:
+        if self.prob(scontent_neighbors * self.content_discontent):
             self.discontent
         return self.content

soil/agents/SentimentCorrelationModel.py

@@ -1,4 +1,3 @@
-import random
 from . import BaseAgent
 
 
@@ -68,10 +67,10 @@ class SentimentCorrelationModel(BaseAgent):
         disgust_prob = self.disgust_prob+(len(disgusted_neighbors_1_time_step)*self.disgust_prob)
         outside_effects_prob = self.outside_effects_prob
 
-        num = random.random()
+        num = self.random.random()
 
         if num<outside_effects_prob:
-            self.state['id'] = random.randint(1, 4)
+            self.state['id'] = self.random.randint(1, 4)
 
             self.state['sentimentCorrelation'] = self.state['id'] # It is stored when it has been infected for the dynamic network
             self.state['time_awareness'][self.state['id']-1] = self.env.now

soil/agents/__init__.py

@@ -1,18 +1,22 @@
+from __future__ import annotations
+
 import logging
 from collections import OrderedDict, defaultdict
 from collections.abc import MutableMapping, Mapping, Set
 from abc import ABCMeta
-from copy import deepcopy
+from copy import deepcopy, copy
 from functools import partial, wraps
 from itertools import islice, chain
-import json
+import inspect
+import types
+import textwrap
 import networkx as nx
 
+from typing import Any
+
 from mesa import Agent as MesaAgent
 from typing import Dict, List
 
-from random import shuffle
-
 from .. import serialization, utils, time, config
 
 
@@ -28,7 +32,32 @@ IGNORED_FIELDS = ('model', 'logger')
 class DeadAgent(Exception):
     pass
 
-class BaseAgent(MesaAgent, MutableMapping):
+
+class MetaAgent(ABCMeta):
+    def __new__(mcls, name, bases, namespace):
+        defaults = {}
+
+        # Re-use defaults from inherited classes
+        for i in bases:
+            if isinstance(i, MetaAgent):
+                defaults.update(i._defaults)
+
+        new_nmspc = {
+            '_defaults': defaults,
+        }
+
+        for attr, func in namespace.items():
+            if isinstance(func, types.FunctionType) or isinstance(func, property) or attr[0] == '_':
+                new_nmspc[attr] = func
+            elif attr == 'defaults':
+                defaults.update(func)
+            else:
+                defaults[attr] = copy(func)
+
+        return super().__new__(mcls=mcls, name=name, bases=bases, namespace=new_nmspc)
+
+
+class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
     """
     A special type of Mesa Agent that:
 
@@ -40,15 +69,12 @@ class BaseAgent(MesaAgent, MutableMapping):
     Any attribute that is not preceded by an underscore (`_`) will also be added to its state.
     """
 
-    defaults = {}
-
     def __init__(self,
                  unique_id,
                  model,
                  name=None,
                  interval=None,
-                 **kwargs
+                 **kwargs):
-    ):
         # Check for REQUIRED arguments
         # Initialize agent parameters
         if isinstance(unique_id, MesaAgent):
@@ -59,15 +85,16 @@ class BaseAgent(MesaAgent, MutableMapping):
         self.name = str(name) if name else'{}[{}]'.format(type(self).__name__, self.unique_id)
 
 
-        self._neighbors = None
         self.alive = True
 
         self.interval = interval or self.get('interval', 1)
-        self.logger = logging.getLogger(self.model.id).getChild(self.name)
+        logger = utils.logger.getChild(getattr(self.model, 'id', self.model)).getChild(self.name)
+        self.logger = logging.LoggerAdapter(logger, {'agent_name': self.name})
 
         if hasattr(self, 'level'):
             self.logger.setLevel(self.level)
-        for (k, v) in self.defaults.items():
+
+        for (k, v) in self._defaults.items():
             if not hasattr(self, k) or getattr(self, k) is None:
                 setattr(self, k, deepcopy(v))
 
@@ -75,26 +102,17 @@ class BaseAgent(MesaAgent, MutableMapping):
 
             setattr(self, k, v)
 
-        for (k, v) in getattr(self, 'defaults', {}).items():
-            if not hasattr(self, k) or getattr(self, k) is None:
-                setattr(self, k, v)
-
     def __hash__(self):
         return hash(self.unique_id)
 
+    def prob(self, probability):
+        return prob(probability, self.model.random)
+
     # TODO: refactor to clean up mesa compatibility
     @property
     def id(self):
         return self.unique_id
 
-    @property
-    def env(self):
-        return self.model
-
-    @env.setter
-    def env(self, model):
-        self.model = model
-
     @property
     def state(self):
         '''
@@ -106,19 +124,16 @@ class BaseAgent(MesaAgent, MutableMapping):
 
     @state.setter
     def state(self, value):
+        if not value:
+            return
         for k, v in value.items():
             self[k] = v
 
-    @property
-    def environment_params(self):
-        return self.model.environment_params
-
-    @environment_params.setter
-    def environment_params(self, value):
-        self.model.environment_params = value
-
     def __getitem__(self, key):
+        try:
             return getattr(self, key)
+        except AttributeError:
+            raise KeyError(f'key {key}  not found in agent')
 
     def __delitem__(self, key):
         return delattr(self, key)
@@ -136,10 +151,14 @@ class BaseAgent(MesaAgent, MutableMapping):
         return self.items()
 
     def keys(self):
-        return (k for k in self.__dict__ if k[0] != '_')
+        return (k for k in self.__dict__ if k[0] != '_' and k not in IGNORED_FIELDS)
 
-    def items(self):
+    def items(self, keys=None, skip=None):
-        return ((k, v) for (k, v) in self.__dict__.items() if k[0] != '_')
+        keys = keys if keys is not None else self.keys()
+        it = ((k, self.get(k, None)) for k in keys)
+        if skip:
+            return filter(lambda x: x[0] not in skip, it)
+        return it
         
     def get(self, key, default=None):
         return self[key] if key in self else default
@@ -152,11 +171,9 @@ class BaseAgent(MesaAgent, MutableMapping):
             # No environment
             return None
 
-    def die(self, remove=False):
+    def die(self):
-        self.info(f'agent {self.unique_id}  is dying')
+        self.info(f'agent dying')
         self.alive = False
-        if remove:
-            self.remove_node(self.id)
         return time.NEVER
 
     def step(self):
@@ -168,7 +185,7 @@ class BaseAgent(MesaAgent, MutableMapping):
         if not self.logger.isEnabledFor(level):
             return
         message = message + " ".join(str(i) for i in args)
-        message = " @{:>3}: {}".format(self.now, message)
+        message = "[@{:>4}]\t{:>10}: {}".format(self.now, repr(self), message)
         for k, v in kwargs:
             message += " {k}={v} ".format(k, v)
         extra = {}
@@ -177,33 +194,48 @@ class BaseAgent(MesaAgent, MutableMapping):
         extra['agent_name'] = self.name
         return self.logger.log(level, message, extra=extra)
 
-
-
     def debug(self, *args, **kwargs):
         return self.log(*args, level=logging.DEBUG, **kwargs)
 
     def info(self, *args, **kwargs):
         return self.log(*args, level=logging.INFO, **kwargs)
 
-# Alias
+    def count_agents(self, **kwargs):
-# Agent = BaseAgent
+        return len(list(self.get_agents(**kwargs)))
+
+    def get_agents(self, *args, **kwargs):
+        it = self.iter_agents(*args, **kwargs)
+        return list(it)
+
+    def iter_agents(self, *args, **kwargs):
+        yield from filter_agents(self.model.schedule._agents, *args, **kwargs)
+
+    def __str__(self):
+        return self.to_str()
+
+    def to_str(self, keys=None, skip=None, pretty=False):
+        content = dict(self.items(keys=keys))
+        if pretty and content:
+            d = content
+            content = '\n'
+            for k, v in d.items():
+                content += f'- {k}: {v}\n'
+            content = textwrap.indent(content, '    ')
+        return f"{repr(self)}{content}"
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self.unique_id})"
+
 
 class NetworkAgent(BaseAgent):
 
-    @property
+    def __init__(self, *args, topology, node_id, **kwargs):
-    def topology(self):
+        super().__init__(*args, **kwargs)
-        return self.env.topology_for(self.unique_id)
 
-    @property
+        self.topology = topology
-    def node_id(self):
+        self.node_id = node_id
-        return self.env.node_id_for(self.unique_id)
+        self.G = self.model.topologies[topology]
-
+        assert self.G
-    @property
-    def G(self):
-        return self.model.topologies[self._topology]
-
-    def count_agents(self, **kwargs):
-        return len(list(self.get_agents(**kwargs)))
 
     def count_neighboring_agents(self, state_id=None, **kwargs):
         return len(self.get_neighboring_agents(state_id=state_id, **kwargs))
@@ -211,57 +243,47 @@ class NetworkAgent(BaseAgent):
     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, *args, limit=None, **kwargs):
-        it = self.iter_agents(*args, **kwargs)
-        if limit is not None:
-            it = islice(it, limit)
-        return list(it)
-
     def iter_agents(self, unique_id=None, limit_neighbors=False, **kwargs):
+        unique_ids = None
+        if isinstance(unique_id, list):
+            unique_ids = set(unique_id)
+        elif unique_id is not None:
+            unique_ids = set([unique_id,])
+
         if limit_neighbors:
-            unique_id = [self.topology.nodes[node]['agent_id'] for node in self.topology.neighbors(self.node_id)]
+            neighbor_ids = set()
-            if not unique_id:
+            for node_id in self.G.neighbors(self.node_id):
+                if self.G.nodes[node_id].get('agent_id') is not None:
+                    neighbor_ids.add(node_id)
+            if unique_ids:
+                unique_ids = unique_ids & neighbor_ids
+            else:
+                unique_ids = neighbor_ids
+            if not unique_ids:
                 return
-
+            unique_ids = list(unique_ids)
-        yield from self.model.agents(unique_id=unique_id, **kwargs)
+        yield from super().iter_agents(unique_id=unique_ids, **kwargs)
-
 
     def subgraph(self, center=True, **kwargs):
         include = [self] if center else []
-        G = self.topology.subgraph(n.node_id for n in list(self.get_agents(**kwargs)+include))
+        G = self.G.subgraph(n.node_id for n in list(self.get_agents(**kwargs)+include))
         return G
 
-    def remove_node(self, unique_id):
+    def remove_node(self):
-        self.topology.remove_node(unique_id)
+        self.G.remove_node(self.node_id)
 
     def add_edge(self, other, edge_attr_dict=None, *edge_attrs):
-        # return super(NetworkAgent, self).add_edge(node1=self.id, node2=other, **kwargs)
+        if self.node_id not in self.G.nodes(data=False):
-        if self.unique_id not in self.topology.nodes(data=False):
             raise ValueError('{} not in list of existing agents in the network'.format(self.unique_id))
-        if other.unique_id not in self.topology.nodes(data=False):
+        if other.node_id not in self.G.nodes(data=False):
             raise ValueError('{} not in list of existing agents in the network'.format(other))
 
-        self.topology.add_edge(self.unique_id, other.unique_id, edge_attr_dict=edge_attr_dict, *edge_attrs)
+        self.G.add_edge(self.node_id, other.node_id, edge_attr_dict=edge_attr_dict, *edge_attrs)
 
-    def ego_search(self, steps=1, center=False, node=None, **kwargs):
+    def die(self, remove=True):
-        '''Get a list of nodes in the ego network of *node* of radius *steps*'''
+        if remove:
-        node = as_node(node if node is not None else self)
+            self.remove_node()
-        G = self.subgraph(**kwargs)
+        return super().die()
-        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.model, '_degree')) or getattr(self.model, '_last_step', 0) < self.now:
-            self.model._degree = nx.degree_centrality(self.topology)
-            self.model._last_step = self.now
-        return self.model._degree[node]
-
-    def betweenness(self, node, force=False):
-        node = as_node(node)
-        if force or (not hasattr(self.model, '_betweenness')) or getattr(self.model, '_last_step', 0) < self.now:
-            self.model._betweenness = nx.betweenness_centrality(self.topology)
-            self.model._last_step = self.now
-        return self.model._betweenness[node]
 
 
 def state(name=None):
@@ -271,24 +293,29 @@ def state(name=None):
         The default value for state_id is the current state id.
         The default value for when is the interval defined in the environment.
         '''
+        if inspect.isgeneratorfunction(func):
+            orig_func = func
 
             @wraps(func)
-        def func_wrapper(self):
+            def func(self):
-            next_state = func(self)
+                while True:
-            when = None
+                    if not self._coroutine:
-            if next_state is None:
+                        self._coroutine = orig_func(self)
-                return when
                     try:
-                next_state, when = next_state
+                        n = next(self._coroutine)
-            except (ValueError, TypeError):
+                        if n:
-                pass
+                            return None, n
-            if next_state:
+                        return
+                    except StopIteration as ex:
+                        self._coroutine = None
+                        next_state = ex.value
+                        if next_state is not None:
                             self.set_state(next_state)
-            return when
+                        return next_state
 
-        func_wrapper.id = name or func.__name__
+        func.id = name or func.__name__
-        func_wrapper.is_default = False
+        func.is_default = False
-        return func_wrapper
+        return func
 
     if callable(name):
         return decorator(name)
@@ -301,62 +328,86 @@ def default_state(func):
     return func
 
 
-class MetaFSM(ABCMeta):
+class MetaFSM(MetaAgent):
-    def __init__(cls, name, bases, nmspc):
+    def __new__(mcls, name, bases, namespace):
-        super(MetaFSM, cls).__init__(name, bases, nmspc)
         states = {}
         # Re-use states from inherited classes
         default_state = None
         for i in bases:
             if isinstance(i, MetaFSM):
-                for state_id, state in i.states.items():
+                for state_id, state in i._states.items():
                     if state.is_default:
                         default_state = state
                     states[state_id] = state
 
         # Add new states
-        for name, func in nmspc.items():
+        for attr, func in namespace.items():
             if hasattr(func, 'id'):
                 if func.is_default:
                     default_state = func
                 states[func.id] = func
-        cls.default_state = default_state
+
-        cls.states = states
+        namespace.update({
+            '_default_state': default_state,
+            '_states': states,
+        })
+
+        return super(MetaFSM, mcls).__new__(mcls=mcls, name=name, bases=bases, namespace=namespace)
 
 
 class FSM(BaseAgent, metaclass=MetaFSM):
     def __init__(self, *args, **kwargs):
         super(FSM, self).__init__(*args, **kwargs)
         if not hasattr(self, 'state_id'):
-            if not self.default_state:
+            if not self._default_state:
                 raise ValueError('No default state specified for {}'.format(self.unique_id))
-            self.state_id = self.default_state.id
+            self.state_id = self._default_state.id
 
+        self._coroutine = None
         self.set_state(self.state_id)
 
     def step(self):
         self.debug(f'Agent {self.unique_id} @ state {self.state_id}')
-        interval = super().step()
+        default_interval = super().step()
-        if 'id' not in self.state:
-            if self.default_state:
-                self.set_state(self.default_state.id)
-            else:
-                raise Exception('{} has no valid state id or default state'.format(self))
-        interval = self.states[self.state_id](self) or interval
-        if not self.alive:
-            return time.NEVER
-        return interval
 
-    def set_state(self, state):
+        next_state = self._states[self.state_id](self)
+
+        when = None
+        try:
+            next_state, *when = next_state
+            if not when:
+                when = None
+            elif len(when) == 1:
+                when = when[0]
+            else:
+                raise ValueError('Too many values returned. Only state (and time) allowed')
+        except TypeError:
+            pass
+
+        if next_state is not None:
+            self.set_state(next_state)
+
+        return when or default_interval
+
+    def set_state(self, state, when=None):
         if hasattr(state, 'id'):
             state = state.id
-        if state not in self.states:
+        if state not in self._states:
             raise ValueError('{} is not a valid state'.format(state))
         self.state_id = state
+        if when is not None:
+            self.model.schedule.add(self, when=when)
         return state
 
+    def die(self):
+        return self.dead, super().die()
 
-def prob(prob=1):
+    @state
+    def dead(self):
+        return self.die()
+
+
+def prob(prob, random):
     '''
     A true/False uniform distribution with a given probability.
     To be used like this:
@@ -474,81 +525,81 @@ def _convert_agent_classs(ind, to_string=False, **kwargs):
     return deserialize_definition(ind, **kwargs)
 
 
-def _agent_from_definition(definition, value=-1, unique_id=None):
+# def _agent_from_definition(definition, random, value=-1, unique_id=None):
-    """Used in the initialization of agents given an agent distribution."""
+#     """Used in the initialization of agents given an agent distribution."""
-    if value < 0:
+#     if value < 0:
-        value = random.random()
+#         value = random.random()
-    for d in sorted(definition, key=lambda x: x.get('threshold')):
+#     for d in sorted(definition, key=lambda x: x.get('threshold')):
-        threshold = d.get('threshold', (-1, -1))
+#         threshold = d.get('threshold', (-1, -1))
-        # Check if the definition matches by id (first) or by threshold
+#         # Check if the definition matches by id (first) or by threshold
-        if (unique_id is not None and unique_id in d.get('ids', [])) or \
+#         if (unique_id is not None and unique_id in d.get('ids', [])) or \
-           (value >= threshold[0] and value < threshold[1]):
+#            (value >= threshold[0] and value < threshold[1]):
-            state = {}
+#             state = {}
-            if 'state' in d:
+#             if 'state' in d:
-                state = deepcopy(d['state'])
+#                 state = deepcopy(d['state'])
-            return d['agent_class'], state
+#             return d['agent_class'], state
 
-    raise Exception('Definition for value {} not found in: {}'.format(value, definition))
+#     raise Exception('Definition for value {} not found in: {}'.format(value, definition))
 
 
-def _definition_to_dict(definition, size=None, default_state=None):
+# def _definition_to_dict(definition, random, size=None, default_state=None):
-    state = default_state or {}
+#     state = default_state or {}
-    agents = {}
+#     agents = {}
-    remaining = {}
+#     remaining = {}
-    if size:
+#     if size:
-        for ix in range(size):
+#         for ix in range(size):
-            remaining[ix] = copy(state)
+#             remaining[ix] = copy(state)
-    else:
+#     else:
-        remaining = defaultdict(lambda x: copy(state))
+#         remaining = defaultdict(lambda x: copy(state))
 
-    distro = sorted([item for item in definition if 'weight' in item])
+#     distro = sorted([item for item in definition if 'weight' in item])
 
-    id = 0
+#     id = 0
 
-    def init_agent(item, id=ix):
+#     def init_agent(item, id=ix):
-        while id in agents:
+#         while id in agents:
-            id += 1
+#             id += 1
 
-        agent = remaining[id]
+#         agent = remaining[id]
-        agent['state'].update(copy(item.get('state', {})))
+#         agent['state'].update(copy(item.get('state', {})))
-        agents[agent.unique_id] = agent
+#         agents[agent.unique_id] = agent
-        del remaining[id]
+#         del remaining[id]
-        return agent
+#         return agent
 
-    for item in definition:
+#     for item in definition:
-        if 'ids' in item:
+#         if 'ids' in item:
-            ids = item['ids']
+#             ids = item['ids']
-            del item['ids']
+#             del item['ids']
-            for id in ids:
+#             for id in ids:
-                agent = init_agent(item, id)
+#                 agent = init_agent(item, id)
 
-    for item in definition:
+#     for item in definition:
-        if 'number' in item:
+#         if 'number' in item:
-            times = item['number']
+#             times = item['number']
-            del item['number']
+#             del item['number']
-            for times in range(times):
+#             for times in range(times):
-                if size:
+#                 if size:
-                    ix = random.choice(remaining.keys())
+#                     ix = random.choice(remaining.keys())
-                    agent = init_agent(item, id)
+#                     agent = init_agent(item, id)
-                else:
+#                 else:
-                    agent = init_agent(item)
+#                     agent = init_agent(item)
-    if not size:
+#     if not size:
-        return agents
+#         return agents
 
-    if len(remaining) < 0:
+#     if len(remaining) < 0:
-        raise Exception('Invalid definition. Too many agents to add')
+#         raise Exception('Invalid definition. Too many agents to add')
 
 
-    total_weight = float(sum(s['weight'] for s in distro))
+#     total_weight = float(sum(s['weight'] for s in distro))
-    unit = size / total_weight
+#     unit = size / total_weight
 
-    for item in distro:
+#     for item in distro:
-        times = unit * item['weight']
+#         times = unit * item['weight']
-        del item['weight']
+#         del item['weight']
-        for times in range(times):
+#         for times in range(times):
-            ix = random.choice(remaining.keys())
+#             ix = random.choice(remaining.keys())
-            agent = init_agent(item, id)
+#             agent = init_agent(item, id)
-    return agents
+#     return agents
 
 
 class AgentView(Mapping, Set):
@@ -569,59 +620,43 @@ class AgentView(Mapping, Set):
 
     # Mapping methods
     def __len__(self):
-        return sum(len(x) for x in self._agents.values())
+        return len(self._agents)
 
     def __iter__(self):
-        yield from iter(chain.from_iterable(g.values() for g in self._agents.values()))
+        yield from self._agents.values()
 
     def __getitem__(self, agent_id):
         if isinstance(agent_id, slice):
             raise ValueError(f"Slicing is not supported")
-        for group in self._agents.values():
+        if agent_id in self._agents:
-            if agent_id in group:
+            return self._agents[agent_id]
-                return group[agent_id]
         raise ValueError(f"Agent {agent_id} not found")
 
     def filter(self, *args, **kwargs):
-        yield from filter_groups(self._agents, *args, **kwargs)
+        yield from filter_agents(self._agents, *args, **kwargs)
 
     def one(self, *args, **kwargs):
-        return next(filter_groups(self._agents, *args, **kwargs))
+        return next(filter_agents(self._agents, *args, **kwargs))
 
     def __call__(self, *args, **kwargs):
         return list(self.filter(*args, **kwargs))
 
     def __contains__(self, agent_id):
-        return any(agent_id in g for g in self._agents)
+        return agent_id in self._agents
 
     def __str__(self):
-        return str(list(a.unique_id for a in self))
+        return str(list(unique_id for unique_id in self.keys()))
 
     def __repr__(self):
         return f"{self.__class__.__name__}({self})"
 
 
-def filter_groups(groups, *, group=None, **kwargs):
+def filter_agents(agents, *id_args, unique_id=None, state_id=None, agent_class=None, ignore=None, state=None,
-    assert isinstance(groups, dict)
+                  limit=None, **kwargs):
-
-    if group is not None and not isinstance(group, list):
-        group = [group]
-
-    if group:
-        groups = list(groups[g] for g in group if g in groups)
-    else:
-        groups = list(groups.values())
-
-    agents = chain.from_iterable(filter_group(g, **kwargs) for g in groups)
-
-    yield from agents
-
-
-def filter_group(group, *id_args, unique_id=None, state_id=None, agent_class=None, ignore=None, state=None, **kwargs):
     '''
     Filter agents given as a dict, by the criteria given as arguments (e.g., certain type or state id).
     '''
-    assert isinstance(group, dict)
+    assert isinstance(agents, dict)
 
     ids = []
 
@@ -634,6 +669,11 @@ def filter_group(group, *id_args, unique_id=None, state_id=None, agent_class=Non
     if id_args:
         ids += id_args
 
+    if ids:
+        f = (agents[aid] for aid in ids if aid in agents)
+    else:
+        f = (a for a in agents.values())
+
     if state_id is not None and not isinstance(state_id, (tuple, list)):
         state_id = tuple([state_id])
 
@@ -644,12 +684,6 @@ def filter_group(group, *id_args, unique_id=None, state_id=None, agent_class=Non
         except TypeError:
             agent_class = tuple([agent_class])
 
-    if ids:
-        agents = (group[aid] for aid in ids if aid in group)
-    else:
-        agents = (a for a in group.values())
-
-    f = agents
     if ignore:
         f = filter(lambda x: x not in ignore, f)
 
@@ -665,77 +699,125 @@ def filter_group(group, *id_args, unique_id=None, state_id=None, agent_class=Non
     for k, v in state.items():
         f = filter(lambda agent: agent.state.get(k, None) == v, f)
 
+    if limit is not None:
+        f = islice(f, limit)
+
     yield from f
 
 
-def from_config(cfg: Dict[str, config.AgentConfig], env):
+def from_config(cfg: config.AgentConfig, random, topologies: Dict[str, nx.Graph] = None) -> List[Dict[str, Any]]:
     '''
-    Agents are specified in groups.
+    This function turns an agentconfig into a list of individual "agent specifications", which are just a dictionary
-    Each group can be specified in two ways, either through a fixed list in which each item has
+    with the parameters that the environment will use to construct each agent.
-    has the agent type, number of agents to create, and the other parameters, or through what we call
+
-    an `agent distribution`, which is similar but instead of number of agents, it specifies the weight
+    This function does NOT return a list of agents, mostly because some attributes to the agent are not known at the
-    of each agent type.
+    time of calling this function, such as `unique_id`.
     '''
-    default = cfg.get('default', None)
+    default = cfg or config.AgentConfig()
-    return {k: _group_from_config(c, default=default, env=env) for (k, c)  in cfg.items() if k is not 'default'}
+    if not isinstance(cfg, config.AgentConfig):
+        cfg = config.AgentConfig(**cfg)
+    return _agents_from_config(cfg, topologies=topologies, random=random)
 
 
-def _group_from_config(cfg: config.AgentConfig, default: config.SingleAgentConfig, env):
+def _agents_from_config(cfg: config.AgentConfig,
-    agents = {}
+                        topologies: Dict[str, nx.Graph],
+                        random) -> List[Dict[str, Any]]:
+    if cfg and not isinstance(cfg, config.AgentConfig):
+        cfg = config.AgentConfig(**cfg)
+
+    agents = []
+
+    assigned = defaultdict(int)
+
     if cfg.fixed is not None:
-        agents = _from_fixed(cfg.fixed, topology=cfg.topology, default=default, env=env)
+        agents, counts = _from_fixed(cfg.fixed, topology=cfg.topology, default=cfg)
-    if cfg.distribution:
+        assigned.update(counts)
-        n = cfg.n or len(env.topologies[cfg.topology or default.topology])
-        target = n - len(agents)
-        agents.update(_from_distro(cfg.distribution, target,
-                                   topology=cfg.topology or default.topology,
-                                   default=default,
-                                   env=env))
-        assert len(agents) == n
-    if cfg.override:
-        for attrs in cfg.override:
-            if attrs.filter:
-                filtered = list(filter_group(agents, **attrs.filter))
-            else:
-                filtered = list(agents)
 
-            if attrs.n > len(filtered):
+    n = cfg.n
-                raise ValueError(f'Not enough agents to sample. Got {len(filtered)}, expected >= {attrs.n}')
+
-            for agent in random.sample(filtered, attrs.n):
+    if cfg.distribution:
-                agent.state.update(attrs.state)
+        topo_size = {top: len(topologies[top]) for top in topologies}
+
+        grouped = defaultdict(list)
+        total = []
+
+        for d in cfg.distribution:
+            if d.strategy == config.Strategy.topology:
+                topology = d.topology if ('topology' in d.__fields_set__) else cfg.topology
+                if not topology:
+                    raise ValueError('The "topology" strategy only works if the topology parameter is specified')
+                if topology not in topo_size:
+                    raise ValueError(f'Unknown topology selected: { topology }. Make sure the topology has been defined')
+
+                grouped[topology].append(d)
+
+            if d.strategy == config.Strategy.total:
+                if not cfg.n:
+                    raise ValueError('Cannot use the "total" strategy without providing the total number of agents')
+                total.append(d)
+
+        
+        for (topo, distro) in grouped.items():
+            if not topologies or topo not in topo_size:
+                raise ValueError(
+                    'You need to specify a target number of agents for the distribution \
+                    or a configuration with a topology, along with a dictionary with \
+                    all the available topologies')
+            n = len(topologies[topo])
+            target = topo_size[topo] - assigned[topo]
+            new_agents = _from_distro(cfg.distribution, target,
+                                      topology=topo,
+                                      default=cfg,
+                                      random=random)
+            assigned[topo] += len(new_agents)
+            agents += new_agents
+
+        if total:
+          remaining = n - sum(assigned.values())
+          agents += _from_distro(total, remaining,
+                                topology='',  # DO NOT assign to any topology
+                                default=cfg,
+                                random=random)
+
+
+        if sum(assigned.values()) != sum(topo_size.values()):
+            utils.logger.warn(f'The total number of agents does not match the total number of nodes in '
+                              'every topology. This may be due to a definition error: assigned: '
+                              f'{ assigned } total sizes: { topo_size }')
 
     return agents
 
 
-def _from_fixed(lst: List[config.FixedAgentConfig], topology: str, default: config.SingleAgentConfig, env):
+def _from_fixed(lst: List[config.FixedAgentConfig], topology: str, default: config.SingleAgentConfig) -> List[Dict[str, Any]]:
-    agents = {}
+    agents = []
+
+    counts = {}
 
     for fixed in lst:
-        agent_id = fixed.agent_id
+        agent = {}
-        if agent_id is None:
+        if default:
-            agent_id = env.next_id()
+            agent = default.state.copy()
+        agent.update(fixed.state)
+        cls = serialization.deserialize(fixed.agent_class or (default and default.agent_class))
+        agent['agent_class'] = cls
+        topo = fixed.topology if ('topology' in fixed.__fields_set__) else topology or default.topology
 
-        cls = serialization.deserialize(fixed.agent_class or default.agent_class)
+        if topo:
-        state = fixed.state.copy()
+            agent['topology'] = topo
-        state.update(default.state)
+        if not fixed.hidden:
-        agent = cls(unique_id=agent_id,
+            counts[topo] = counts.get(topo, 0) + 1
-                    model=env,
+        agents.append(agent)
-                    **state)
-        topology = fixed.topology if (fixed.topology is not None) else (topology or default.topology)
-        if topology:
-            env.agent_to_node(agent_id, topology, fixed.node_id)
-        agents[agent.unique_id] = agent
 
-    return agents
+    return agents, counts
 
 
 def _from_distro(distro: List[config.AgentDistro],
                  n: int,
                  topology: str,
                  default: config.SingleAgentConfig,
-                 env):
+                 random) -> List[Dict[str, Any]]:
 
-    agents = {}
+    agents = []
 
     if n is None:
         if any(lambda dist: dist.n is None, distro):
@@ -767,19 +849,16 @@ def _from_distro(distro: List[config.AgentDistro],
 
     for idx in indices:
         d = distro[idx]
+        agent = d.state.copy()
         cls = classes[idx]
-        agent_id = env.next_id()
+        agent['agent_class'] = cls
-        state = d.state.copy()
         if default:
-            state.update(default.state)
+            agent.update(default.state)
-        agent = cls(unique_id=agent_id, model=env, **state)
+        # agent = cls(unique_id=agent_id, model=env, **state)
-        topology = d.topology if (d.topology is not None) else topology or default.topology
+        topology = d.topology if ('topology' in d.__fields_set__) else topology or default.topology
         if topology:
-            env.agent_to_node(agent.unique_id, topology)
+            agent['topology'] = topology
-        assert agent.name is not None
+        agents.append(agent)
-        assert agent.name != 'None'
-        assert agent.name
-        agents[agent.unique_id] = agent
 
     return agents
 

soil/analysis.py

@@ -1,206 +0,0 @@
-import pandas as pd
-
-import glob
-import yaml
-from os.path import join
-
-from . import serialization
-from tsih import History
-
-
-def read_data(*args, group=False, **kwargs):
-    iterable = _read_data(*args, **kwargs)
-    if group:
-        return group_trials(iterable)
-    else:
-        return list(iterable)
-
-
-def _read_data(pattern, *args, from_csv=False, process_args=None, **kwargs):
-    if not process_args:
-        process_args = {}
-    for folder in glob.glob(pattern):
-        config_file = glob.glob(join(folder, '*.yml'))[0]
-        config = yaml.load(open(config_file), Loader=yaml.SafeLoader)
-        df = None
-        if from_csv:
-            for trial_data in sorted(glob.glob(join(folder,
-                                                    '*.environment.csv'))):
-                df = read_csv(trial_data, **kwargs)
-                yield config_file, df, config
-        else:
-            for trial_data in sorted(glob.glob(join(folder, '*.sqlite'))):
-                df = read_sql(trial_data, **kwargs)
-                yield config_file, df, config
-
-
-def read_sql(db, *args, **kwargs):
-    h = History(db_path=db, backup=False, readonly=True)
-    df = h.read_sql(*args, **kwargs)
-    return df
-
-
-def read_csv(filename, keys=None, convert_types=False, **kwargs):
-    '''
-    Read a CSV in canonical form: ::
-
-        <agent_id, t_step, key, value, value_type>
-
-    '''
-    df = pd.read_csv(filename)
-    if convert_types:
-        df = convert_types_slow(df)
-    if keys:
-        df = df[df['key'].isin(keys)]
-    df = process_one(df)
-    return df
-
-
-def convert_row(row):
-    row['value'] = serialization.deserialize(row['value_type'], row['value'])
-    return row
-
-
-def convert_types_slow(df):
-    '''
-    Go over every column in a dataframe and convert it to the type determined by the `get_types`
-    function.
-
-    This is a slow operation.
-    '''
-    dtypes = get_types(df)
-    for k, v in dtypes.items():
-        t = df[df['key']==k]
-        t['value'] = t['value'].astype(v)
-    df = df.apply(convert_row, axis=1)
-    return df
-
-
-def split_processed(df):
-    env = df.loc[:, df.columns.get_level_values(1).isin(['env', 'stats'])]
-    agents = df.loc[:, ~df.columns.get_level_values(1).isin(['env', 'stats'])]
-    return env, agents
-
-
-def split_df(df):
-    '''
-    Split a dataframe in two dataframes: one with the history of agents,
-    and one with the environment history
-    '''
-    envmask = (df['agent_id'] == 'env')
-    n_env = envmask.sum()
-    if n_env == len(df):
-        return df, None
-    elif n_env == 0:
-        return None, df
-    agents, env = [x for _, x in df.groupby(envmask)]
-    return env, agents
-
-
-def process(df, **kwargs):
-    '''
-    Process a dataframe in canonical form ``(t_step, agent_id, key, value, value_type)`` into
-    two dataframes with a column per key: one with the history of the agents, and one for the
-    history of the environment.
-    '''
-    env, agents = split_df(df)
-    return process_one(env, **kwargs), process_one(agents, **kwargs)
-
-
-def get_types(df):
-    '''
-    Get the value type for every key stored in a raw history dataframe.
-    '''
-    dtypes = df.groupby(by=['key'])['value_type'].unique()
-    return {k:v[0] for k,v in dtypes.iteritems()}
-
-
-def process_one(df, *keys, columns=['key', 'agent_id'], values='value',
-                fill=True, index=['t_step',],
-                aggfunc='first', **kwargs):
-    '''
-    Process a dataframe in canonical form ``(t_step, agent_id, key, value, value_type)`` into
-    a dataframe with a column per key
-    '''
-    if df is None:
-        return df
-    if keys:
-        df = df[df['key'].isin(keys)]
-
-    df = df.pivot_table(values=values, index=index, columns=columns,
-                        aggfunc=aggfunc, **kwargs)
-    if fill:
-        df = fillna(df)
-    return df
-
-
-def get_count(df, *keys):
-    '''
-    For every t_step and key, get the value count.
-
-    The result is a dataframe with `t_step` as index, an a multiindex column based on `key` and the values found for each `key`.
-    '''
-    if keys:
-        df = df[list(keys)]
-        df.columns = df.columns.remove_unused_levels()
-    counts = pd.DataFrame()
-    for key in df.columns.levels[0]:
-        g = df[[key]].apply(pd.Series.value_counts, axis=1).fillna(0)
-        for value, series in g.iteritems():
-            counts[key, value] = series
-    counts.columns = pd.MultiIndex.from_tuples(counts.columns)
-    return counts
-
-
-def get_majority(df, *keys):
-    '''
-    For every t_step and key, get the value of the majority of agents
-
-    The result is a dataframe with `t_step` as index, and columns based on `key`.
-    '''
-    df = get_count(df, *keys)
-    return df.stack(level=0).idxmax(axis=1).unstack()
-
-
-def get_value(df, *keys, aggfunc='sum'):
-    '''
-    For every t_step and key, get the value of *numeric columns*, aggregated using a specific function.
-    '''
-    if keys:
-        df = df[list(keys)]
-        df.columns = df.columns.remove_unused_levels()
-    df = df.select_dtypes('number')
-    return df.groupby(level='key', axis=1).agg(aggfunc)
-
-
-def plot_all(*args, plot_args={}, **kwargs):
-    '''
-    Read all the trial data and plot the result of applying a function on them.
-    '''
-    dfs = do_all(*args, **kwargs)
-    ps = []
-    for line in dfs:
-        f, df, config = line
-        if len(df) < 1:
-            continue
-        df.plot(title=config['name'], **plot_args)
-        ps.append(df)
-    return ps
-
-def do_all(pattern, func, *keys, include_env=False, **kwargs):
-    for config_file, df, config in read_data(pattern, keys=keys):
-        if len(df) < 1:
-            continue
-        p = func(df, *keys, **kwargs)
-        yield config_file, p, config
-
-
-def group_trials(trials, aggfunc=['mean', 'min', 'max', 'std']):
-    trials = list(trials)
-    trials = list(map(lambda x: x[1] if isinstance(x, tuple) else x, trials))
-    return pd.concat(trials).groupby(level=0).agg(aggfunc).reorder_levels([2, 0,1] ,axis=1)
-
-
-def fillna(df):
-    new_df = df.ffill(axis=0)
-    return new_df

soil/config.py

@@ -1,27 +1,20 @@
 from __future__ import annotations
+
+from enum import Enum
 from pydantic import BaseModel, ValidationError, validator, root_validator
 
 import yaml
 import os
 import sys
 
+
 from typing import Any, Callable, Dict, List, Optional, Union, Type
 from pydantic import BaseModel, Extra
+
+from . import environment, utils
+
 import networkx as nx
 
-class General(BaseModel):
-    id: str = 'Unnamed Simulation'
-    group: str = None
-    dir_path: Optional[str] = None
-    num_trials: int = 1
-    max_time: float = 100
-    interval: float = 1
-    seed: str = ""
-
-    @staticmethod
-    def default():
-        return General()
-
 
 # Could use TypeAlias in python >= 3.10
 nodeId = int
@@ -49,7 +42,6 @@ class NetParams(BaseModel, extra=Extra.allow):
 
 
 class NetConfig(BaseModel):
-    group: str = 'network'
     params: Optional[NetParams]
     topology: Optional[Union[Topology, nx.Graph]]
     path: Optional[str]
@@ -69,9 +61,6 @@ class NetConfig(BaseModel):
 
 
 class EnvConfig(BaseModel):
-    environment_class: Union[Type, str] = 'soil.Environment'
-    params: Dict[str, Any] = {}
-    schedule: Union[Type, str] = 'soil.time.TimedActivation'
 
     @staticmethod
     def default():
@@ -80,19 +69,19 @@ class EnvConfig(BaseModel):
 
 class SingleAgentConfig(BaseModel):
     agent_class: Optional[Union[Type, str]] = None
-    agent_id: Optional[int] = None
+    unique_id: Optional[int] = None
     topology: Optional[str] = None
     node_id: Optional[Union[int, str]] = None
-    name: Optional[str] = None
     state: Optional[Dict[str, Any]] = {}
 
+
 class FixedAgentConfig(SingleAgentConfig):
     n: Optional[int] = 1
+    hidden: Optional[bool] = False  # Do not count this agent towards total agent count
 
     @root_validator
     def validate_all(cls,  values):
         if values.get('agent_id', None) is not None and values.get('n', 1) > 1:
-            print(values)
             raise ValueError(f"An agent_id can only be provided when there is only one agent ({values.get('n')} given)")
         return values
 
@@ -101,13 +90,19 @@ class OverrideAgentConfig(FixedAgentConfig):
     filter: Optional[Dict[str, Any]] = None
 
 
+class Strategy(Enum):
+    topology = 'topology'
+    total = 'total'
+
+
 class AgentDistro(SingleAgentConfig):
     weight: Optional[float] = 1
+    strategy: Strategy = Strategy.topology
 
 
 class AgentConfig(SingleAgentConfig):
     n: Optional[int] = None
-    topology: Optional[str] = None
+    topology: Optional[str]
     distribution: Optional[List[AgentDistro]] = None
     fixed: Optional[List[FixedAgentConfig]] = None
     override: Optional[List[OverrideAgentConfig]] = None
@@ -123,12 +118,33 @@ class AgentConfig(SingleAgentConfig):
         return values
 
 
-class Config(BaseModel, extra=Extra.forbid):
+class Config(BaseModel, extra=Extra.allow):
     version: Optional[str] = '1'
-    general: General = General.default()
+
-    topologies: Optional[Dict[str, NetConfig]] = {}
+    name: str = 'Unnamed Simulation'
-    environment: EnvConfig = EnvConfig.default()
+    description: Optional[str] = None
-    agents: Optional[Dict[str, AgentConfig]] = {}
+    group: str = None
+    dir_path: Optional[str] = None
+    num_trials: int = 1
+    max_time: float = 100
+    max_steps: int = -1
+    interval: float = 1
+    seed: str = ""
+    dry_run: bool = False
+
+    model_class: Union[Type, str] = environment.Environment
+    model_params: Optional[Dict[str, Any]] = {}
+
+    visualization_params: Optional[Dict[str, Any]] = {}
+
+    @classmethod
+    def from_raw(cls, cfg):
+        if isinstance(cfg, Config):
+            return cfg
+        if cfg.get('version', '1') == '1' and any(k in cfg for k in ['agents', 'agent_class', 'topology', 'environment_class']):
+            return convert_old(cfg)
+        return Config(**cfg)
+
 
 def convert_old(old, strict=True):
     '''
@@ -137,83 +153,84 @@ def convert_old(old, strict=True):
     This is still a work in progress and might not work in many cases.
     '''
 
+    utils.logger.warning('The old configuration format is deprecated. The converted file MAY NOT yield the right results')
 
-    new = {}
+    new = old.copy()
-
-
-    general = {}
-    for k in ['id', 
-              'group',
-              'dir_path',
-              'num_trials',
-              'max_time',
-              'interval',
-              'seed']:
-        if k in old:
-            general[k] = old[k]
-
-    if 'name' in old:
-        general['id'] = old['name']
 
     network = {}
 
+    if 'topology' in old:
+        del new['topology']
+        network['topology'] = old['topology']
 
     if 'network_params' in old and old['network_params']:
+        del new['network_params']
         for (k, v) in old['network_params'].items():
             if k == 'path':
                 network['path'] = v
             else:
                 network.setdefault('params', {})[k] = v
 
-    if 'topology' in old:
+    topologies = {}
-        network['topology'] = old['topology']
+    if network:
+        topologies['default'] = network
 
-    agents = {
-        'network': {},
-        'default': {},
-    }
-
-    if 'agent_type' in old:
-        agents['default']['agent_class'] = old['agent_type']
-
-    if 'default_state' in old:
-        agents['default']['state'] = old['default_state']
 
+    agents = {'fixed': [], 'distribution': []}
 
     def updated_agent(agent):
+        '''Convert an agent definition'''
         newagent = dict(agent)
-        newagent['agent_class'] = newagent['agent_type']
-        del newagent['agent_type']
         return newagent
 
-    for agent in old.get('environment_agents', []):
-        agents['environment'] = {'distribution': [], 'fixed': []}
-        if 'agent_id' in agent:
-            agent['name'] = agent['agent_id']
-            del agent['agent_id']
-        agents['environment']['fixed'].append(updated_agent(agent))
-
     by_weight = []
     fixed = []
     override = []
 
-    if 'network_agents' in old:
+    if 'environment_agents' in new:
-        agents['network']['topology'] = 'default'
 
-        for agent in old['network_agents']:
+        for agent in new['environment_agents']:
+            agent.setdefault('state', {})['group'] = 'environment'
+            if 'agent_id' in agent:
+                agent['state']['name'] = agent['agent_id']
+                del agent['agent_id']
+            agent['hidden'] = True
+            agent['topology'] = None
+            fixed.append(updated_agent(agent))
+        del new['environment_agents']
+
+
+    if 'agent_class' in old:
+        del new['agent_class']
+        agents['agent_class'] = old['agent_class']
+
+    if 'default_state' in old:
+        del new['default_state']
+        agents['state'] = old['default_state']
+
+    if 'network_agents' in old:
+        agents['topology'] = 'default'
+
+        agents.setdefault('state', {})['group'] = 'network'
+
+        for agent in new['network_agents']:
             agent = updated_agent(agent)
             if 'agent_id' in agent:
+                agent['state']['name'] = agent['agent_id']
+                del agent['agent_id']
                 fixed.append(agent)
             else:
                 by_weight.append(agent)
+        del new['network_agents']
 
-    if 'agent_type' in old and (not fixed and not by_weight):
+    if 'agent_class' in old and (not fixed and not by_weight):
-        agents['network']['topology'] = 'default'
+        agents['topology'] = 'default'
-        by_weight = [{'agent_class': old['agent_type']}]
+        by_weight = [{'agent_class': old['agent_class'], 'weight': 1}]
 
     
     # TODO: translate states properly
     if 'states' in old:
+        del new['states']
         states = old['states']
         if isinstance(states, dict):
             states = states.items()
@@ -221,22 +238,29 @@ def convert_old(old, strict=True):
             states = enumerate(states)
         for (k, v) in states:
             override.append({'filter': {'node_id': k},
-                             'state': v
+                             'state': v})
-            })
 
-    agents['network']['override'] = override
+    agents['override'] = override
-    agents['network']['fixed'] = fixed
+    agents['fixed'] = fixed
-    agents['network']['distribution'] = by_weight
+    agents['distribution'] = by_weight
+
+
+    model_params = {}
+    if 'environment_params' in new:
+        del new['environment_params']
+        model_params = dict(old['environment_params'])
 
-    environment = {'params': {}}
     if 'environment_class' in old:
-        environment['environment_class'] = old['environment_class']
+        del new['environment_class']
+        new['model_class'] = old['environment_class']
 
-    for (k, v) in old.get('environment_params', {}).items():
+    if 'dump' in old:
-        environment['params'][k] = v
+        del new['dump']
+        new['dry_run'] = not old['dump']
+
+    model_params['topologies'] = topologies
+    model_params['agents'] = agents
 
     return Config(version='2',
-                  general=general,
+                  model_params=model_params,
-                  topologies={'default': network},
+                  **new)
-                  environment=environment,
-                  agents=agents)

soil/datacollection.py

@@ -2,23 +2,5 @@ from mesa import DataCollector as MDC
 
 class SoilDataCollector(MDC):
 
-
+    def __init__(self, *args, **kwargs):
-    def __init__(self, environment, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        # Populate model and env reporters so they have a key per 
-        # So they can be shown in the web interface
-        self.environment = environment
-        raise NotImplementedError()
-
-    @property
-    def model_vars(self):
-        raise NotImplementedError()
-
-    @model_vars.setter
-    def model_vars(self, value):
-        raise NotImplementedError()
-
-    @property
-    def agent_reporters(self):
-        raise NotImplementedError()
-

soil/debugging.py

@@ -0,0 +1,151 @@
+from __future__ import annotations
+
+import pdb
+import sys
+import os
+
+from textwrap import indent
+from functools import wraps
+
+from .agents import FSM, MetaFSM
+
+
+def wrapcmd(func):
+    @wraps(func)
+    def wrapper(self, arg: str, temporary=False):
+        sys.settrace(self.trace_dispatch)
+
+        known = globals()
+        known.update(self.curframe.f_globals)
+        known.update(self.curframe.f_locals)
+        known['agent'] = known.get('self', None)
+        known['model'] = known.get('self', {}).get('model')
+        known['attrs'] = arg.strip().split()
+
+        exec(func.__code__, known, known)
+
+    return wrapper
+
+
+class Debug(pdb.Pdb):
+    def __init__(self, *args, skip_soil=False, **kwargs):
+        skip = kwargs.get('skip', [])
+        if skip_soil:
+            skip.append('soil.*')
+            skip.append('mesa.*')
+        super(Debug, self).__init__(*args, skip=skip, **kwargs)
+        self.prompt = "[soil-pdb] "
+
+    @staticmethod
+    def _soil_agents(model, attrs=None, pretty=True, **kwargs):
+        for agent in model.agents(**kwargs):
+            d = agent
+            print(' - ' + indent(agent.to_str(keys=attrs, pretty=pretty), '  '))
+
+    @wrapcmd
+    def do_soil_agents():
+        return Debug._soil_agents(model, attrs=attrs or None)
+
+    do_sa = do_soil_agents
+
+    @wrapcmd
+    def do_soil_list():
+        return Debug._soil_agents(model, attrs=['state_id'], pretty=False)
+
+    do_sl = do_soil_list
+
+    @wrapcmd
+    def do_soil_self():
+        if not agent:
+            print('No agent available')
+            return
+
+        keys = None
+        if attrs:
+            keys = []
+            for k in attrs:
+                for key in agent.keys():
+                    if key.startswith(k):
+                        keys.append(key)
+
+        print(agent.to_str(pretty=True, keys=keys))
+
+    do_ss = do_soil_self
+
+    def do_break_state(self, arg: str, temporary=False):
+        '''
+        Break before a specified state is stepped into.
+        '''
+
+        klass = None
+        state = arg.strip()
+        if not state:
+            self.error("Specify at least a state name")
+            return
+
+        comma = arg.find(':')
+        if comma > 0:
+            state = arg[comma+1:].lstrip()
+            klass = arg[:comma].rstrip()
+            klass = eval(klass,
+                         self.curframe.f_globals,
+                         self.curframe_locals)
+
+        if klass:
+            klasses = [klass]
+        else:
+            klasses = [k for k in self.curframe.f_globals.values() if isinstance(k, type) and issubclass(k, FSM)]
+            print(klasses)
+            if not klasses:
+                self.error('No agent classes found')
+        
+        for klass in klasses:
+            try:
+                func = getattr(klass, state)
+            except AttributeError:
+                continue
+            if hasattr(func, '__func__'):
+                func = func.__func__
+
+            code = func.__code__
+            #use co_name to identify the bkpt (function names
+            #could be aliased, but co_name is invariant)
+            funcname = code.co_name
+            lineno = code.co_firstlineno
+            filename = code.co_filename
+
+            # Check for reasonable breakpoint
+            line = self.checkline(filename, lineno)
+            if not line:
+                raise ValueError('no line found')
+                # now set the break point
+            cond = None
+            existing = self.get_breaks(filename, line)
+            if existing:
+                self.message("Breakpoint already exists at %s:%d" %
+                              (filename, line))
+                continue
+            err = self.set_break(filename, line, temporary, cond, funcname)
+            if err:
+                self.error(err)
+            else:
+                bp = self.get_breaks(filename, line)[-1]
+                self.message("Breakpoint %d at %s:%d" %
+                              (bp.number, bp.file, bp.line))
+    do_bs = do_break_state
+
+
+def setup(frame=None):
+    debugger = Debug()
+    frame = frame or sys._getframe().f_back
+    debugger.set_trace(frame)
+
+def debug_env():
+    if os.environ.get('SOIL_DEBUG'):
+        return setup(frame=sys._getframe().f_back)
+
+def post_mortem(traceback=None):
+    p = Debug()
+    t = sys.exc_info()[2]
+    p.reset()
+    p.interaction(None, t)

soil/environment.py

@@ -1,11 +1,12 @@
 from __future__ import annotations
+
 import os
 import sqlite3
 import math
 import random
 import logging
 
-from typing import Dict
+from typing import Any, Dict, Optional, Union
 from collections import namedtuple
 from time import time as current_time
 from copy import deepcopy
@@ -17,90 +18,113 @@ import networkx as nx
 from mesa import Model
 from mesa.datacollection import DataCollector
 
-from . import serialization, agents, analysis, utils, time, config, network
+from . import agents as agentmod, config, serialization, utils, time, network
 
 
 Record = namedtuple('Record', 'dict_id t_step key value')
 
 
-class Environment(Model):
+class BaseEnvironment(Model):
     """
-    The environment is key in a simulation. It contains the network topology,
+    The environment is key in a simulation. It controls how agents interact,
-    a reference to network and environment agents, as well as the environment
+    and what information is available to them.
-    params, which are used as shared state between agents.
+
+    This is an opinionated version of `mesa.Model` class, which adds many
+    convenience methods and abstractions.
 
     The environment parameters and the state of every agent can be accessed
-    both by using the environment as a dictionary or with the environment's
+    both by using the environment as a dictionary and with the environment's
     :meth:`soil.environment.Environment.get` method.
     """
 
     def __init__(self,
-                 env_id='unnamed_env',
+                 id='unnamed_env',
                  seed='default',
                  schedule=None,
                  dir_path=None,
                  interval=1,
-                 agents: Dict[str, config.AgentConfig] = {},
+                 agent_class=None,
-                 topologies: Dict[str, config.NetConfig] = {},
+                 agents: [tuple[type, Dict[str, Any]]] = {},
                  agent_reporters: Optional[Any] = None,
                  model_reporters: Optional[Any] = None,
                  tables: Optional[Any] = None,
                  **env_params):
 
-        super().__init__()
+        super().__init__(seed=seed)
         self.current_id = -1
 
-        self.seed = '{}_{}'.format(seed, env_id)
+        self.id = id
-        self.id = env_id
 
         self.dir_path = dir_path or os.getcwd()
 
         if schedule is None:
-            schedule = time.TimedActivation()
+            schedule = time.TimedActivation(self)
         self.schedule = schedule
 
-        seed = seed or current_time()
+        self.agent_class = agent_class or agentmod.BaseAgent
 
-        random.seed(seed)
+        self.init_agents(agents)
-
-
-        self.topologies = {}
-        self._node_ids = {}
-        for (name, cfg) in topologies.items():
-            self.set_topology(cfg=cfg,
-                              graph=name)
-        self.agents = agents or {}
 
         self.env_params = env_params or {}
 
         self.interval = interval
-        self['SEED'] = seed
 
         self.logger = utils.logger.getChild(self.id)
-        self.datacollector = DataCollector(model_reporters, agent_reporters, tables)
+
+        self.datacollector = DataCollector(
+            model_reporters=model_reporters,
+            agent_reporters=agent_reporters,
+            tables=tables,
+        )
+
+    def _read_single_agent(self, agent):
+        agent = dict(**agent)
+        cls = agent.pop('agent_class', None) or self.agent_class
+        unique_id = agent.pop('unique_id', None)
+        if unique_id is None:
+            unique_id = self.next_id()
+
+        return serialization.deserialize(cls)(unique_id=unique_id,
+                                              model=self, **agent)
+
+    def init_agents(self, agents: Union[config.AgentConfig, [Dict[str, Any]]] = {}):
+        if not agents:
+            return
+
+        lst = agents
+        override = []
+        if not isinstance(lst, list):
+            if not isinstance(agents, config.AgentConfig):
+                lst = config.AgentConfig(**agents)
+            if lst.override:
+                override = lst.override
+            lst = agentmod.from_config(lst,
+                                       topologies=getattr(self, 'topologies', None),
+                                       random=self.random)
+
+        #TODO: check override is working again. It cannot (easily) be part of agents.from_config anymore, 
+        # because it needs attribute such as unique_id, which are only present after init
+        new_agents = [self._read_single_agent(agent) for agent in lst]
+
+
+        for a in new_agents:
+            self.schedule.add(a)
+
+        for rule in override:
+            for agent in agentmod.filter_agents(self.schedule._agents, **rule.filter):
+                for attr, value in rule.state.items():
+                    setattr(agent, attr, value)
+
 
     @property
-    def topology(self):
+    def agents(self):
-        return self.topologies['default']
+        return agentmod.AgentView(self.schedule._agents)
 
-    @property
+    def find_one(self, *args, **kwargs):
-    def network_agents(self):
+        return agentmod.AgentView(self.schedule._agents).one(*args, **kwargs)
-        yield from self.agents(agent_class=agents.NetworkAgent)
     
-    @staticmethod
+    def count_agents(self, *args, **kwargs):
-    def from_config(conf: config.Config, trial_id, **kwargs) -> Environment:
+        return sum(1 for i in self.agents(*args, **kwargs))
-        '''Create an environment for a trial of the simulation'''
-        conf = conf
-        if kwargs:
-            conf = config.Config(**conf.dict(exclude_defaults=True), **kwargs)
-        seed = '{}_{}'.format(conf.general.seed, trial_id)
-        id = '{}_trial_{}'.format(conf.general.id, trial_id).replace('.', '-')
-        opts = conf.environment.params.copy()
-        dir_path = conf.general.dir_path
-        opts.update(conf)
-        opts.update(kwargs)
-        env = serialization.deserialize(conf.environment.environment_class)(env_id=id, seed=seed, dir_path=dir_path, **opts)
-        return env
 
     @property
     def now(self):
@@ -109,115 +133,16 @@ class Environment(Model):
         raise Exception('The environment has not been scheduled, so it has no sense of time')
 
 
-    def topology_for(self, agent_id):
+    def add_agent(self, agent_id, agent_class, **kwargs):
-        return self.topologies[self._node_ids[agent_id][0]]
-
-    def node_id_for(self, agent_id):
-        return self._node_ids[agent_id][1]
-
-    def set_topology(self, cfg=None, dir_path=None, graph='default'):
-        topology = cfg
-        if not isinstance(cfg, nx.Graph):
-            topology = network.from_config(cfg, dir_path=dir_path or self.dir_path)
-
-        self.topologies[graph] = topology
-
-    @property
-    def agents(self):
-        return agents.AgentView(self._agents)
-    
-    def count_agents(self, *args, **kwargs):
-        return sum(1 for i in self.find_all(*args, **kwargs))
-
-    def find_all(self, *args, **kwargs):
-        return agents.AgentView(self._agents).filter(*args, **kwargs)
-
-    def find_one(self, *args, **kwargs):
-        return agents.AgentView(self._agents).one(*args, **kwargs)
-
-    @agents.setter
-    def agents(self, agents_def: Dict[str, config.AgentConfig]):
-        self._agents = agents.from_config(agents_def, env=self)
-        for d in self._agents.values():
-            for a in d.values():
-                self.schedule.add(a)
-
-    def init_agent(self, agent_id, agent_definitions, graph='default'):
-        node = self.topologies[graph].nodes[agent_id]
-        init = False
-        state = dict(node)
-
-        agent_class = None
-        if 'agent_class' in self.states.get(agent_id, {}):
-            agent_class = self.states[agent_id]['agent_class']
-        elif 'agent_class' in node:
-            agent_class = node['agent_class']
-        elif 'agent_class' in self.default_state:
-            agent_class = self.default_state['agent_class']
-
-        if agent_class:
-            agent_class = agents.deserialize_type(agent_class)
-        elif agent_definitions:
-            agent_class, state = agents._agent_from_definition(agent_definitions, unique_id=agent_id)
-        else:
-            serialization.logger.debug('Skipping node {}'.format(agent_id))
-            return
-        return self.set_agent(agent_id, agent_class, state)
-
-    def agent_to_node(self, agent_id, graph_name='default', node_id=None, shuffle=False):
-        #TODO: test
-        if node_id is None:
-            G = self.topologies[graph_name]
-            candidates = list(G.nodes(data=True))
-            if shuffle:
-                random.shuffle(candidates)
-            for next_id, data in candidates:
-                if data.get('agent_id', None) is None:
-                    node_id = next_id
-                    data['agent_id'] = agent_id
-                    break
-                    
-
-        self._node_ids[agent_id] = (graph_name, node_id)
-        print(self._node_ids)
-
-
-    def set_agent(self, agent_id, agent_class, state=None, graph='default'):
-        node = self.topologies[graph].nodes[agent_id]
-        defstate = deepcopy(self.default_state) or {}
-        defstate.update(self.states.get(agent_id, {}))
-        defstate.update(node.get('state', {}))
-        if state:
-            defstate.update(state)
         a = None
         if agent_class:
-            state = defstate
             a = agent_class(model=self,
-                           unique_id=agent_id
+                            unique_id=agent_id,
-            )
+                            **kwargs)
 
-        for (k, v) in state.items():
-            setattr(a, k, v)
-
-        node['agent'] = a
         self.schedule.add(a)
         return a
 
-    def add_node(self, agent_class, state=None, graph='default'):
-        agent_id = int(len(self.topologies[graph].nodes()))
-        self.topologies[graph].add_node(agent_id)
-        a = self.set_agent(agent_id, agent_class, state, graph=graph)
-        a['visible'] = True
-        return a
-
-    def add_edge(self, agent1, agent2, start=None, graph='default', **attrs):
-        if hasattr(agent1, 'id'):
-            agent1 = agent1.id
-        if hasattr(agent2, 'id'):
-            agent2 = agent2.id
-        start = start or self.now
-        return self.topologies[graph].add_edge(agent1, agent2, **attrs)
-
     def log(self, message, *args, level=logging.INFO, **kwargs):
         if not self.logger.isEnabledFor(level):
             return
@@ -227,7 +152,7 @@ class Environment(Model):
             message += " {k}={v} ".format(k, v)
         extra = {}
         extra['now'] = self.now
-        extra['unique_id'] = self.id
+        extra['id'] = self.id
         return self.logger.log(level, message, extra=extra)
 
     def step(self):
@@ -235,17 +160,10 @@ class Environment(Model):
         Advance one step in the simulation, and update the data collection and scheduler appropriately
         '''
         super().step()
+        self.logger.info(f'--- Step {self.now:^5} ---')
         self.schedule.step()
         self.datacollector.collect(self)
 
-    def run(self, until, *args, **kwargs):
-        until = until or float('inf')
-
-        while self.schedule.next_time < until:
-            self.step()
-            utils.logger.debug(f'Simulation step {self.schedule.time}/{until}. Next: {self.schedule.next_time}')
-        self.schedule.time = until
-
     def __contains__(self, key):
         return key in self.env_params
 
@@ -289,5 +207,96 @@ class Environment(Model):
             yield from self._agent_to_tuples(agent, now)
 
 
+class NetworkEnvironment(BaseEnvironment):
 
-SoilEnvironment = Environment
+    def __init__(self, *args, topology: nx.Graph = None, topologies: Dict[str, config.NetConfig] = {}, **kwargs):
+        agents = kwargs.pop('agents', None)
+        super().__init__(*args, agents=None, **kwargs)
+        self._node_ids = {}
+        assert not hasattr(self, 'topologies')
+        if topology is not None:
+            if topologies:
+                raise ValueError('Please, provide either a single topology or a dictionary of them')
+            topologies = {'default': topology}
+
+        self.topologies = {}
+        for (name, cfg) in topologies.items():
+            self.set_topology(cfg=cfg, graph=name)
+
+        self.init_agents(agents)
+
+
+    def _read_single_agent(self, agent, unique_id=None):
+        agent = dict(agent)
+
+        if agent.get('topology', None) is not None:
+            topology = agent.get('topology')
+            if unique_id is None:
+                unique_id = self.next_id()
+            if topology:
+                node_id = self.agent_to_node(unique_id, graph_name=topology, node_id=agent.get('node_id'))
+                agent['node_id'] = node_id
+                agent['topology'] = topology
+            agent['unique_id'] = unique_id
+
+        return super()._read_single_agent(agent)
+        
+
+    @property
+    def topology(self):
+        return self.topologies['default']
+
+    def set_topology(self, cfg=None, dir_path=None, graph='default'):
+        topology = cfg
+        if not isinstance(cfg, nx.Graph):
+            topology = network.from_config(cfg, dir_path=dir_path or self.dir_path)
+
+        self.topologies[graph] = topology
+
+    def topology_for(self, unique_id):
+        return self.topologies[self._node_ids[unique_id][0]]
+
+    @property
+    def network_agents(self):
+        yield from self.agents(agent_class=agentmod.NetworkAgent)
+
+    def agent_to_node(self, unique_id, graph_name='default',
+                      node_id=None, shuffle=False):
+        node_id = network.agent_to_node(G=self.topologies[graph_name],
+                                        agent_id=unique_id,
+                                        node_id=node_id,
+                                        shuffle=shuffle,
+                                        random=self.random)
+
+        self._node_ids[unique_id] = (graph_name, node_id)
+        return node_id
+
+    def add_node(self, agent_class, topology, **kwargs):
+        unique_id = self.next_id()
+        self.topologies[topology].add_node(unique_id)
+        node_id = self.agent_to_node(unique_id=unique_id, node_id=unique_id, graph_name=topology)
+
+        a = self.add_agent(unique_id=unique_id, agent_class=agent_class, node_id=node_id, topology=topology, **kwargs)
+        a['visible'] = True
+        return a
+
+    def add_edge(self, agent1, agent2, start=None, graph='default', **attrs):
+        agent1 = agent1.node_id
+        agent2 = agent2.node_id
+        return self.topologies[graph].add_edge(agent1, agent2, start=start)
+
+    def add_agent(self, unique_id, state=None, graph='default', **kwargs):
+        node = self.topologies[graph].nodes[unique_id]
+        node_state = node.get('state', {})
+        if node_state:
+            node_state.update(state or {})
+            state = node_state
+        a = super().add_agent(unique_id, state=state, **kwargs)
+        node['agent'] = a
+        return a
+
+    def node_id_for(self, agent_id):
+        return self._node_ids[agent_id][1]
+
+
+Environment = NetworkEnvironment

soil/exporters.py

@@ -12,7 +12,7 @@ from .serialization import deserialize
 from .utils import open_or_reuse, logger, timer
 
 
-from . import utils
+from . import utils, network
 
 
 class DryRunner(BytesIO):
@@ -49,8 +49,8 @@ class Exporter:
         self.simulation = simulation
         outdir = outdir or os.path.join(os.getcwd(), 'soil_output')
         self.outdir = os.path.join(outdir,
-                                   simulation.config.general.group or '',
+                                   simulation.group or '',
-                                   simulation.config.general.id)
+                                   simulation.name)
         self.dry_run = dry_run
         self.copy_to = copy_to
 
@@ -85,38 +85,28 @@ class Exporter:
 class default(Exporter):
     '''Default exporter. Writes sqlite results, as well as the simulation YAML'''
 
-    # def sim_start(self):
+    def sim_start(self):
-    #     if not self.dry_run:
+        if not self.dry_run:
-    #         logger.info('Dumping results to %s', self.outdir)
+            logger.info('Dumping results to %s', self.outdir)
-    #         self.simulation.dump_yaml(outdir=self.outdir)
+            with self.output(self.simulation.name + '.dumped.yml') as f:
-    #     else:
+                f.write(self.simulation.to_yaml())
-    #         logger.info('NOT dumping results')
+        else:
+            logger.info('NOT dumping results')
 
-    # def trial_start(self, env, stats):
+    def trial_end(self, env):
-    #     if not self.dry_run:
+        if not self.dry_run:
-    #         with timer('Dumping simulation {} trial {}'.format(self.simulation.name,
+            with timer('Dumping simulation {} trial {}'.format(self.simulation.name,
-    #                                                            env.name)):
+                                                               env.id)):
-    #             engine = create_engine('sqlite:///{}.sqlite'.format(env.name), echo=False)
+                engine = create_engine('sqlite:///{}.sqlite'.format(env.id), echo=False)
 
-    #             dc = env.datacollector
+                dc = env.datacollector
-    #             tables = {'env': dc.get_model_vars_dataframe(),
+                for (t, df) in get_dc_dfs(dc):
-    #                       'agents': dc.get_agent_vars_dataframe(),
+                    df.to_sql(t, con=engine, if_exists='append')
-    #                       'agents': dc.get_agent_vars_dataframe()}
-    #             for table in dc.tables:
-    #                 tables[table] = dc.get_table_dataframe(table)
-    #             for (t, df) in tables.items():
-    #                 df.to_sql(t, con=engine)
-
-    # def sim_end(self, stats):
-    #       with timer('Dumping simulation {}\'s stats'.format(self.simulation.name)):
-    #           engine = create_engine('sqlite:///{}.sqlite'.format(self.simulation.name), echo=False)
-    #           with self.output('{}.sqlite'.format(self.simulation.name), mode='wb') as f:
-    #               self.simulation.dump_sqlite(f)
 
 
 def get_dc_dfs(dc):
     dfs = {'env': dc.get_model_vars_dataframe(),
-        'agents': dc.get_agent_vars_dataframe }
+           'agents': dc.get_agent_vars_dataframe() }
     for table_name in dc.tables:
         dfs[table_name] = dc.get_table_dataframe(table_name)
     yield from dfs.items() 
@@ -130,10 +120,11 @@ class csv(Exporter):
                                                                           env.id,
                                                                           self.outdir)):
             for (df_name, df) in get_dc_dfs(env.datacollector):
-                with self.output('{}.stats.{}.csv'.format(env.id, df_name)) as f:
+                with self.output('{}.{}.csv'.format(env.id, df_name)) as f:
                     df.to_csv(f)
 
 
+#TODO: reimplement GEXF exporting without history
 class gexf(Exporter):
     def trial_end(self, env):
         if self.dry_run:
@@ -143,18 +134,9 @@ class gexf(Exporter):
         with timer('[GEXF] Dumping simulation {} trial {}'.format(self.simulation.name,
                                                                   env.id)):
             with self.output('{}.gexf'.format(env.id), mode='wb') as f:
+                network.dump_gexf(env.history_to_graph(), f)
                 self.dump_gexf(env, f)
 
-    def dump_gexf(self, env, f):
-        G = env.history_to_graph()
-        # Workaround for geometric models
-        # See soil/soil#4
-        for node in G.nodes():
-            if 'pos' in G.nodes[node]:
-                G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}}
-                del (G.nodes[node]['pos'])
-
-        nx.write_gexf(G, f, version="1.2draft")
 
 class dummy(Exporter):
 

soil/network.py

@@ -1,6 +1,9 @@
+from __future__ import annotations
+
 from typing import Dict
 import os
 import sys
+import random
 
 import networkx as nx
 
@@ -36,7 +39,40 @@ def from_config(cfg: config.NetConfig, dir_path: str = None):
                                             known_modules=['networkx.generators',])
         return method(**net_args)
 
-    if isinstance(cfg.topology, basestring) or isinstance(cfg.topology, dict):
+    if isinstance(cfg.topology, config.Topology):
-        return nx.json_graph.node_link_graph(cfg.topology)
+        cfg = cfg.topology.dict()
+    if isinstance(cfg, str) or isinstance(cfg, dict):
+        return nx.json_graph.node_link_graph(cfg)
 
     return nx.Graph()
+
+
+def agent_to_node(G, agent_id, node_id=None, shuffle=False, random=random):
+    '''
+    Link an agent to a node in a topology.
+
+    If node_id is None, a node without an agent_id will be found.
+    '''
+    #TODO: test
+    if node_id is None:
+        candidates = list(G.nodes(data=True))
+        if shuffle:
+            random.shuffle(candidates)
+        for next_id, data in candidates:
+            if data.get('agent_id', None) is None:
+                node_id = next_id
+                break
+
+    if node_id is None:
+        raise ValueError(f"Not enough nodes in topology to assign one to agent {agent_id}")
+    G.nodes[node_id]['agent_id'] = agent_id
+    return node_id
+
+
+def dump_gexf(G, f):
+    for node in G.nodes():
+        if 'pos' in G.nodes[node]:
+            G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}}
+            del (G.nodes[node]['pos'])
+
+    nx.write_gexf(G, f, version="1.2draft")

soil/serialization.py

@@ -7,6 +7,8 @@ import importlib
 from glob import glob
 from itertools import product, chain
 
+from .config import Config
+
 import yaml
 import networkx as nx
 
@@ -120,24 +122,25 @@ def params_for_template(config):
 def load_files(*patterns, **kwargs):
     for pattern in patterns:
         for i in glob(pattern, **kwargs):
-            for config in load_file(i):
+            for cfg in load_file(i):
                 path = os.path.abspath(i)
-                if 'general' in config and 'dir_path' not in config['general']:
+                yield Config.from_raw(cfg), path
-                    config['general']['dir_path'] = os.path.dirname(path)
-                yield config, path
 
 
-def load_config(config):
+def load_config(cfg):
-    if isinstance(config, dict):
+    if isinstance(cfg, Config):
-        yield config, os.getcwd()
+        yield cfg, os.getcwd()
+    elif isinstance(cfg, dict):
+        yield Config.from_raw(cfg), os.getcwd()
     else:
-        yield from load_files(config)
+        yield from load_files(cfg)
 
 
 builtins = importlib.import_module('builtins')
 
 KNOWN_MODULES = ['soil', ]
 
+
 def name(value, known_modules=KNOWN_MODULES):
     '''Return a name that can be imported, to serialize/deserialize an object'''
     if value is None:
@@ -174,8 +177,22 @@ def serialize(v, known_modules=KNOWN_MODULES):
     return func(v), tname
 
 
+def serialize_dict(d, known_modules=KNOWN_MODULES):
+    d = dict(d)
+    for (k, v) in d.items():
+        if isinstance(v, dict):
+            d[k] = serialize_dict(v, known_modules=known_modules)
+        elif isinstance(v, list):
+            for ix in range(len(v)):
+                v[ix] = serialize_dict(v[ix], known_modules=known_modules)
+        elif isinstance(v, type):
+            d[k] = serialize(v, known_modules=known_modules)[1]
+    return d
+
+
 IS_CLASS = re.compile(r"<class '(.*)'>")
 
+
 def deserializer(type_, known_modules=KNOWN_MODULES):
     if type(type_) != str:  # Already deserialized
         return type_

soil/simulation.py

@@ -4,23 +4,30 @@ import importlib
 import sys
 import yaml
 import traceback
+import inspect
 import logging
 import networkx as nx
 
+from textwrap import dedent
+
+from dataclasses import dataclass, field, asdict
+from typing import Any, Dict, Union, Optional
+
+
 from networkx.readwrite import json_graph
-from multiprocessing import Pool
 from functools import partial
 import pickle
 
 from . import serialization, utils, basestring, agents
 from .environment import Environment
-from .utils import logger
+from .utils import logger, run_and_return_exceptions
 from .exporters import default
-
+from .time import INFINITY
 from .config import Config, convert_old
 
 
 #TODO: change documentation for simulation
+@dataclass
 class Simulation:
     """
     Parameters
@@ -30,45 +37,47 @@ class Simulation:
 
     kwargs: parameters to use to initialize a new configuration, if one has not been provided.
     """
+    version: str = '2'
+    name: str = 'Unnamed simulation'
+    description: Optional[str] = ''
+    group: str = None
+    model_class: Union[str, type] = 'soil.Environment'
+    model_params: dict = field(default_factory=dict)
+    seed: str = field(default_factory=lambda: current_time())
+    dir_path: str = field(default_factory=lambda: os.getcwd())
+    max_time: float = float('inf')
+    max_steps: int = -1
+    interval: int = 1
+    num_trials: int = 3
+    dry_run: bool = False
+    extra: Dict[str, Any] = field(default_factory=dict)
 
-    def __init__(self, config=None,
+    @classmethod
-                 **kwargs):
+    def from_dict(cls, env):      
-        if kwargs:
-            cfg = {}
-            if config:
-                cfg.update(config.dict(include_defaults=False))
-            cfg.update(kwargs)
-            config = Config(**cfg)
-        if not config:
-            raise ValueError("You need to specify a simulation configuration")
-        self.config = config
 
+        ignored = {k: v for k, v in env.items() 
+                   if k not in inspect.signature(cls).parameters}
 
-    @property
+        kwargs = {k:v for k, v in env.items() if k not in ignored}
-    def name(self) -> str:
+        if ignored:
-        return self.config.general.id
+            kwargs.setdefault('extra', {}).update(ignored)
+        if ignored:
+            print(f'Warning: Ignoring these parameters (added to "extra"): { ignored }')
+
+        return cls(**kwargs)
 
     def run_simulation(self, *args, **kwargs):
         return self.run(*args, **kwargs)
 
     def run(self, *args, **kwargs):
         '''Run the simulation and return the list of resulting environments'''
+        logger.info(dedent('''
+        Simulation:
+        ---
+        ''') +
+        self.to_yaml())
         return list(self.run_gen(*args, **kwargs))
 
-    def _run_sync_or_async(self, parallel=False, **kwargs):
-        if parallel and not os.environ.get('SENPY_DEBUG', None):
-            p = Pool()
-            func = partial(self.run_trial_exceptions, **kwargs)
-            for i in p.imap_unordered(func, range(self.config.general.num_trials)):
-                if isinstance(i, Exception):
-                    logger.error('Trial failed:\n\t%s', i.message)
-                    continue
-                yield i
-        else:
-            for i in range(self.config.general.num_trials):
-                yield self.run_trial(trial_id=i,
-                                     **kwargs)
-
     def run_gen(self, parallel=False, dry_run=False,
                 exporters=[default, ], outdir=None, exporter_params={},
                 log_level=None,
@@ -80,16 +89,18 @@ class Simulation:
         logger.info('Output directory: %s', outdir)
         exporters = serialization.deserialize_all(exporters,
                                                   simulation=self,
-                                                  known_modules=['soil.exporters',],
+                                                  known_modules=['soil.exporters', ],
                                                   dry_run=dry_run,
                                                   outdir=outdir,
                                                   **exporter_params)
 
-        with utils.timer('simulation {}'.format(self.config.general.id)):
+        with utils.timer('simulation {}'.format(self.name)):
             for exporter in exporters:
                 exporter.sim_start()
 
-            for env in self._run_sync_or_async(parallel=parallel,
+            for env in utils.run_parallel(func=self.run_trial,
+                                          iterable=range(int(self.num_trials)),
+                                          parallel=parallel,
                                           log_level=log_level,
                                           **kwargs):
 
@@ -106,93 +117,105 @@ class Simulation:
 
     def get_env(self, trial_id=0, **kwargs):
         '''Create an environment for a trial of the simulation'''
-        # opts = self.environment_params.copy()
+        def deserialize_reporters(reporters):
-        # opts.update({
+            for (k, v) in reporters.items():
-        #     'name': '{}_trial_{}'.format(self.name, trial_id),
+                if isinstance(v, str) and v.startswith('py:'):
-        #     'topology': self.topology.copy(),
+                    reporters[k] = serialization.deserialize(value.lsplit(':', 1)[1])
-        #     'network_params': self.network_params,
-        #     'seed': '{}_trial_{}'.format(self.seed, trial_id),
-        #     'initial_time': 0,
-        #     'interval': self.interval,
-        #     'network_agents': self.network_agents,
-        #     'initial_time': 0,
-        #     'states': self.states,
-        #     'dir_path': self.dir_path,
-        #     'default_state': self.default_state,
-        #     'history': bool(self._history),
-        #     'environment_agents': self.environment_agents,
-        # })
-        # opts.update(kwargs)
-        print(self.config)
-        env = Environment.from_config(self.config, trial_id=trial_id, **kwargs)
-        return env
 
-    def run_trial(self, trial_id=None, until=None, log_level=logging.INFO, **opts):
+        model_params = self.model_params.copy()
+        model_params.update(kwargs)
+
+        agent_reporters = deserialize_reporters(model_params.pop('agent_reporters', {}))
+        model_reporters = deserialize_reporters(model_params.pop('model_reporters', {}))
+
+        env =  serialization.deserialize(self.model_class)
+        return env(id=f'{self.name}_trial_{trial_id}',
+                   seed=f'{self.seed}_trial_{trial_id}',
+                   dir_path=self.dir_path,
+                   agent_reporters=agent_reporters,
+                   model_reporters=model_reporters,
+                   **model_params)
+
+    def run_trial(self, trial_id=None, until=None, log_file=False, log_level=logging.INFO, **opts):
         """
         Run a single trial of the simulation
 
         """
-        trial_id = trial_id if trial_id is not None else current_time()
         if log_level:
             logger.setLevel(log_level)
+        model = self.get_env(trial_id, **opts)
+        trial_id = trial_id if trial_id is not None else current_time()
+        with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)):
+            return self.run_model(model=model, trial_id=trial_id, until=until, log_level=log_level)
+
+    def run_model(self, model, until=None, **opts):
         # Set-up trial environment and graph
-        until = until or self.config.general.max_time
+        until = float(until or self.max_time or 'inf')
 
-        env = self.get_env(trial_id, **opts)
         # Set up agents on nodes
-        with utils.timer('Simulation {} trial {}'.format(self.config.general.id, trial_id)):
+        def is_done():
-            env.run(until)
+            return False
-        return env
 
-    def run_trial_exceptions(self, *args, **kwargs):
+        if until and hasattr(model.schedule, 'time'):
-        '''
+            prev = is_done
-        A wrapper for run_trial that catches exceptions and returns them.
+
-        It is meant for async simulations
+            def is_done():
-        '''
+                return prev() or model.schedule.time >= until
-        try:
+
-            return self.run_trial(*args, **kwargs)
+        if self.max_steps and self.max_steps > 0 and hasattr(model.schedule, 'steps'):
-        except Exception as ex:
+            prev_steps = is_done
-            if ex.__cause__ is not None:
+
-                ex = ex.__cause__
+            def is_done():
-            ex.message = ''.join(traceback.format_exception(type(ex), ex, ex.__traceback__)[:])
+                return prev_steps() or model.schedule.steps >= self.max_steps
-            return ex
+
+        newline = '\n'
+        logger.info(dedent(f'''
+Model stats:
+  Agents (total: { model.schedule.get_agent_count() }):
+      - { (newline + '      - ').join(str(a) for a in model.schedule.agents) }'''
+f'''
+
+  Topologies (size):
+      -  { dict( (k, len(v)) for (k, v) in model.topologies.items())  }
+''' if getattr(model, "topologies", None) else ''
+))
+
+        while not is_done():
+            utils.logger.debug(f'Simulation time {model.schedule.time}/{until}. Next: {getattr(model.schedule, "next_time", model.schedule.time + self.interval)}')
+            model.step()
+        return model
 
     def to_dict(self):
-        return self.config.dict()
+        d = asdict(self)
+        if not isinstance(d['model_class'], str):
+            d['model_class'] = serialization.name(d['model_class'])
+        d['model_params'] = serialization.serialize_dict(d['model_params'])
+        d['dir_path'] = str(d['dir_path'])
+        d['version'] = '2'
+        return d
 
     def to_yaml(self):
-        return yaml.dump(self.config.dict())
+        return yaml.dump(self.to_dict())
 
 
-def all_from_config(config):
+def iter_from_config(*cfgs):
+    for config in cfgs:
         configs = list(serialization.load_config(config))
         for config, path in configs:
-        if config.get('version', '1') == '1':
+            d = dict(config)
-            config = convert_old(config)
+            if 'dir_path' not in d:
-        if not isinstance(config, Config):
+                d['dir_path'] = os.path.dirname(path)
-            config = Config(**config)
+            yield Simulation.from_dict(d)
-        if not config.general.dir_path:
-            config.general.dir_path = os.path.dirname(path)
-        sim = Simulation(config=config)
-        yield sim
 
 
 def from_config(conf_or_path):
-    lst = list(all_from_config(conf_or_path))
+    lst = list(iter_from_config(conf_or_path))
     if len(lst) > 1:
         raise AttributeError('Provide only one configuration')
     return lst[0]
 
-def from_old_config(conf_or_path):
-    config = list(serialization.load_config(conf_or_path))
-    if len(config) > 1:
-        raise AttributeError('Provide only one configuration')
-    config = convert_old(config[0][0])
-    return Simulation(config)
-
 
 def run_from_config(*configs, **kwargs):
-    for sim in all_from_config(configs):
+    for sim in iter_from_config(*configs):
-        name = config.general.id
+        logger.info(f"Using config(s): {sim.name}")
-        logger.info("Using config(s): {name}".format(name=name))
         sim.run_simulation(**kwargs)

soil/time.py

@@ -1,6 +1,6 @@
 from mesa.time import BaseScheduler
 from queue import Empty
-from heapq import heappush, heappop
+from heapq import heappush, heappop, heapify
 import math
 from .utils import logger
 from mesa import Agent as MesaAgent
@@ -17,6 +17,7 @@ class When:
     def abs(self, time):
         return self._time
 
+
 NEVER = When(INFINITY)
 
 
@@ -37,13 +38,22 @@ class TimedActivation(BaseScheduler):
     """
 
     def __init__(self, *args, **kwargs):
-        super().__init__(self)
+        super().__init__(*args, **kwargs)
+        self._next = {}
         self._queue = []
         self.next_time = 0
+        self.logger = logger.getChild(f'time_{ self.model }')
 
-    def add(self, agent: MesaAgent):
+    def add(self, agent: MesaAgent, when=None):
-        if agent.unique_id not in self._agents:
+        if when is None:
-            heappush(self._queue, (self.time, agent.unique_id))
+            when = self.time
+        if agent.unique_id in self._agents:
+            self._queue.remove((self._next[agent.unique_id], agent.unique_id))
+            del self._agents[agent.unique_id]
+            heapify(self._queue)
+        
+        heappush(self._queue, (when, agent.unique_id))
+        self._next[agent.unique_id] = when
         super().add(agent)
 
     def step(self) -> None:
@@ -52,7 +62,8 @@ class TimedActivation(BaseScheduler):
         an agent will signal when it wants to be scheduled next.
         """
 
-        if self.next_time == INFINITY:
+        self.logger.debug(f'Simulation step {self.next_time}')
+        if not self.model.running:
             return
 
         self.time = self.next_time
@@ -60,13 +71,20 @@ class TimedActivation(BaseScheduler):
 
         while self._queue and self._queue[0][0] == self.time:
             (when, agent_id) = heappop(self._queue)
-            logger.debug(f'Stepping agent {agent_id}')
+            self.logger.debug(f'Stepping agent {agent_id}')
+
+            agent = self._agents[agent_id]
+            returned = agent.step()
+
+            if not agent.alive:
+                self.remove(agent)
+                continue
 
-            returned = self._agents[agent_id].step()
             when = (returned or Delta(1)).abs(self.time)
             if when < self.time:
                 raise Exception("Cannot schedule an agent for a time in the past ({} < {})".format(when, self.time))
 
+            self._next[agent_id] = when
             heappush(self._queue, (when, agent_id))
 
         self.steps += 1
@@ -74,7 +92,8 @@ class TimedActivation(BaseScheduler):
         if not self._queue:
             self.time = INFINITY
             self.next_time = INFINITY
-            return
+            self.model.running = False
+            return self.time
 
         self.next_time = self._queue[0][0]
-
+        self.logger.debug(f'Next step: {self.next_time}')

soil/utils.py

@@ -1,14 +1,29 @@
 import logging
 from time import time as current_time, strftime, gmtime, localtime
 import os
+import traceback
 
+from functools import partial
 from shutil import copyfile
+from multiprocessing import Pool
 
 from contextlib import contextmanager
 
 logger = logging.getLogger('soil')
-# logging.basicConfig()
+logger.setLevel(logging.INFO)
-# logger.setLevel(logging.INFO)
+
+timeformat = "%H:%M:%S"
+
+if os.environ.get('SOIL_VERBOSE', ''):
+    logformat = "[%(levelname)-5.5s][%(asctime)s][%(name)s]:  %(message)s"
+else:
+    logformat = "[%(levelname)-5.5s][%(asctime)s] %(message)s"
+
+logFormatter = logging.Formatter(logformat, timeformat)
+
+consoleHandler = logging.StreamHandler()
+consoleHandler.setFormatter(logFormatter)
+logger.addHandler(consoleHandler)
 
 
 @contextmanager
@@ -26,8 +41,6 @@ def timer(name='task', pre="", function=logger.info, to_object=None):
         to_object.end = end
 
 
-
-
 def safe_open(path, mode='r', backup=True, **kwargs):
     outdir = os.path.dirname(path)
     if outdir and not os.path.exists(outdir):
@@ -89,3 +102,32 @@ def unflatten_dict(d):
             target = target[token]
         target[tokens[-1]] = v
     return out
+
+
+def run_and_return_exceptions(func, *args, **kwargs):
+    '''
+    A wrapper for run_trial that catches exceptions and returns them.
+    It is meant for async simulations.
+    '''
+    try:
+        return func(*args, **kwargs)
+    except Exception as ex:
+        if ex.__cause__ is not None:
+            ex = ex.__cause__
+        ex.message = ''.join(traceback.format_exception(type(ex), ex, ex.__traceback__)[:])
+        return ex
+
+
+def run_parallel(func, iterable, parallel=False, **kwargs):
+    if parallel and not os.environ.get('SOIL_DEBUG', None):
+        p = Pool()
+        wrapped_func = partial(run_and_return_exceptions,
+                               func, **kwargs)
+        for i in p.imap_unordered(wrapped_func, iterable):
+            if isinstance(i, Exception):
+                logger.error('Trial failed:\n\t%s', i.message)
+                continue
+            yield i
+    else:
+        for i in iterable:
+            yield func(i, **kwargs)

soil/web/config.yml

@@ -6,11 +6,11 @@ network_params:
   n: 100
   m: 2
 network_agents:
-  - agent_type: ControlModelM2
+  - agent_class: ControlModelM2
     weight: 0.1
     state:
       id: 1
-  - agent_type: ControlModelM2
+  - agent_class: ControlModelM2
     weight: 0.9
     state:
       id: 0

tests/complete_converted.yml

@@ -1,49 +1,50 @@
 ---
 version: '2'
-general:
+name: simple
-  id: simple
+group: tests
-  group: tests
+dir_path: "/tmp/"
-  dir_path: "/tmp/"
+num_trials: 3
-  num_trials: 3
+max_time: 100
-  max_time: 100
+interval: 1
-  interval: 1
+seed: "CompleteSeed!"
-  seed: "CompleteSeed!"
+model_class: Environment
-topologies:
+model_params:
+  topologies:
     default:
       params:
         generator: complete_graph
-      n: 10
+        n: 4
-agents:
+  agents:
-  default:
     agent_class: CounterModel
     state:
+      group: network
       times: 1
-  network:
     topology: 'default'
     distribution:
       - agent_class: CounterModel
-        weight: 0.4
+        weight: 0.25
         state:
           state_id: 0
+          times: 1
       - agent_class: AggregatedCounter
-        weight: 0.6
+        weight: 0.5
-    override:
-      - filter:
-          node_id: 0
         state:
-          name: 'The first node'
+          times: 2
+    override:
       - filter:
           node_id: 1
         state:
-          name: 'The second node'
+          name: 'Node 1'
-
+      - filter:
-  environment:
+          node_id: 2
-    fixed:
-      - name: 'Environment Agent 1'
-        agent_class: CounterModel
         state:
+          name: 'Node 2'
+    fixed:
+      - agent_class: BaseAgent
+        hidden: true
+        topology: null
+        state:
+          name: 'Environment Agent 1'
           times: 10
-environment:
+          group: environment
-  environment_class: Environment
-  params:
   am_i_complete: true

tests/old_complete.yml

@@ -8,25 +8,30 @@ interval: 1
 seed: "CompleteSeed!"
 network_params:
   generator: complete_graph
-  n: 10
+  n: 4
 network_agents:
-  - agent_type: CounterModel
+  - agent_class: CounterModel
-    weight: 0.4
+    weight: 0.25
     state:
       state_id: 0
-  - agent_type: AggregatedCounter
+      times: 1
-    weight: 0.6
+  - agent_class: AggregatedCounter
+    weight: 0.5
+    state:
+      times: 2
 environment_agents:
   - agent_id: 'Environment Agent 1'
-    agent_type: CounterModel
+    agent_class: BaseAgent
     state:
       times: 10
 environment_class: Environment
 environment_params:
   am_i_complete: true
-agent_type: CounterModel
+agent_class: CounterModel
 default_state:
   times: 1
 states:
-  - name: 'The first node'
+  1:
-  - name: 'The second node'
+    name: 'Node 1'
+  2:
+    name: 'Node 2'

tests/test_agents.py

@@ -8,7 +8,7 @@ class Dead(agents.FSM):
     @agents.default_state
     @agents.state
     def only(self):
-        self.die()
+        return self.die()
 
 class TestMain(TestCase):
     def test_die_raises_exception(self):
@@ -19,4 +19,6 @@ class TestMain(TestCase):
 
     def test_die_returns_infinity(self):
         d = Dead(unique_id=0, model=environment.Environment())
-        assert d.step().abs(0) == stime.INFINITY
+        ret = d.step().abs(0)
+        print(ret, 'next')
+        assert ret == stime.INFINITY

tests/test_analysis.py

@@ -1,91 +0,0 @@
-from unittest import TestCase
-
-import os
-import pandas as pd
-import yaml
-from functools import partial
-
-from os.path import join
-from soil import simulation, analysis, agents
-
-
-ROOT = os.path.abspath(os.path.dirname(__file__))
-
-
-class Ping(agents.FSM):
-
-    defaults = {
-        'count': 0,
-    }
-
-    @agents.default_state
-    @agents.state
-    def even(self):
-        self.debug(f'Even {self["count"]}')
-        self['count'] += 1
-        return self.odd
-
-    @agents.state
-    def odd(self):
-        self.debug(f'Odd {self["count"]}')
-        self['count'] += 1
-        return self.even
-
-
-class TestAnalysis(TestCase):
-
-    # Code to generate a simple sqlite history
-    def setUp(self):
-        """
-        The initial states should be applied to the agent and the
-        agent should be able to update its state."""
-        config = {
-            'name': 'analysis',
-            'seed': 'seed',
-            'network_params': {
-                'generator': 'complete_graph',
-                'n': 2
-            },
-            'agent_type': Ping,
-            'states': [{'interval': 1}, {'interval': 2}],
-            'max_time': 30,
-            'num_trials': 1,
-            'history': True,
-            'environment_params': {
-            }
-        }
-        s = simulation.from_config(config)
-        self.env = s.run_simulation(dry_run=True)[0]
-
-    def test_saved(self):
-        env = self.env
-        assert env.get_agent(0)['count', 0] == 1
-        assert env.get_agent(0)['count', 29] == 30
-        assert env.get_agent(1)['count', 0] == 1
-        assert env.get_agent(1)['count', 29] == 15
-        assert env['env', 29, None]['SEED'] == env['env', 29, 'SEED']
-
-    def test_count(self):
-        env = self.env
-        df = analysis.read_sql(env._history.db_path)
-        res = analysis.get_count(df, 'SEED', 'state_id')
-        assert res['SEED'][self.env['SEED']].iloc[0] == 1
-        assert res['SEED'][self.env['SEED']].iloc[-1] == 1
-        assert res['state_id']['odd'].iloc[0] == 2
-        assert res['state_id']['even'].iloc[0] == 0
-        assert res['state_id']['odd'].iloc[-1] == 1
-        assert res['state_id']['even'].iloc[-1] == 1
-
-    def test_value(self):
-        env = self.env
-        df = analysis.read_sql(env._history.db_path)
-        res_sum = analysis.get_value(df, 'count')
-
-        assert res_sum['count'].iloc[0] == 2
-
-        import numpy as np
-        res_mean = analysis.get_value(df, 'count', aggfunc=np.mean)
-        assert res_mean['count'].iloc[15] == (16+8)/2
-
-        res_total = analysis.get_majority(df)
-        res_total['SEED'].iloc[0] == self.env['SEED']

tests/test_config.py

@@ -1,8 +1,10 @@
 from unittest import TestCase
 import os
+import yaml
+import copy
 from os.path import join
 
-from soil import simulation, serialization, config, network, agents
+from soil import simulation, serialization, config, network, agents, utils
 
 ROOT = os.path.abspath(os.path.dirname(__file__))
 EXAMPLES = join(ROOT, '..', 'examples')
@@ -10,15 +12,7 @@ EXAMPLES = join(ROOT, '..', 'examples')
 FORCE_TESTS = os.environ.get('FORCE_TESTS', '')
 
 
-class TestConfig(TestCase):
+def isequal(a, b):
-
-    def test_conversion(self):
-        expected = serialization.load_file(join(ROOT, "complete_converted.yml"))[0]
-        old = serialization.load_file(join(ROOT, "old_complete.yml"))[0]
-        converted_defaults = config.convert_old(old, strict=False)
-        converted = converted_defaults.dict(skip_defaults=True)
-
-        def isequal(a, b):
     if isinstance(a, dict):
         for (k, v) in a.items():
             if v:
@@ -28,8 +22,32 @@ class TestConfig(TestCase):
         return
     assert a == b
 
+
+class TestConfig(TestCase):
+
+    def test_conversion(self):
+        expected = serialization.load_file(join(ROOT, "complete_converted.yml"))[0]
+        old = serialization.load_file(join(ROOT, "old_complete.yml"))[0]
+        converted_defaults = config.convert_old(old, strict=False)
+        converted = converted_defaults.dict(exclude_unset=True)
+
         isequal(converted, expected)
 
+    def test_configuration_changes(self):
+        """
+        The configuration should not change after running
+         the simulation.
+        """
+        config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
+        s = simulation.from_config(config)
+        init_config = copy.copy(s.to_dict())
+
+        s.run_simulation(dry_run=True)
+        nconfig = s.to_dict()
+        # del nconfig['to
+        isequal(init_config, nconfig)
+
+
     def test_topology_config(self):
         netconfig = config.NetConfig(**{
             'path': join(ROOT, 'test.gexf')
@@ -43,12 +61,12 @@ class TestConfig(TestCase):
         Simple configuration that tests that the graph is loaded, and that
         network agents are initialized properly.
         """
-        config = {
+        cfg = {
             'name': 'CounterAgent',
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
-            'agent_type': 'CounterModel',
+            'agent_class': 'CounterModel',
             # 'states': [{'times': 10}, {'times': 20}],
             'max_time': 2,
             'dry_run': True,
@@ -56,13 +74,14 @@ class TestConfig(TestCase):
             'environment_params': {
             }
         }
-        s = simulation.from_old_config(config)
+        conf = config.convert_old(cfg)
+        s = simulation.from_config(conf)
+
         env = s.get_env()
         assert len(env.topologies['default'].nodes) == 2
         assert len(env.topologies['default'].edges) == 1
         assert len(env.agents) == 2
-        assert env.agents[0].topology == env.topologies['default']
+        assert env.agents[0].G == env.topologies['default']
-
 
     def test_agents_from_config(self):
         '''We test that the known complete configuration produces
@@ -70,12 +89,26 @@ class TestConfig(TestCase):
         cfg = serialization.load_file(join(ROOT, "complete_converted.yml"))[0]
         s = simulation.from_config(cfg)
         env = s.get_env()
-        assert len(env.topologies['default'].nodes) == 10
+        assert len(env.topologies['default'].nodes) == 4
-        assert len(env.agents(group='network')) == 10
+        assert len(env.agents(group='network')) == 4
         assert len(env.agents(group='environment')) == 1
 
-        assert sum(1 for a in env.agents(group='network', agent_type=agents.CounterModel)) == 4
+    def test_yaml(self):
-        assert sum(1 for a in env.agents(group='network', agent_type=agents.AggregatedCounter)) == 6
+        """
+        The YAML version of a newly created configuration should be equivalent
+        to the configuration file used.
+        Values not present in the original config file should have reasonable
+        defaults.
+        """
+        with utils.timer('loading'):
+            config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
+            s = simulation.from_config(config)
+        with utils.timer('serializing'):
+            serial = s.to_yaml()
+        with utils.timer('recovering'):
+            recovered = yaml.load(serial, Loader=yaml.SafeLoader)
+        for (k, v) in config.items():
+            assert recovered[k] == v
 
 def make_example_test(path, cfg):
     def wrapped(self):

tests/test_examples.py

@@ -2,7 +2,7 @@ from unittest import TestCase
 import os
 from os.path import join
 
-from soil import serialization, simulation
+from soil import serialization, simulation, config
 
 ROOT = os.path.abspath(os.path.dirname(__file__))
 EXAMPLES = join(ROOT, '..', 'examples')
@@ -14,36 +14,37 @@ class TestExamples(TestCase):
     pass
 
 
-def make_example_test(path, config):
+def make_example_test(path, cfg):
     def wrapped(self):
         root = os.getcwd()
-        for s in simulation.all_from_config(path):
+        for s in simulation.iter_from_config(cfg):
-            iterations = s.config.general.max_time * s.config.general.num_trials
+            iterations = s.max_steps * s.num_trials
-            if iterations > 1000:
+            if iterations < 0 or iterations > 1000:
-                s.config.general.max_time = 100
+                s.max_steps = 100
-                s.config.general.num_trials = 1
+                s.num_trials = 1
-            if config.get('skip_test', False) and not FORCE_TESTS:
+            assert isinstance(cfg, config.Config)
+            if getattr(cfg, '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']
+                    n = cfg.model_params['network_params']['n']
                     assert len(list(env.network_agents)) == n
-                    assert env.now > 0  # It has run
-                    assert env.now <= config['max_time']  # But not further than allowed
                 except KeyError:
                     pass
+                assert env.schedule.steps > 0  # It has run
+                assert env.schedule.steps <= s.max_steps  # But not further than allowed
     return wrapped
 
 
 def add_example_tests():
-    for config, path in serialization.load_files(
+    for cfg, path in serialization.load_files(
             join(EXAMPLES, '*', '*.yml'),
             join(EXAMPLES, '*.yml'),
     ):
-        p = make_example_test(path=path, config=config)
+        p = make_example_test(path=path, cfg=config.Config.from_raw(cfg))
         fname = os.path.basename(path)
         p.__name__ = 'test_example_file_%s' % fname
         p.__doc__ = '%s should be a valid configuration' % fname

tests/test_exporters.py

@@ -6,6 +6,8 @@ import shutil
 from unittest import TestCase
 from soil import exporters
 from soil import simulation
+from soil import agents
+
 
 class Dummy(exporters.Exporter):
     started = False
@@ -33,28 +35,36 @@ class Dummy(exporters.Exporter):
 
 class Exporters(TestCase):
     def test_basic(self):
+        # We need to add at least one agent to make sure the scheduler
+        # ticks every step
+        num_trials = 5
+        max_time = 2
         config = {
             'name': 'exporter_sim',
-            'network_params': {},
+            'model_params': {
-            'agent_type': 'CounterModel',
+                'agents': [{
-            'max_time': 2,
+                    'agent_class': agents.BaseAgent
-            'num_trials': 5,
+                }]
-            'environment_params': {}
+            },
+            'max_time': max_time,
+            'num_trials': num_trials,
         }
         s = simulation.from_config(config)
+
         for env in s.run_simulation(exporters=[Dummy], dry_run=True):
-            assert env.now <= 2
+            assert len(env.agents) == 1
+            assert env.now == max_time
 
         assert Dummy.started
         assert Dummy.ended
         assert Dummy.called_start == 1
         assert Dummy.called_end == 1
-        assert Dummy.called_trial == 5
+        assert Dummy.called_trial == num_trials
-        assert Dummy.trials == 5
+        assert Dummy.trials == num_trials
-        assert Dummy.total_time == 2*5
+        assert Dummy.total_time == max_time * num_trials
 
     def test_writing(self):
-        '''Try to write CSV, GEXF, sqlite and YAML (without dry_run)'''
+        '''Try to write CSV, sqlite and YAML (without dry_run)'''
         n_trials = 5
         config = {
             'name': 'exporter_sim',
@@ -62,7 +72,7 @@ class Exporters(TestCase):
                 'generator': 'complete_graph',
                 'n': 4
             },
-            'agent_type': 'CounterModel',
+            'agent_class': 'CounterModel',
             'max_time': 2,
             'num_trials': n_trials,
             'dry_run': False,
@@ -74,7 +84,6 @@ class Exporters(TestCase):
         envs = s.run_simulation(exporters=[
                                     exporters.default,
                                     exporters.csv,
-                                    exporters.gexf,
                                 ],
                                 dry_run=False,
                                 outdir=tmpdir,
@@ -88,11 +97,7 @@ class Exporters(TestCase):
 
         try:
             for e in envs:
-                with open(os.path.join(simdir, '{}.gexf'.format(e.name))) as f:
+                with open(os.path.join(simdir, '{}.env.csv'.format(e.id))) 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:

tests/test_history.py

@@ -1,128 +0,0 @@
-from unittest import TestCase
-
-import os
-import io
-import yaml
-import copy
-import pickle
-import networkx as nx
-from functools import partial
-
-from os.path import join
-from soil import (simulation, Environment, agents, serialization,
-                  utils)
-from soil.time import Delta
-from tsih import NoHistory, History
-
-
-ROOT = os.path.abspath(os.path.dirname(__file__))
-EXAMPLES = join(ROOT, '..', 'examples')
-
-
-class CustomAgent(agents.FSM):
-    @agents.default_state
-    @agents.state
-    def normal(self):
-        self.neighbors = self.count_agents(state_id='normal',
-                                           limit_neighbors=True)
-    @agents.state
-    def unreachable(self):
-        return
-
-class TestHistory(TestCase):
-
-    def test_counter_agent_history(self):
-        """
-        The evolution of the state should be recorded in the logging agent
-        """
-        config = {
-            'name': 'CounterAgent',
-            'network_params': {
-                'path': join(ROOT, 'test.gexf')
-            },
-            'network_agents': [{
-                'agent_type': 'AggregatedCounter',
-                'weight': 1,
-                'state': {'state_id': 0}
-
-            }],
-            'max_time': 10,
-            'environment_params': {
-            }
-        }
-        s = simulation.from_config(config)
-        env = s.run_simulation(dry_run=True)[0]
-        for agent in env.network_agents:
-            last = 0
-            assert len(agent[None, None]) == 11
-            for step, total in sorted(agent['total', None]):
-                assert total == last + 2
-                last = total
-
-    def test_row_conversion(self):
-        env = Environment(history=True)
-        env['test'] = 'test_value'
-
-        res = list(env.history_to_tuples())
-        assert len(res) == len(env.environment_params)
-
-        env.schedule.time = 1
-        env['test'] = 'second_value'
-        res = list(env.history_to_tuples())
-
-        assert env['env', 0, 'test' ] == 'test_value'
-        assert env['env', 1, 'test' ] == 'second_value'
-
-    def test_nohistory(self):
-        '''
-        Make sure that no history(/sqlite) is used by default
-        '''
-        env = Environment(topology=nx.Graph(), network_agents=[])
-        assert isinstance(env._history, NoHistory)
-
-    def test_save_graph_history(self):
-        '''
-        The history_to_graph method should return a valid networkx graph.
-
-        The state of the agent should be encoded as intervals in the nx graph.
-        '''
-        G = nx.cycle_graph(5)
-        distribution = agents.calculate_distribution(None, agents.BaseAgent)
-        env = Environment(topology=G, network_agents=distribution, history=True)
-        env[0, 0, 'testvalue'] = 'start'
-        env[0, 10, 'testvalue'] = 'finish'
-        nG = env.history_to_graph()
-        values = nG.nodes[0]['attr_testvalue']
-        assert ('start', 0, 10) in values
-        assert ('finish', 10, None) in values
-
-    def test_save_graph_nohistory(self):
-        '''
-        The history_to_graph method should return a valid networkx graph.
-
-        When NoHistory is used, only the last known value is known
-        '''
-        G = nx.cycle_graph(5)
-        distribution = agents.calculate_distribution(None, agents.BaseAgent)
-        env = Environment(topology=G, network_agents=distribution, history=False)
-        env.get_agent(0)['testvalue'] = 'start'
-        env.schedule.time = 10
-        env.get_agent(0)['testvalue'] = 'finish'
-        nG = env.history_to_graph()
-        values = nG.nodes[0]['attr_testvalue']
-        assert ('start', 0, None) not in values
-        assert ('finish', 10, None) in values
-
-    def test_pickle_agent_environment(self):
-        env = Environment(name='Test', history=True)
-        a = agents.BaseAgent(model=env, unique_id=25)
-
-        a['key'] = 'test'
-
-        pickled = pickle.dumps(a)
-        recovered = pickle.loads(pickled)
-
-        assert recovered.env.name == 'Test'
-        assert list(recovered.env._history.to_tuples())
-        assert recovered['key', 0] == 'test'
-        assert recovered['key'] == 'test'

tests/test_main.py

@@ -1,9 +1,6 @@
 from unittest import TestCase
 
 import os
-import io
-import yaml
-import copy
 import pickle
 import networkx as nx
 from functools import partial
@@ -27,58 +24,20 @@ class CustomAgent(agents.FSM, agents.NetworkAgent):
     def unreachable(self):
         return
 
+
 class TestMain(TestCase):
 
-    def test_load_graph(self):
-        """
-        Load a graph from file if the extension is known.
-        Raise an exception otherwise.
-        """
-        config = {
-            'network_params': {
-                'path': join(ROOT, 'test.gexf')
-            }
-        }
-        G = network.from_config(config['network_params'])
-        assert G
-        assert len(G) == 2
-        with self.assertRaises(AttributeError):
-            config = {
-                'network_params': {
-                    'path': join(ROOT, 'unknown.extension')
-                }
-            }
-            G = network.from_config(config['network_params'])
-            print(G)
-
-    def test_generate_barabasi(self):
-        """
-        If no path is given, a generator and network parameters
-        should be used to generate a network
-        """
-        cfg = {
-            'params': {
-                'generator': 'barabasi_albert_graph'
-            }
-        }
-        with self.assertRaises(Exception):
-            G = network.from_config(cfg)
-        cfg['params']['n'] = 100
-        cfg['params']['m'] = 10
-        G = network.from_config(cfg)
-        assert len(G) == 100
-
     def test_empty_simulation(self):
         """A simulation with a base behaviour should do nothing"""
         config = {
+            'model_params': {
               'network_params': {
                   'path': join(ROOT, 'test.gexf')
               },
-            'agent_type': 'BaseAgent',
+              'agent_class': 'BaseAgent',
-            'environment_params': {
             }
         }
-        s = simulation.from_old_config(config)
+        s = simulation.from_config(config)
         s.run_simulation(dry_run=True)
 
 
@@ -88,21 +47,21 @@ class TestMain(TestCase):
         agent should be able to update its state."""
         config = {
             'name': 'CounterAgent',
+            'num_trials': 1,
+            'max_time': 2,
+            'model_params': {
                 'network_params': {
                     'generator': nx.complete_graph,
                     'n': 2,
                 },
-            'agent_type': 'CounterModel',
+                'agent_class': 'CounterModel',
                 'states': {
                     0: {'times': 10},
                     1: {'times': 20},
                 },
-            'max_time': 2,
-            'num_trials': 1,
-            'environment_params': {
             }
         }
-        s = simulation.from_old_config(config)
+        s = simulation.from_config(config)
 
     def test_counter_agent(self):
         """
@@ -110,22 +69,18 @@ class TestMain(TestCase):
         agent should be able to update its state."""
         config = {
             'version': '2',
-            'general': {
             'name': 'CounterAgent',
-                'max_time': 2,
             'dry_run': True,
             'num_trials': 1,
-            },
+            'max_time': 2,
+            'model_params': {
                 'topologies': {
                     'default': {
                         'path': join(ROOT, 'test.gexf')
                     }
                 },
                 'agents': {
-                'default': {
                     'agent_class': 'CounterModel',
-                },
-                'counters': {
                     'topology': 'default',
                     'fixed': [{'state': {'times': 10}}, {'state': {'times': 20}}],
                 }
@@ -134,40 +89,44 @@ class TestMain(TestCase):
         s = simulation.from_config(config)
         env = s.get_env()
         assert isinstance(env.agents[0], agents.CounterModel)
-        assert env.agents[0].topology == env.topologies['default']
+        assert env.agents[0].G == env.topologies['default']
         assert env.agents[0]['times'] == 10
         assert env.agents[0]['times'] == 10
         env.step()
         assert env.agents[0]['times'] == 11
         assert env.agents[1]['times'] == 21
 
-    def test_custom_agent(self):
+    def test_init_and_count_agents(self):
-        """Allow for search of neighbors with a certain state_id"""
+        """Agents should be properly initialized and counting should filter them properly"""
+        #TODO: separate this test into two or more test cases
         config = {
-            'network_params': {
-                'path': join(ROOT, 'test.gexf')
-            },
-            'network_agents': [{
-                'agent_type': CustomAgent,
-                'weight': 1
-
-            }],
             'max_time': 10,
-            'environment_params': {
+            'model_params': {
+                'agents': [{'agent_class': CustomAgent, 'weight': 1, 'topology': 'default'},
+                           {'agent_class': CustomAgent, 'weight': 3, 'topology': 'default'},
+                ],
+                'topologies': {
+                    'default': {
+                        'path': join(ROOT, 'test.gexf')
                     }
+                },
+            },
         }
-        s = simulation.from_old_config(config)
+        s = simulation.from_config(config)
         env = s.run_simulation(dry_run=True)[0]
-        assert env.agents[1].count_agents(state_id='normal') == 2
+        assert env.agents[0].weight == 1
-        assert env.agents[1].count_agents(state_id='normal', limit_neighbors=True) == 1
+        assert env.count_agents() == 2
-        assert env.agents[0].neighbors == 1
+        assert env.count_agents(weight=1) == 1
+        assert env.count_agents(weight=3) == 1
+        assert env.count_agents(agent_class=CustomAgent) == 2
+
 
     def test_torvalds_example(self):
         """A complete example from a documentation should work."""
         config = serialization.load_file(join(EXAMPLES, 'torvalds.yml'))[0]
-        config['network_params']['path'] = join(EXAMPLES,
+        config['model_params']['network_params']['path'] = join(EXAMPLES,
-                                                config['network_params']['path'])
+                                                config['model_params']['network_params']['path'])
-        s = simulation.from_old_config(config)
+        s = simulation.from_config(config)
         env = s.run_simulation(dry_run=True)[0]
         for a in env.network_agents:
             skill_level = a.state['skill_level']
@@ -184,47 +143,6 @@ class TestMain(TestCase):
                 assert a.state['total'] == 3
                 assert a.state['neighbors'] == 1
 
-    def test_yaml(self):
-        """
-        The YAML version of a newly created configuration should be equivalent
-        to the configuration file used.
-        Values not present in the original config file should have reasonable
-        defaults.
-        """
-        with utils.timer('loading'):
-            config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
-            s = simulation.from_old_config(config)
-        with utils.timer('serializing'):
-            serial = s.to_yaml()
-        with utils.timer('recovering'):
-            recovered = yaml.load(serial, Loader=yaml.SafeLoader)
-        for (k, v) in config.items():
-            assert recovered[k] == v
-
-    def test_configuration_changes(self):
-        """
-        The configuration should not change after running
-         the simulation.
-        """
-        config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
-        s = simulation.from_old_config(config)
-        init_config = copy.copy(s.config)
-
-        s.run_simulation(dry_run=True)
-        nconfig = s.config
-        # del nconfig['to
-        assert init_config == nconfig
-
-    def test_save_geometric(self):
-        """
-        There is a bug in networkx that prevents it from creating a GEXF file 
-        from geometric models. We should work around it.
-        """
-        G = nx.random_geometric_graph(20, 0.1)
-        env = Environment(topology=G)
-        f = io.BytesIO()
-        env.dump_gexf(f)
-
     def test_serialize_class(self):
         ser, name = serialization.serialize(agents.BaseAgent, known_modules=[])
         assert name == 'soil.agents.BaseAgent'
@@ -247,7 +165,7 @@ class TestMain(TestCase):
             des = serialization.deserialize(name, ser)
             assert i == des
 
-    def test_serialize_agent_type(self):
+    def test_serialize_agent_class(self):
         '''A class from soil.agents should be serialized without the module part'''
         ser = agents.serialize_type(CustomAgent)
         assert ser == 'test_main.CustomAgent'
@@ -258,53 +176,35 @@ class TestMain(TestCase):
     def test_deserialize_agent_distribution(self):
         agent_distro = [
             {
-                'agent_type': 'CounterModel',
+                'agent_class': 'CounterModel',
                 'weight': 1
             },
             {
-                'agent_type': 'test_main.CustomAgent',
+                'agent_class': 'test_main.CustomAgent',
                 'weight': 2
             },
         ]
         converted = agents.deserialize_definition(agent_distro)
-        assert converted[0]['agent_type'] == agents.CounterModel
+        assert converted[0]['agent_class'] == agents.CounterModel
-        assert converted[1]['agent_type'] == CustomAgent
+        assert converted[1]['agent_class'] == CustomAgent
         pickle.dumps(converted)
 
     def test_serialize_agent_distribution(self):
         agent_distro = [
             {
-                'agent_type': agents.CounterModel,
+                'agent_class': agents.CounterModel,
                 'weight': 1
             },
             {
-                'agent_type': CustomAgent,
+                'agent_class': CustomAgent,
                 'weight': 2
             },
         ]
         converted = agents.serialize_definition(agent_distro)
-        assert converted[0]['agent_type'] == 'CounterModel'
+        assert converted[0]['agent_class'] == 'CounterModel'
-        assert converted[1]['agent_type'] == 'test_main.CustomAgent'
+        assert converted[1]['agent_class'] == 'test_main.CustomAgent'
         pickle.dumps(converted)
 
-    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)
-        distro[0]['topology'] = 'default'
-        aconfig = config.AgentConfig(distribution=distro, topology='default')
-        env = Environment(name='Test', topologies={'default': G}, agents={'network': aconfig})
-        lst = list(env.network_agents)
-
-        a2 = env.find_one(node_id=2)
-        a3 = env.find_one(node_id=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'))
@@ -313,19 +213,23 @@ class TestMain(TestCase):
     def test_until(self):
         config = {
             'name': 'until_sim',
+            'model_params': {
                 'network_params': {},
-            'agent_type': 'CounterModel',
+                'agents': {
+                    'fixed': [{
+                        'agent_class': agents.BaseAgent,
+                    }]
+                },
+            },
             'max_time': 2,
             'num_trials': 50,
-            'environment_params': {}
         }
-        s = simulation.from_old_config(config)
+        s = simulation.from_config(config)
         runs = list(s.run_simulation(dry_run=True))
-        over = list(x.now for x in runs if x.now>2)
+        over = list(x.now for x in runs if x.now > 2)
         assert len(runs) == config['num_trials']
         assert len(over) == 0
 
-
     def test_fsm(self):
         '''Basic state change'''
         class ToggleAgent(agents.FSM):

tests/test_network.py

@@ -0,0 +1,133 @@
+from unittest import TestCase
+
+import io
+import os
+import networkx as nx
+
+from os.path import join
+
+from soil import config, network, environment, agents, simulation
+from test_main import CustomAgent
+
+ROOT = os.path.abspath(os.path.dirname(__file__))
+EXAMPLES = join(ROOT, '..', 'examples')
+
+
+class TestNetwork(TestCase):
+    def test_load_graph(self):
+        """
+        Load a graph from file if the extension is known.
+        Raise an exception otherwise.
+        """
+        config = {
+            'network_params': {
+                'path': join(ROOT, 'test.gexf')
+            }
+        }
+        G = network.from_config(config['network_params'])
+        assert G
+        assert len(G) == 2
+        with self.assertRaises(AttributeError):
+            config = {
+                'network_params': {
+                    'path': join(ROOT, 'unknown.extension')
+                }
+            }
+            G = network.from_config(config['network_params'])
+            print(G)
+
+    def test_generate_barabasi(self):
+        """
+        If no path is given, a generator and network parameters
+        should be used to generate a network
+        """
+        cfg = {
+            'params': {
+                'generator': 'barabasi_albert_graph'
+            }
+        }
+        with self.assertRaises(Exception):
+            G = network.from_config(cfg)
+        cfg['params']['n'] = 100
+        cfg['params']['m'] = 10
+        G = network.from_config(cfg)
+        assert len(G) == 100
+
+    def test_save_geometric(self):
+        """
+        There is a bug in networkx that prevents it from creating a GEXF file 
+        from geometric models. We should work around it.
+        """
+        G = nx.random_geometric_graph(20, 0.1)
+        env = environment.NetworkEnvironment(topology=G)
+        f = io.BytesIO()
+        assert env.topologies['default']
+        network.dump_gexf(env.topologies['default'], f)
+
+    def test_networkenvironment_creation(self):
+        """Networkenvironment should accept netconfig as parameters"""
+        model_params = {
+            'topologies': {
+                'default': {
+                    'path': join(ROOT, 'test.gexf')
+                }
+            },
+            'agents': {
+                'topology': 'default',
+                'distribution': [{
+                    'agent_class': CustomAgent,
+                }]
+            }
+        }
+        env = environment.Environment(**model_params)
+        assert env.topologies
+        env.step()
+        assert len(env.topologies['default']) == 2
+        assert len(env.agents) == 2
+        assert env.agents[1].count_agents(state_id='normal') == 2
+        assert env.agents[1].count_agents(state_id='normal', limit_neighbors=True) == 1
+        assert env.agents[0].neighbors == 1
+
+    def test_custom_agent_neighbors(self):
+        """Allow for search of neighbors with a certain state_id"""
+        config = {
+            'model_params': {
+                'topologies': {
+                    'default': {
+                        'path': join(ROOT, 'test.gexf')
+                    }
+                 },
+                'agents': {
+                    'topology': 'default',
+                    'distribution': [
+                        {
+                            'weight': 1,
+                            'agent_class': CustomAgent
+                        }
+                    ]
+                }
+            },
+            'max_time': 10,
+        }
+        s = simulation.from_config(config)
+        env = s.run_simulation(dry_run=True)[0]
+        assert env.agents[1].count_agents(state_id='normal') == 2
+        assert env.agents[1].count_agents(state_id='normal', limit_neighbors=True) == 1
+        assert env.agents[0].neighbors == 1
+
+    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_class=agents.NetworkAgent)
+        aconfig = config.AgentConfig(distribution=distro, topology='default')
+        env = environment.Environment(name='Test', topologies={'default': G}, agents=aconfig)
+        lst = list(env.network_agents)
+
+        a2 = env.find_one(node_id=2)
+        a3 = env.find_one(node_id=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_class=agents.NetworkAgent)) == 3