WIP: all tests pass

First iteration to achieve MESA compatibility.
As a side effect, we have removed `simpy`.

For a full list of changes, see `CHANGELOG.md`.

.gitlab-ci.yml

@@ -1,9 +1,10 @@
 stages:
   - test
-  - build
+  - publish
+  - check_published
 
-build:
-  stage: build
+docker:
+  stage: publish
   image:
     name: gcr.io/kaniko-project/executor:debug
     entrypoint: [""]
@@ -16,13 +17,37 @@ build:
   only:
     - tags
 
-
 test:
-  except:
-    - tags  # Avoid running tests for tags, because they are already run for the branch
   tags:
     - docker
   image: python:3.7
   stage: test
   script:
-    - python setup.py test
+    - pip install -r requirements.txt -r test-requirements.txt
+    - python setup.py test
+
+push_pypi:
+  only:
+    - tags
+  tags:
+    - docker
+  image: python:3.7
+  stage: publish
+  script:
+    - echo $CI_COMMIT_TAG > soil/VERSION
+    - pip install twine
+    - python setup.py sdist bdist_wheel
+    - TWINE_PASSWORD=$PYPI_PASSWORD TWINE_USERNAME=$PYPI_USERNAME python -m twine upload dist/*
+
+check_pypi:
+  only:
+    - tags
+  tags:
+    - docker
+  image: python:3.7
+  stage: check_published
+  script:
+    - pip install soil==$CI_COMMIT_TAG
+  # Allow PYPI to update its index before we try to install 
+  when: delayed
+  start_in: 2 minutes

CHANGELOG.md

@@ -3,10 +3,58 @@ 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 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)
+### Fixed
+* Bug with time.NEVER/time.INFINITY
+## [0.20.6]
+### Fixed
+* Agents now return `time.INFINITY` when dead, instead of 'inf'
+* `soil.__init__` does not re-export built-in time (change in `soil.simulation`. It used to create subtle import conflicts when importing soil.time.
+* Parallel simulations were broken because lambdas cannot be pickled properly, which is needed for multiprocessing.
+### Changed
+* Some internal simulation methods do not accept `*args` anymore, to avoid ambiguity and bugs.
+## [0.20.5]
+### Changed
+* Defaults are now set in the agent __init__, not in the environment. This decouples both classes a bit more, and it is more intuitive 
+## [0.20.4]
+### Added
+* Agents can now be given any kwargs, which will be used to set their state
+* Environments have a default logger `self.logger` and a log method, just like agents
+## [0.20.3]
+### Fixed
+* Default state values are now deepcopied again.
+* Seeds for environments only concatenate the trial id (i.e., a number), to provide repeatable results.
+* `Environment.run` now calls `Environment.step`, to allow for easy overloading of the environment step
+### Removed
+* Datacollectors are not being used for now.
+* `time.TimedActivation.step` does not use an `until` parameter anymore.
+### Changed
+* Simulations now run right up to `until` (open interval)
+* Time instants (`time.When`) don't need to be floats anymore. Now we can avoid precision issues with big numbers by using ints.
+* Rabbits simulation is more idiomatic (using subclasses)
+
+## [0.20.2]
+### Fixed
+* CI/CD testing issues
+## [0.20.1]
+### Fixed
+* Agents would run another step after dying.
+## [0.20.0]
 ### Added
 * Integration with MESA
-* `not_agent_ids` paramter to get sql in history
+* `not_agent_ids` parameter to get sql in history
 ### Changed
 * `soil.Environment` now also inherits from `mesa.Model`
 * `soil.Agent` now also inherits from `mesa.Agent`
@@ -16,11 +64,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 ### Removed
 * `simpy` dependency and compatibility. Each agent used to be a simpy generator, but that made debugging and error handling more complex. That has been replaced by a scheduler within the `soil.Environment` class, similar to how `mesa` does it.
 * `soil.history` is now a separate package named `tsih`. The keys namedtuple uses `dict_id` instead of `agent_id`.
-
 ### Added
 * An option to choose whether a database should be used for history 
-
-
 ## [0.15.2]
 ### Fixed
 * Pass the right known_modules and parameters to stats discovery in simulation

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 
 
 
@@ -31,24 +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``).
@@ -88,9 +88,18 @@ For example, the following configuration is equivalent to :code:`nx.complete_gra
 
 Environment
 ============
+
 The environment is the place where the shared state of the simulation is stored.
-For instance, the probability of disease outbreak.
-The configuration file may specify the initial value of the environment parameters:
+That means both global parameters, such as the probability of disease outbreak.
+But it also means other data, such as a map, or a network topology that connects multiple agents.
+As a result, it is also typical to add custom functions in an environment that help agents interact with each other and with the state of the simulation.
+
+Last but not least, an environment controls when and how its agents will be executed.
+By default, soil environments incorporate a ``soil.time.TimedActivation`` model for agent execution (more on this on the following section).
+
+Soil environments are very similar, and often interchangeable with, mesa models (``mesa.Model``).
+
+A configuration may specify the initial value of the environment parameters:
 
 .. code:: yaml
 
@@ -98,23 +107,33 @@ The configuration file may specify the initial value of the environment paramete
         daily_probability_of_earthquake: 0.001
         number_of_earthquakes: 0
 
-All agents have access to the environment parameters.
+All agents have access to the environment (and its parameters).
 
 In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
 For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.
 
-
 Agents
 ======
+
 Agents are a way of modelling behavior.
-Agents can be characterized with two variables: agent type (``agent_type``) and state.
-Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters.
+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.
+
+When and how agent steps are executed in a simulation depends entirely on the ``environment``.
+Most environments will internally use a scheduler (``mesa.time.BaseScheduler``), which controls the activation of agents.
+
+In soil, we generally used the ``soil.time.TimedActivation`` scheduler, which allows agents to specify when their next activation will happen, defaulting to a 
+
+When an agent's step is executed (generally, every ``interval`` seconds), the agent has access to its state and the environment.
 Through the environment, it can access the network topology and the state of other agents.
 
-There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
+There are two types of agents according to how they are added to the simulation: network agents and environment agent.
 
 Network Agents
 ##############
+
 Network agents are attached to a node in the topology.
 The configuration file allows you to specify how agents will be mapped to topology nodes.
 
@@ -123,17 +142,19 @@ 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, and to control the ratio of each type (using the ``weight`` property).
+It is also possible to add more than one type of agent to the simulation.
+
+To control the ratio of each type (using the ``weight`` property).
 For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
 
 .. code:: yaml
 
     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.:
@@ -144,10 +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:
@@ -158,9 +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
 
               
 
@@ -171,11 +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
@@ -185,7 +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
@@ -210,10 +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/requirements.txt

@@ -1 +1 @@
-ipython==7.23
+ipython>=7.31.1

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

@@ -1,27 +1,61 @@
 ---
+version: '2'
 name: simple
 group: tests
 dir_path: "/tmp/"
 num_trials: 3
-max_time: 100
+max_steps: 100
 interval: 1
 seed: "CompleteSeed!"
-network_params:
-  generator: complete_graph
-  n: 10
-network_agents:
-  - agent_type: CounterModel
-    weight: 1
-    state:
-      state_id: 0
-  - agent_type: AggregatedCounter
-    weight: 0.2
-environment_agents: []
-environment_class: Environment
-environment_params:
+model_class: Environment
+model_params:
   am_i_complete: true
-default_state:
-  incidents: 0
-states:
-  - name: 'The first node'
-  - name: 'The second node'
+  topologies:
+    default:
+      params:
+        generator: complete_graph
+        n: 10
+    another_graph:
+      params:
+        generator: complete_graph
+        n: 2
+  environment:
+  agents:
+    agent_class: CounterModel
+    topology: default
+    state:
+      times: 1
+      # In this group we are not specifying any topology
+    fixed:
+      - name: 'Environment Agent 1'
+        agent_class: BaseAgent
+        group: environment
+        topology: null
+        hidden: true
+        state:
+          times: 10
+      - agent_class: CounterModel
+        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
+        weight: 0.2
+    override:
+      - filter:
+          agent_class: AggregatedCounter
+        n: 2
+        state:
+          times: 5

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
     
@@ -24,4 +24,4 @@ def mygenerator(n=5, n_edges=5):
 
 
 
-    
+    

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},
-                                   {'ids': [1], 'agent_type': Odds}],
+    s = Simulation(network_agents=[{'ids': [0], 'agent_class': Fibonacci},
+                                   {'ids': [1], 'agent_class': Odds}],
                    network_params={"generator": "complete_graph", "n": 2},
                    max_time=100,
                    )

examples/mesa/mesa.yml

@@ -3,19 +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:
-  generator: social_wealth.graph_generator
-  n: 5
-network_agents:
-  - agent_type: social_wealth.SocialMoneyAgent
-    weight: 1
-environment_class: social_wealth.MoneyEnv
-environment_params:
-  num_mesa_agents: 5
-  mesa_agent_type: social_wealth.MoneyAgent
+model_class: social_wealth.MoneyEnv
+model_params:
+  topologies:
+    default: 
+      params:
+        generator: social_wealth.graph_generator
+        n: 5
+  agents:
+    distribution:
+      - agent_class: social_wealth.SocialMoneyAgent
+        topology: default
+        weight: 1
+  mesa_agent_class: 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

@@ -71,10 +71,9 @@ class SocialMoneyAgent(NetworkAgent, MoneyAgent):
 
 class MoneyEnv(Environment):
     """A model with some number of agents."""
-    def __init__(self, N, width, height, *args, network_params, **kwargs):
+    def __init__(self, width, height, *args, topologies, **kwargs):
 
-        network_params['n'] = N
-        super().__init__(*args, network_params=network_params, **kwargs)
+        super().__init__(*args, topologies=topologies, **kwargs)
         self.grid = MultiGrid(width, height, False)
 
         # Create agents
@@ -100,7 +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

@@ -1,19 +1,18 @@
 ---
 default_state: {}
-load_module: newsspread
 environment_agents: []
 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: DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: false
   weight: 1
-- agent_type: DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: true
   weight: 1
@@ -24,28 +23,27 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
-load_module: newsspread
 environment_agents: []
 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: DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: false
   weight: 1
-- agent_type: DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: true
   weight: 1
-- agent_type: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: false
   weight: 1
-- agent_type: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
   weight: 1
@@ -56,21 +54,20 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
-load_module: newsspread
 environment_agents: []
 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: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
     state_id: neutral
   weight: 1
-- agent_type: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
     state_id: neutral
@@ -82,22 +79,21 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
-load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   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: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
     state_id: neutral
   weight: 1
-- agent_type: WiseViewer
+- agent_class: newsspread.WiseViewer
   state:
     has_tv: true
   weight: 1
@@ -108,22 +104,21 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
-load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   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: WiseViewer
+- agent_class: newsspread.WiseViewer
   state:
     has_tv: true
     state_id: neutral
   weight: 1
-- agent_type: WiseViewer
+- agent_class: newsspread.WiseViewer
   state:
     has_tv: true
   weight: 1

examples/newsspread/newsspread.py

@@ -1,8 +1,8 @@
-from soil.agents import FSM, state, default_state, prob
+from soil.agents import FSM, NetworkAgent, state, default_state, prob
 import logging
 
 
-class DumbViewer(FSM):
+class DumbViewer(FSM, NetworkAgent):
     '''
     A viewer that gets infected via TV (if it has one) and tries to infect
     its neighbors once it's infected.
@@ -16,16 +16,22 @@ class DumbViewer(FSM):
     @state
     def neutral(self):
         if self['has_tv']:
-            if prob(self.env['prob_tv_spread']):
-                self.set_state(self.infected)
+            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):
+        '''
+        This is not a state. It is a function that other agents can use to try to
+        infect this agent. DumbViewer always gets infected, but other agents like
+        HerdViewer might not become infected right away
+        '''
+
         self.set_state(self.infected)
 
 
@@ -35,12 +41,13 @@ class HerdViewer(DumbViewer):
     '''
 
     def infect(self):
+        '''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):
-            self.set_state(self.infected.id)
+        if self.prob(prob_infect):
+            self.set_state(self.infected)
 
 
 class WiseViewer(HerdViewer):
@@ -56,9 +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:
@@ -73,7 +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)
-        if prob(prob_cure):
-            return self.cure()
+        prob_cure = self.model['prob_neighbor_cure'] * (cured/infected)
+        if self.prob(prob_cure):
+            return self.cured
         return self.set_state(super().infected)

examples/programmatic/programmatic.py

@@ -18,21 +18,23 @@ class MyAgent(agents.FSM):
     @agents.default_state
     @agents.state
     def neutral(self):
-        self.info('I am running')
+        self.debug('I am running')
+        if agents.prob(0.2):
+            self.info('This runs 2/10 times on average')
 
 
 s = Simulation(name='Programmatic',
                network_params={'generator': mygenerator},
                num_trials=1,
                max_time=100,
-               agent_type=MyAgent,
+               agent_class=MyAgent,
                dry_run=True)
 
 
+# By default, logging will only print WARNING logs (and above).
+# You need to choose a lower logging level to get INFO/DEBUG traces
 logging.basicConfig(level=logging.INFO)
 envs = s.run()
 
-s.dump_yaml()
-
-for env in envs:
-    env.dump_csv()
+# Uncomment this to output the simulation to a YAML file
+# s.dump_yaml('simulation.yaml')

examples/pubcrawl/pubcrawl.py

@@ -1,6 +1,5 @@
-from soil.agents import FSM, state, default_state
+from soil.agents import FSM, NetworkAgent, state, default_state
 from soil import Environment
-from random import random, shuffle
 from itertools import islice
 import logging
 
@@ -53,7 +52,7 @@ class CityPubs(Environment):
                 pub['occupancy'] -= 1
 
 
-class Patron(FSM):
+class Patron(FSM, NetworkAgent):
     '''Agent that looks for friends to drink with. It will do three things:
         1) Look for other patrons to drink with
         2) Look for a bar where the agent and other agents in the same group can get in.
@@ -61,12 +60,10 @@ class Patron(FSM):
     '''
     level = logging.DEBUG
 
-    defaults = {
-        'pub': None,
-        'drunk': False,
-        'pints': 0,
-        'max_pints': 3,
-    }
+    pub = None
+    drunk = False
+    pints = 0
+    max_pints = 3
 
     @default_state
     @state
@@ -90,9 +87,9 @@ class Patron(FSM):
             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):
         Try to become friends with another agent. The chances of
         success depend on both agents' openness.
         '''
-        if force or self['openness'] > random():
-            self.env.add_edge(self, other_agent)
+        if force or self['openness'] > self.random.random():
+            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):
         ''' 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

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

@@ -1,122 +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):
-
-    level = logging.INFO
-
-    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):
-        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=self.fertile.id, gender=Genders.female.value, 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):
-        if self['gender'] == Genders.female.value:
-            raise NotImplementedError('Females cannot impregnate')
-        whom['pregnancy'] = 0
-        whom['mate'] = self.id
-        whom.set_state(whom.pregnant)
-        self.debug('{} impregnating: {}. {}'.format(self.id, whom.id, whom.state))
-
-    @state
-    def pregnant(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        self['pregnancy'] += 1
-        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):
-        self.info('Agent {} is dying'.format(self.id))
-        if 'pregnancy' in self and self['pregnancy'] > -1:
-            self.info('A mother has died carrying a baby!!')
-        self.die()
-        return
-
-
-class RandomAccident(NetworkAgent):
-
-    level = logging.DEBUG
-
-    def step(self):
-        rabbits_total = self.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.count_agents(state_id=RabbitModel.dead.id) == self.topology.number_of_nodes():
-            self.die()

examples/rabbits/rabbits.yml

@@ -1,23 +0,0 @@
----
-load_module: rabbit_agents
-name: rabbits_example
-max_time: 150
-interval: 1
-seed: MySeed
-agent_type: RabbitModel
-environment_agents:
-    - agent_type: RandomAccident
-environment_params:
-  prob_death: 0.001
-default_state:
-  mating_prob: 0.01
-topology:
-  nodes:
-    - id: 1
-      state:
-        gender: female
-    - id: 0
-      state:
-        gender: male
-  directed: true
-  links: []

examples/random_delays/random_delays.py

@@ -0,0 +1,44 @@
+'''
+Example of setting a 
+Example of a fully programmatic simulation, without definition files.
+'''
+from soil import Simulation, agents
+from soil.time import Delta
+import logging
+
+
+
+class MyAgent(agents.FSM):
+    '''
+    An agent that first does a ping
+    '''
+
+    defaults = {'pong_counts': 2}
+
+    @agents.default_state
+    @agents.state
+    def ping(self):
+        self.info('Ping')
+        return self.pong, Delta(self.random.expovariate(1/16))
+
+    @agents.state
+    def pong(self):
+        self.info('Pong')
+        self.pong_counts -= 1
+        self.info(str(self.pong_counts))
+        if self.pong_counts < 1:
+            return self.die()
+        return None, Delta(self.random.expovariate(1/16))
+
+
+s = Simulation(name='Programmatic',
+               network_agents=[{'agent_class': MyAgent, 'id': 0}],
+               topology={'nodes': [{'id': 0}], 'links': []},
+               num_trials=1,
+               max_time=100,
+               agent_class=MyAgent,
+               dry_run=True)
+
+
+logging.basicConfig(level=logging.INFO)
+envs = s.run()

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
-  network_params:
-    generator: complete_graph
-    n: 10
-  network_agents:
-    - agent_type: CounterModel
-      weight: "{{ x1 }}"
-      state:
-        state_id: 0
-    - agent_type: AggregatedCounter
-      weight: "{{ 1 - x1 }}"
-  environment_params:
+  model_params:
+    network_params:
+      generator: complete_graph
+      n: 10
+    network_agents:
+      - agent_class: CounterModel
+        weight: "{{ x1 }}"
+        state:
+          state_id: 0
+      - agent_class: AggregatedCounter
+        weight: "{{ 1 - x1 }}"
     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))
-            step_neighbours = set(self.ego_search(self.sphere_influence, agent_type=TerroristNetworkModel, center=False))
-            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_type=TerroristNetworkModel))
+            close_ups = set(self.geo_search(radius=self.vision_range, agent_class=TerroristNetworkModel))
+            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_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]
-        target_x, target_y = nx.get_node_attributes(self.topology, 'pos')[target]
+        source_x, source_y = nx.get_node_attributes(self.G, 'pos')[self.id]
+        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

@@ -1,32 +1,31 @@
 name: TerroristNetworkModel_sim
-load_module: TerroristNetworkModel
-max_time: 150 
+max_steps: 150 
 num_trials: 1
-network_params:
-  generator: random_geometric_graph
-  radius: 0.2
-  # generator: geographical_threshold_graph
-  # theta: 20
-  n: 100
-network_agents:
-  - agent_type: TerroristNetworkModel
-    weight: 0.8
-    state:
-      id: civilian  # Civilians
-  - agent_type: TerroristNetworkModel
-    weight: 0.1
-    state:
-      id: leader    # Leaders
-  - agent_type: TrainingAreaModel
-    weight: 0.05
-    state:
-      id: terrorist # Terrorism
-  - agent_type: HavenModel
-    weight: 0.05
-    state:
-      id: civilian  # Civilian
+model_params:
+  network_params:
+    generator: random_geometric_graph
+    radius: 0.2
+    # generator: geographical_threshold_graph
+    # theta: 20
+    n: 100
+  network_agents:
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
+      weight: 0.8
+      state:
+        id: civilian  # Civilians
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
+      weight: 0.1
+      state:
+        id: leader    # Leaders
+    - agent_class: TerroristNetworkModel.TrainingAreaModel
+      weight: 0.05
+      state:
+        id: terrorist # Terrorism
+    - 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

@@ -1,14 +1,15 @@
 ---
 name: torvalds_example
-max_time: 10
+max_steps: 10
 interval: 2
-agent_type: CounterModel
-default_state:
-  skill_level: 'beginner'
-network_params:
-  path: 'torvalds.edgelist'
-states:
-  Torvalds:
-    skill_level: 'God'
-  balkian:
-    skill_level: 'developer'
+model_params:
+  agent_class: CounterModel
+  default_state:
+    skill_level: 'beginner'
+  network_params:
+    path: 'torvalds.edgelist'
+  states:
+    Torvalds:
+      skill_level: 'God'
+    balkian:
+      skill_level: 'developer'

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
-tsih>=0.1.5
+Mesa>=1
+pydantic>=1.9
+sqlalchemy>=1.4

setup.py

@@ -49,6 +49,7 @@ setup(
     extras_require=extras_require,
     tests_require=test_reqs,
     setup_requires=['pytest-runner', ],
+    pytest_plugins = ['pytest_profiling'],
     include_package_data=True,
     entry_points={
         'console_scripts':

soil/VERSION

@@ -1 +1 @@
-0.20.0
+0.20.7

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
 
@@ -29,20 +30,22 @@ 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')
     parser.add_argument('--module', '-m', type=str,
                         help='file containing the code of any custom agents.')
     parser.add_argument('--dry-run', '--dry', action='store_true',
-                        help='Do not store the results of the simulation.')
+                        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 GEXF graph. Defaults to false.')
+                        help='Dump each trial\'s network topology as a GEXF graph. Defaults to false.')
     parser.add_argument('--csv', action='store_true',
-                        help='Dump history in CSV format. Defaults to false.')
+                        help='Dump all data collected in CSV format. Defaults to false.')
     parser.add_argument('--level', type=str,
                         help='Logging level')
     parser.add_argument('--output', '-o', type=str, default="soil_output",
@@ -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
@@ -65,6 +81,11 @@ def main():
 
     logger.info('Loading config file: {}'.format(args.file))
 
+    if args.pdb or args.debug:
+        args.synchronous = True
+    if args.debug:
+        os.environ['SOIL_DEBUG'] = 'true'
+
     try:
         exporters = list(args.exporter or ['default', ])
         if args.csv:
@@ -78,18 +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,
-                                   dry_run=args.dry_run,
-                                   exporters=exporters,
-                                   parallel=(not args.synchronous),
-                                   outdir=args.output,
-                                   exporter_params=exp_params)
-    except Exception:
+        for sim in simulation.iter_from_config(args.file):
+            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,
+                               **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 random.random() < self.tweet_relevant_probability:  # Tweets something relevant
+        if self.random.random() < self.tweet_probability:  # Tweets
+            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

@@ -7,9 +7,13 @@ class CounterModel(NetworkAgent):
     in each step and adds it to its state.
     """
 
+    times = 0
+    neighbors = 0
+    total = 0
+
     def step(self):
         # Outside effects
-        total = len(list(self.get_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
@@ -22,17 +26,15 @@ class AggregatedCounter(NetworkAgent):
     in each step and adds it to its state.
     """
 
-    defaults = {
-        'times': 0,
-        'neighbors': 0,
-        'total': 0
-    }
+    times = 0
+    neighbors = 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.get_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
 
@@ -24,23 +23,26 @@ 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'],
-                                                           environment.environment_params['standard_variance'])
 
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
-                                            environment.environment_params['standard_variance'])
+        # 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_cured_healing_infected = np.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'],
-                                                             environment.environment_params['standard_variance'])
+        self.prob_infect = random.normal(environment.environment_params['prob_infect'],
+                                         environment.environment_params['standard_variance'])
 
-        self.prob_vaccinated_healing_infected = np.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'],
-                                                                  environment.environment_params['standard_variance'])
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
+        self.prob_cured_healing_infected = random.normal(environment.environment_params['prob_cured_healing_infected'],
                                                          environment.environment_params['standard_variance'])
+        self.prob_cured_vaccinate_neutral = random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
+                                                          environment.environment_params['standard_variance'])
+
+        self.prob_vaccinated_healing_infected = random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
+                                                              environment.environment_params['standard_variance'])
+        self.prob_vaccinated_vaccinate_neutral = random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
+                                                               environment.environment_params['standard_variance'])
+        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/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

@@ -0,0 +1,266 @@
+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
+
+
+# Could use TypeAlias in python >= 3.10
+nodeId = int
+
+class Node(BaseModel):
+    id: nodeId
+    state: Optional[Dict[str, Any]] = {}
+
+
+class Edge(BaseModel):
+    source: nodeId
+    target: nodeId
+    value: Optional[float] = 1
+
+
+class Topology(BaseModel):
+    nodes: List[Node]
+    directed: bool
+    links: List[Edge]
+
+
+class NetParams(BaseModel, extra=Extra.allow):
+    generator: Union[Callable, str]
+    n: int 
+
+
+class NetConfig(BaseModel):
+    params: Optional[NetParams]
+    topology: Optional[Union[Topology, nx.Graph]]
+    path: Optional[str]
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    @staticmethod
+    def default():
+        return NetConfig(topology=None, params=None)
+
+    @root_validator
+    def validate_all(cls,  values):
+        if 'params' not in values and 'topology' not in values:
+            raise ValueError('You must specify either a topology or the parameters to generate a graph')
+        return values
+
+
+class EnvConfig(BaseModel):
+
+    @staticmethod
+    def default():
+        return EnvConfig()
+
+
+class SingleAgentConfig(BaseModel):
+    agent_class: Optional[Union[Type, str]] = None
+    unique_id: Optional[int] = None
+    topology: Optional[str] = None
+    node_id: Optional[Union[int, 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:
+            raise ValueError(f"An agent_id can only be provided when there is only one agent ({values.get('n')} given)")
+        return values
+
+
+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]
+    distribution: Optional[List[AgentDistro]] = None
+    fixed: Optional[List[FixedAgentConfig]] = None
+    override: Optional[List[OverrideAgentConfig]] = None
+
+    @staticmethod
+    def default():
+        return AgentConfig()
+
+    @root_validator
+    def validate_all(cls,  values):
+        if 'distribution' in values and ('n' not in values and 'topology' not in values):
+            raise ValueError("You need to provide the number of agents or a topology to extract the value from.")
+        return values
+
+
+class Config(BaseModel, extra=Extra.allow):
+    version: Optional[str] = '1'
+
+    name: str = 'Unnamed Simulation'
+    description: Optional[str] = None
+    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):
+    '''
+    Try to convert old style configs into the new format.
+
+    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 = old.copy()
+
+    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
+
+    topologies = {}
+    if network:
+        topologies['default'] = network
+
+
+    agents = {'fixed': [], 'distribution': []}
+
+    def updated_agent(agent):
+        '''Convert an agent definition'''
+        newagent = dict(agent)
+        return newagent
+
+    by_weight = []
+    fixed = []
+    override = []
+
+    if 'environment_agents' in new:
+
+        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_class' in old and (not fixed and not by_weight):
+        agents['topology'] = 'default'
+        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()
+        else:
+            states = enumerate(states)
+        for (k, v) in states:
+            override.append({'filter': {'node_id': k},
+                             'state': v})
+
+    agents['override'] = override
+    agents['fixed'] = fixed
+    agents['distribution'] = by_weight
+
+
+    model_params = {}
+    if 'environment_params' in new:
+        del new['environment_params']
+        model_params = dict(old['environment_params'])
+
+    if 'environment_class' in old:
+        del new['environment_class']
+        new['model_class'] = old['environment_class']
+
+    if 'dump' in old:
+        del new['dump']
+        new['dry_run'] = not old['dump']
+
+    model_params['topologies'] = topologies
+    model_params['agents'] = agents
+
+    return Config(version='2',
+                  model_params=model_params,
+                  **new)

soil/datacollection.py

@@ -2,25 +2,5 @@ from mesa import DataCollector as MDC
 
 class SoilDataCollector(MDC):
 
-
-    def __init__(self, environment, *args, **kwargs):
+    def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        # Populate model and env reporters so they have a key per 
-        # So they can be shown in the web interface
-        self.environment = environment
-
-
-    @property
-    def model_vars(self):
-        pass
-
-    @model_vars.setter
-    def model_vars(self, value):
-        pass
-
-    @property
-    def agent_reporters(self):
-        self.model._history._
-
-        pass
-

soil/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

@@ -1,105 +1,130 @@
+from __future__ import annotations
+
 import os
 import sqlite3
-import csv
 import math
 import random
-import yaml
-import tempfile
-import pandas as pd
+import logging
+
+from typing import Any, Dict, Optional, Union
+from collections import namedtuple
 from time import time as current_time
 from copy import deepcopy
 from networkx.readwrite import json_graph
 
+
 import networkx as nx
 
-from tsih import History, Record, Key, NoHistory
-
 from mesa import Model
+from mesa.datacollection import DataCollector
 
-from . import serialization, agents, analysis, utils, time
+from . import agents as agentmod, config, serialization, utils, time, network
 
-# These properties will be copied when pickling/unpickling the environment
-_CONFIG_PROPS = [ 'name',
-                  'states',
-                  'default_state',
-                  'interval',
-                 ]
 
-class Environment(Model):
+Record = namedtuple('Record', 'dict_id t_step key value')
+
+
+class BaseEnvironment(Model):
     """
-    The environment is key in a simulation. It contains the network topology,
-    a reference to network and environment agents, as well as the environment
-    params, which are used as shared state between agents.
+    The environment is key in a simulation. It controls how agents interact,
+    and what information is available to them.
+
+    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, name=None,
-                 network_agents=None,
-                 environment_agents=None,
-                 states=None,
-                 default_state=None,
-                 interval=1,
-                 network_params=None,
-                 seed=None,
-                 topology=None,
+    def __init__(self,
+                 id='unnamed_env',
+                 seed='default',
                  schedule=None,
-                 initial_time=0,
-                 environment_params=None,
-                 history=True,
                  dir_path=None,
-                 **kwargs):
+                 interval=1,
+                 agent_class=None,
+                 agents: [tuple[type, Dict[str, Any]]] = {},
+                 agent_reporters: Optional[Any] = None,
+                 model_reporters: Optional[Any] = None,
+                 tables: Optional[Any] = None,
+                 **env_params):
 
+        super().__init__(seed=seed)
+        self.current_id = -1
 
-        super().__init__()
+        self.id = id
+
+        self.dir_path = dir_path or os.getcwd()
 
-        self.schedule = schedule
         if schedule is None:
-            self.schedule = time.TimedActivation()
+            schedule = time.TimedActivation(self)
+        self.schedule = schedule
 
-        self.name = name or 'UnnamedEnvironment'
-        seed = seed or current_time()
-        random.seed(seed)
-        if isinstance(states, list):
-            states = dict(enumerate(states))
-        self.states = deepcopy(states) if states else {}
-        self.default_state = deepcopy(default_state) or {}
+        self.agent_class = agent_class or agentmod.BaseAgent
 
-        if topology is None:
-            network_params = network_params or {}
-            topology = serialization.load_network(network_params,
-                                                  dir_path=dir_path)
-        if not topology:
-            topology = nx.Graph()
-        self.G = nx.Graph(topology) 
+        self.init_agents(agents)
 
+        self.env_params = env_params or {}
 
-        self.environment_params = environment_params or {}
-        self.environment_params.update(kwargs)
-
-        self._env_agents = {}
         self.interval = interval
-        if history:
-            history = History
-        else:
-            history = NoHistory
-        self._history = history(name=self.name,
-                                backup=True)
-        self['SEED'] = seed
 
-        if network_agents:
-            distro = agents.calculate_distribution(network_agents)
-            self.network_agents = agents._convert_agent_types(distro)
-        else:
-            self.network_agents = []
+        self.logger = utils.logger.getChild(self.id)
 
-        environment_agents = environment_agents or []
-        if environment_agents:
-            distro = agents.calculate_distribution(environment_agents)
-            environment_agents = agents._convert_agent_types(distro)
-        self.environment_agents = environment_agents
+        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 agents(self):
+        return agentmod.AgentView(self.schedule._agents)
+
+    def find_one(self, *args, **kwargs):
+        return agentmod.AgentView(self.schedule._agents).one(*args, **kwargs)
+    
+    def count_agents(self, *args, **kwargs):
+        return sum(1 for i in self.agents(*args, **kwargs))
 
     @property
     def now(self):
@@ -107,271 +132,171 @@ class Environment(Model):
             return self.schedule.time
         raise Exception('The environment has not been scheduled, so it has no sense of time')
 
-    @property
-    def agents(self):
-        yield from self.environment_agents
-        yield from self.network_agents
 
-    @property
-    def environment_agents(self):
-        for ref in self._env_agents.values():
-            yield ref
-
-    @environment_agents.setter
-    def environment_agents(self, environment_agents):
-        self._environment_agents = environment_agents
-
-        self._env_agents = agents._definition_to_dict(definition=environment_agents)
-
-    @property
-    def network_agents(self):
-        for i in self.G.nodes():
-            node = self.G.nodes[i]
-            if 'agent' in node:
-                yield node['agent']
-
-    @network_agents.setter
-    def network_agents(self, network_agents):
-        self._network_agents = network_agents
-        for ix in self.G.nodes():
-            self.init_agent(ix, agent_definitions=network_agents)
-
-    def init_agent(self, agent_id, agent_definitions):
-        node = self.G.nodes[agent_id]
-        init = False
-        state = dict(node)
-
-        agent_type = None
-        if 'agent_type' in self.states.get(agent_id, {}):
-            agent_type = self.states[agent_id]['agent_type']
-        elif 'agent_type' in node:
-            agent_type = node['agent_type']
-        elif 'agent_type' in self.default_state:
-            agent_type = self.default_state['agent_type']
-
-        if agent_type:
-            agent_type = agents.deserialize_type(agent_type)
-        elif agent_definitions:
-            agent_type, 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_type, state)
-
-    def set_agent(self, agent_id, agent_type, state=None):
-        node = self.G.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)
+    def add_agent(self, agent_id, agent_class, **kwargs):
         a = None
-        if agent_type:
-            state = defstate
-            a = agent_type(model=self,
-                           unique_id=agent_id)
+        if agent_class:
+            a = agent_class(model=self,
+                            unique_id=agent_id,
+                            **kwargs)
 
-        for (k, v) in getattr(a, 'defaults', {}).items():
-            if not hasattr(a, k) or getattr(a, k) is None:
-                setattr(a, k, v)
-
-        for (k, v) in state.items():
-            setattr(a, k, v)
-
-        node['agent'] = a
         self.schedule.add(a)
         return a
 
-    def add_node(self, agent_type, state=None):
-        agent_id = int(len(self.G.nodes()))
-        self.G.add_node(agent_id)
-        a = self.set_agent(agent_id, agent_type, state)
-        a['visible'] = True
-        return a
-
-    def add_edge(self, agent1, agent2, start=None, **attrs):
-        if hasattr(agent1, 'id'):
-            agent1 = agent1.id
-        if hasattr(agent2, 'id'):
-            agent2 = agent2.id
-        start = start or self.now
-        return self.G.add_edge(agent1, agent2, **attrs)
+    def log(self, message, *args, level=logging.INFO, **kwargs):
+        if not self.logger.isEnabledFor(level):
+            return
+        message = message + " ".join(str(i) for i in args)
+        message = " @{:>3}: {}".format(self.now, message)
+        for k, v in kwargs:
+            message += " {k}={v} ".format(k, v)
+        extra = {}
+        extra['now'] = self.now
+        extra['id'] = self.id
+        return self.logger.log(level, message, extra=extra)
 
     def step(self):
+        '''
+        Advance one step in the simulation, and update the data collection and scheduler appropriately
+        '''
         super().step()
-        self.datacollector.collect(self)
+        self.logger.info(f'--- Step {self.now:^5} ---')
         self.schedule.step()
-
-    def run(self, until, *args, **kwargs):
-        self._save_state()
-
-        while self.schedule.next_time <= until and not math.isinf(self.schedule.next_time):
-            self.schedule.step(until=until)
-            utils.logger.debug(f'Simulation step {self.schedule.time}/{until}. Next: {self.schedule.next_time}')
-        self._history.flush_cache()
-
-    def _save_state(self, now=None):
-        serialization.logger.debug('Saving state @{}'.format(self.now))
-        self._history.save_records(self.state_to_tuples(now=now))
-
-    def __getitem__(self, key):
-        if isinstance(key, tuple):
-            self._history.flush_cache()
-            return self._history[key]
-
-        return self.environment_params[key]
-
-    def __setitem__(self, key, value):
-        if isinstance(key, tuple):
-            k = Key(*key)
-            self._history.save_record(*k,
-                                      value=value)
-            return
-        self.environment_params[key] = value
-        self._history.save_record(dict_id='env',
-                                  t_step=self.now,
-                                  key=key,
-                                  value=value)
+        self.datacollector.collect(self)
 
     def __contains__(self, key):
-        return key in self.environment_params
+        return key in self.env_params
 
     def get(self, key, default=None):
         '''
-        Get the value of an environment attribute in a
-        given point in the simulation (history).
-        If key is an attribute name, this method returns
-        the current value.
-        To get values at other times, use a
-        :meth: `soil.history.Key` tuple.
+        Get the value of an environment attribute.
+        Return `default` if the value is not set.
         '''
-        return self[key] if key in self else default
+        return self.env_params.get(key, default)
 
-    def get_agent(self, agent_id):
-        return self.G.nodes[agent_id]['agent']
+    def __getitem__(self, key):
+        return self.env_params.get(key)
 
-    def get_agents(self, nodes=None):
-        if nodes is None:
-            return self.agents
-        return (self.G.nodes[i]['agent'] for i in nodes)
+    def __setitem__(self, key, value):
+        return self.env_params.__setitem__(key, value)
 
-    def dump_csv(self, f):
-        with utils.open_or_reuse(f, 'w') as f:
-            cr = csv.writer(f)
-            cr.writerow(('agent_id', 't_step', 'key', 'value'))
-            for i in self.history_to_tuples():
-                cr.writerow(i)
-
-    def dump_gexf(self, f):
-        G = self.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")
-
-    def dump(self, *args, formats=None, **kwargs):
-        if not formats:
-            return
-        functions = {
-            'csv': self.dump_csv,
-            'gexf': self.dump_gexf
-        }
-        for f in formats:
-            if f in functions:
-                functions[f](*args, **kwargs)
-            else:
-                raise ValueError('Unknown format: {}'.format(f))
-
-    def dump_sqlite(self, f):
-        return self._history.dump(f)
-
-    def state_to_tuples(self, now=None):
+    def _agent_to_tuples(self, agent, now=None):
         if now is None:
             now = self.now
-        for k, v in self.environment_params.items():
+        for k, v in agent.state.items():
+            yield Record(dict_id=agent.id,
+                          t_step=now,
+                          key=k,
+                          value=v)
+
+    def state_to_tuples(self, agent_id=None, now=None):
+        if now is None:
+            now = self.now
+
+        if agent_id:
+            agent = self.agents[agent_id]
+            yield from self._agent_to_tuples(agent, now)
+            return
+
+        for k, v in self.env_params.items():
             yield Record(dict_id='env',
                          t_step=now,
                          key=k,
                          value=v)
         for agent in self.agents:
-            for k, v in agent.state.items():
-                yield Record(dict_id=agent.id,
-                             t_step=now,
-                             key=k,
-                             value=v)
-
-    def history_to_tuples(self):
-        return self._history.to_tuples()
-
-    def history_to_graph(self):
-        G = nx.Graph(self.G)
-
-        for agent in self.network_agents:
-
-            attributes = {'agent': str(agent.__class__)}
-            lastattributes = {}
-            spells = []
-            lastvisible = False
-            laststep = None
-            history = self[agent.id, None, None]
-            if not history:
-                continue
-            for t_step, attribute, value in sorted(list(history)):
-                if attribute == 'visible':
-                    nowvisible = value
-                    if nowvisible and not lastvisible:
-                        laststep = t_step
-                    if not nowvisible and lastvisible:
-                        spells.append((laststep, t_step))
-
-                    lastvisible = nowvisible
-                    continue
-                key = 'attr_' + attribute
-                if key not in attributes:
-                    attributes[key] = list()
-                if key not in lastattributes:
-                    lastattributes[key] = (value, t_step)
-                elif lastattributes[key][0] != value:
-                    last_value, laststep = lastattributes[key]
-                    commit_value = (last_value, laststep, t_step)
-                    if key not in attributes:
-                        attributes[key] = list()
-                    attributes[key].append(commit_value)
-                    lastattributes[key] = (value, t_step)
-            for k, v in lastattributes.items():
-                attributes[k].append((v[0], v[1], None))
-            if lastvisible:
-                spells.append((laststep, None))
-            if spells:
-                G.add_node(agent.id, spells=spells, **attributes)
-            else:
-                G.add_node(agent.id, **attributes)
-
-        return G
-
-    def __getstate__(self):
-        state = {}
-        for prop in _CONFIG_PROPS:
-            state[prop] = self.__dict__[prop]
-        state['G'] = json_graph.node_link_data(self.G)
-        state['environment_agents'] = self._env_agents
-        state['history'] = self._history
-        state['schedule'] = self.schedule
-        return state
-
-    def __setstate__(self, state):
-        for prop in _CONFIG_PROPS:
-            self.__dict__[prop] = state[prop]
-        self._env_agents = state['environment_agents']
-        self.G = json_graph.node_link_graph(state['G'])
-        self._history = state['history']
-        # self._env = None
-        self.schedule = state['schedule']
-        self._queue = []
+            yield from self._agent_to_tuples(agent, now)
 
 
-SoilEnvironment = Environment
+class NetworkEnvironment(BaseEnvironment):
+
+    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

@@ -1,7 +1,8 @@
 import os
-import csv as csvlib
-import time
+from time import time as current_time
 from io import BytesIO
+from sqlalchemy import create_engine
+
 
 import matplotlib.pyplot as plt
 import networkx as nx
@@ -11,7 +12,7 @@ from .serialization import deserialize
 from .utils import open_or_reuse, logger, timer
 
 
-from . import utils
+from . import utils, network
 
 
 class DryRunner(BytesIO):
@@ -53,15 +54,19 @@ class Exporter:
         self.dry_run = dry_run
         self.copy_to = copy_to
 
-    def start(self):
+    def sim_start(self):
         '''Method to call when the simulation starts'''
         pass
 
-    def end(self, stats):
+    def sim_end(self):
         '''Method to call when the simulation ends'''
         pass
 
-    def trial(self, env, stats):
+    def trial_start(self, env):
+        '''Method to call when a trial start'''
+        pass
+
+    def trial_end(self, env):
         '''Method to call when a trial ends'''
         pass
 
@@ -80,79 +85,130 @@ class Exporter:
 class default(Exporter):
     '''Default exporter. Writes sqlite results, as well as the simulation YAML'''
 
-    def start(self):
+    def sim_start(self):
         if not self.dry_run:
             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:
+                f.write(self.simulation.to_yaml())
         else:
             logger.info('NOT dumping results')
 
-    def trial(self, env, stats):
+    def trial_end(self, env):
         if not self.dry_run:
             with timer('Dumping simulation {} trial {}'.format(self.simulation.name,
-                                                               env.name)):
-                with self.output('{}.sqlite'.format(env.name), mode='wb') as f:
-                    env.dump_sqlite(f)
+                                                               env.id)):
+                engine = create_engine('sqlite:///{}.sqlite'.format(env.id), echo=False)
 
-    def end(self, stats):
-          with timer('Dumping simulation {}\'s stats'.format(self.simulation.name)):
-              with self.output('{}.sqlite'.format(self.simulation.name), mode='wb') as f:
-                  self.simulation.dump_sqlite(f)
+                dc = env.datacollector
+                for (t, df) in get_dc_dfs(dc):
+                    df.to_sql(t, con=engine, if_exists='append')
 
 
+def get_dc_dfs(dc):
+    dfs = {'env': dc.get_model_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() 
+
 
 class csv(Exporter):
+
     '''Export the state of each environment (and its agents) in a separate CSV file'''
-    def trial(self, env, stats):
+    def trial_end(self, env):
         with timer('[CSV] Dumping simulation {} trial {} @ dir {}'.format(self.simulation.name,
-                                                                          env.name,
+                                                                          env.id,
                                                                           self.outdir)):
-            with self.output('{}.csv'.format(env.name)) as f:
-                env.dump_csv(f)
-
-            with self.output('{}.stats.csv'.format(env.name)) as f:
-                statwriter = csvlib.writer(f, delimiter='\t', quotechar='"', quoting=csvlib.QUOTE_ALL)
-
-                for stat in stats:
-                    statwriter.writerow(stat)
+            for (df_name, df) in get_dc_dfs(env.datacollector):
+                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(self, env, stats):
+    def trial_end(self, env):
         if self.dry_run:
             logger.info('Not dumping GEXF in dry_run mode')
             return
 
         with timer('[GEXF] Dumping simulation {} trial {}'.format(self.simulation.name,
-                                                                  env.name)):
-            with self.output('{}.gexf'.format(env.name), mode='wb') as f:
-                env.dump_gexf(f)
+                                                                  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)
 
 
 class dummy(Exporter):
 
-    def start(self):
+    def sim_start(self):
         with self.output('dummy', 'w') as f:
-            f.write('simulation started @ {}\n'.format(time.time()))
+            f.write('simulation started @ {}\n'.format(current_time()))
 
-    def trial(self, env, stats):
+    def trial_start(self, env):
         with self.output('dummy', 'w') as f:
-            for i in env.history_to_tuples():
-                f.write(','.join(map(str, i)))
-                f.write('\n')
+            f.write('trial started@ {}\n'.format(current_time()))
 
-    def sim(self, stats):
+    def trial_end(self, env):
+        with self.output('dummy', 'w') as f:
+            f.write('trial ended@ {}\n'.format(current_time()))
+
+    def sim_end(self):
         with self.output('dummy', 'a') as f:
-            f.write('simulation ended @ {}\n'.format(time.time()))
-
-
+            f.write('simulation ended @ {}\n'.format(current_time()))
 
 class graphdrawing(Exporter):
 
-    def trial(self, env, stats):
+    def trial_end(self, env):
         # Outside effects
         f = plt.figure()
         nx.draw(env.G, node_size=10, width=0.2, pos=nx.spring_layout(env.G, scale=100), ax=f.add_subplot(111))
-        with open('graph-{}.png'.format(env.name)) as f:
+        with open('graph-{}.png'.format(env.id)) as f:
             f.savefig(f)
 
+'''
+Convert an environment into a NetworkX graph
+'''
+def env_to_graph(env, history=None):
+    G = nx.Graph(env.G)
+
+    for agent in env.network_agents:
+
+        attributes = {'agent': str(agent.__class__)}
+        lastattributes = {}
+        spells = []
+        lastvisible = False
+        laststep = None
+        if not history:
+            history = sorted(list(env.state_to_tuples()))
+        for _, t_step, attribute, value in history:
+            if attribute == 'visible':
+                nowvisible = value
+                if nowvisible and not lastvisible:
+                    laststep = t_step
+                if not nowvisible and lastvisible:
+                    spells.append((laststep, t_step))
+
+                lastvisible = nowvisible
+                continue
+            key = 'attr_' + attribute
+            if key not in attributes:
+                attributes[key] = list()
+            if key not in lastattributes:
+                lastattributes[key] = (value, t_step)
+            elif lastattributes[key][0] != value:
+                last_value, laststep = lastattributes[key]
+                commit_value = (last_value, laststep, t_step)
+                if key not in attributes:
+                    attributes[key] = list()
+                attributes[key].append(commit_value)
+                lastattributes[key] = (value, t_step)
+        for k, v in lastattributes.items():
+            attributes[k].append((v[0], v[1], None))
+        if lastvisible:
+            spells.append((laststep, None))
+        if spells:
+            G.add_node(agent.id, spells=spells, **attributes)
+        else:
+            G.add_node(agent.id, **attributes)
+
+    return G

soil/network.py

@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+from typing import Dict
+import os
+import sys
+import random
+
+import networkx as nx
+
+from . import config, serialization, basestring
+
+def from_config(cfg: config.NetConfig, dir_path: str = None):
+    if not isinstance(cfg, config.NetConfig):
+        cfg = config.NetConfig(**cfg)
+
+    if cfg.path:
+        path = cfg.path
+        if dir_path and not os.path.isabs(path):
+            path = os.path.join(dir_path, path)
+        extension = os.path.splitext(path)[1][1:]
+        kwargs = {}
+        if extension == 'gexf':
+            kwargs['version'] = '1.2draft'
+            kwargs['node_type'] = int
+        try:
+            method = getattr(nx.readwrite, 'read_' + extension)
+        except AttributeError:
+            raise AttributeError('Unknown format')
+        return method(path, **kwargs)
+
+    if cfg.params:
+        net_args = cfg.params.dict()
+        net_gen = net_args.pop('generator')
+
+        if dir_path not in sys.path:
+            sys.path.append(dir_path)
+
+        method = serialization.deserializer(net_gen,
+                                            known_modules=['networkx.generators',])
+        return method(**net_args)
+
+    if isinstance(cfg.topology, config.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

@@ -2,10 +2,13 @@ import os
 import logging
 import ast
 import sys
+import re
 import importlib
 from glob import glob
 from itertools import product, chain
 
+from .config import Config
+
 import yaml
 import networkx as nx
 
@@ -15,38 +18,39 @@ from jinja2 import Template
 logger = logging.getLogger('soil')
 
 
-def load_network(network_params, dir_path=None):
-    G = nx.Graph()
+# def load_network(network_params, dir_path=None):
+#     G = nx.Graph()
 
-    if 'path' in network_params:
-        path = network_params['path']
-        if dir_path and not os.path.isabs(path):
-            path = os.path.join(dir_path, path)
-        extension = os.path.splitext(path)[1][1:]
-        kwargs = {}
-        if extension == 'gexf':
-            kwargs['version'] = '1.2draft'
-            kwargs['node_type'] = int
-        try:
-            method = getattr(nx.readwrite, 'read_' + extension)
-        except AttributeError:
-            raise AttributeError('Unknown format')
-        G = method(path, **kwargs)
+#     if not network_params:
+#         return G
 
-    elif 'generator' in network_params:
-        net_args = network_params.copy()
-        net_gen = net_args.pop('generator')
+#     if 'path' in network_params:
+#         path = network_params['path']
+#         if dir_path and not os.path.isabs(path):
+#             path = os.path.join(dir_path, path)
+#         extension = os.path.splitext(path)[1][1:]
+#         kwargs = {}
+#         if extension == 'gexf':
+#             kwargs['version'] = '1.2draft'
+#             kwargs['node_type'] = int
+#         try:
+#             method = getattr(nx.readwrite, 'read_' + extension)
+#         except AttributeError:
+#             raise AttributeError('Unknown format')
+#         G = method(path, **kwargs)
 
-        if dir_path not in sys.path:
-            sys.path.append(dir_path)
+#     elif 'generator' in network_params:
+#         net_args = network_params.copy()
+#         net_gen = net_args.pop('generator')
 
-        method = deserializer(net_gen,
-                              known_modules=['networkx.generators',])
-        G = method(**net_args)
-
-    return G
+#         if dir_path not in sys.path:
+#             sys.path.append(dir_path)
 
+#         method = deserializer(net_gen,
+#                               known_modules=['networkx.generators',])
+#         G = method(**net_args)
 
+#     return G
 
 
 def load_file(infile):
@@ -118,23 +122,26 @@ 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 'dir_path' not in config:
-                    config['dir_path'] = os.path.dirname(path)
-                yield config, path
+                yield Config.from_raw(cfg), path
 
 
-def load_config(config):
-    if isinstance(config, dict):
-        yield config, os.getcwd()
+def load_config(cfg):
+    if isinstance(cfg, Config):
+        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')
 
-def name(value, known_modules=[]):
+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:
         return 'None'
@@ -163,13 +170,30 @@ def serializer(type_):
     return lambda x: x
 
 
-def serialize(v, known_modules=[]):
+def serialize(v, known_modules=KNOWN_MODULES):
     '''Get a text representation of an object.'''
     tname = name(v, known_modules=known_modules)
     func = serializer(tname)
     return func(v), tname
 
-def deserializer(type_, known_modules=[]):
+
+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_
     if type_ == 'str':
@@ -179,17 +203,23 @@ def deserializer(type_, known_modules=[]):
     if hasattr(builtins, type_):  # Check if it's a builtin type
         cls = getattr(builtins, type_)
         return lambda x=None: ast.literal_eval(x) if x is not None else cls()
+    match = IS_CLASS.match(type_)
+    if match:
+        modname, tname = match.group(1).rsplit(".", 1)
+        module = importlib.import_module(modname)
+        cls = getattr(module, tname)
+        return getattr(cls, 'deserialize', cls)
+        
     # Otherwise, see if we can find the module and the class
-    modules = known_modules or []
     options = []
 
-    for mod in modules:
+    for mod in known_modules:
         if mod:
             options.append((mod, type_))
 
     if '.' in type_:  # Fully qualified module
         module, type_ = type_.rsplit(".", 1)
-        options.append ((module, type_))
+        options.append((module, type_))
 
     errors = []
     for modname, tname in options:
@@ -212,11 +242,11 @@ def deserialize(type_, value=None, **kwargs):
     return des(value)
 
 
-def deserialize_all(names, *args, known_modules=['soil'], **kwargs):
-    '''Return the set of exporters for a simulation, given the exporter names'''
-    exporters = []
+def deserialize_all(names, *args, known_modules=KNOWN_MODULES, **kwargs):
+    '''Return the list of deserialized objects'''
+    objects = []
     for name in names:
         mod = deserialize(name, known_modules=known_modules)
-        exporters.append(mod(*args, **kwargs))
-    return exporters
+        objects.append(mod(*args, **kwargs))
+    return objects
 

soil/simulation.py

@@ -1,166 +1,86 @@
 import os
-import time
+from time import time as current_time, strftime 
 import importlib
 import sys
 import yaml
 import traceback
+import inspect
 import logging
 import networkx as nx
-from networkx.readwrite import json_graph
-from multiprocessing import Pool
-from functools import partial
-from tsih import History
 
+from textwrap import dedent
+
+from dataclasses import dataclass, field, asdict
+from typing import Any, Dict, Union, Optional
+
+
+from networkx.readwrite import json_graph
+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 .stats import defaultStats
+from .time import INFINITY
+from .config import Config, convert_old
 
 
 #TODO: change documentation for simulation
-
+@dataclass
 class Simulation:
     """
-    Similar to nsim.NetworkSimulation with three main differences:
-        1) agent type can be specified by name or by class.
-        2) instead of just one type, a network agents distribution can be used.
-           The distribution specifies the weight (or probability) of each
-           agent type in the topology. This is an example distribution: ::
-
-                  [
-                    {'agent_type': 'agent_type_1',
-                     'weight': 0.2,
-                     'state': {
-                         'id': 0
-                      }
-                    },
-                    {'agent_type': 'agent_type_2',
-                     'weight': 0.8,
-                     'state': {
-                         'id': 1
-                      }
-                    }
-                  ]
-
-          In this example, 20% of the nodes will be marked as type
-          'agent_type_1'.
-        3) if no initial state is given, each node's state will be set
-           to `{'id': 0}`.
-
     Parameters
     ---------
-    name : str, optional
+    config (optional): :class:`config.Config`
         name of the Simulation
-    group : str, optional
-        a group name can be used to link simulations
-    topology : networkx.Graph instance, optional
-    network_params : dict
-        parameters used to create a topology with networkx, if no topology is given
-    network_agents : dict
-        definition of agents to populate the topology with
-    agent_type : NetworkAgent subclass, optional
-        Default type of NetworkAgent to use for nodes not specified in network_agents
-    states : list, optional
-        List of initial states corresponding to the nodes in the topology. Basic form is a list of integers
-        whose value indicates the state
-    dir_path: str, optional
-        Directory path to load simulation assets (files, modules...)
-    seed : str, optional
-        Seed to use for the random generator
-    num_trials : int, optional
-        Number of independent simulation runs
-    max_time : int, optional
-        Time how long the simulation should run
-    environment_params : dict, optional
-        Dictionary of globally-shared environmental parameters
-    environment_agents: dict, optional
-        Similar to network_agents. Distribution of Agents that control the environment
-    environment_class: soil.environment.Environment subclass, optional
-        Class for the environment. It defailts to soil.environment.Environment
-    load_module : str, module name, deprecated
-        If specified, soil will load the content of this module under 'soil.agents.custom'
-
 
+    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, name=None, group=None, topology=None, network_params=None,
-                 network_agents=None, agent_type=None, states=None,
-                 default_state=None, interval=1, num_trials=1,
-                 max_time=100, load_module=None, seed=None,
-                 dir_path=None, environment_agents=None,
-                 environment_params=None, environment_class=None,
-                 **kwargs):
+    @classmethod
+    def from_dict(cls, env):      
 
-        self.load_module = load_module
-        self.network_params = network_params
-        self.name = name or 'Unnamed'
-        self.seed = str(seed or name)
-        self._id = '{}_{}'.format(self.name, time.strftime("%Y-%m-%d_%H.%M.%S"))
-        self.group = group or ''
-        self.num_trials = num_trials
-        self.max_time = max_time
-        self.default_state = default_state or {}
-        self.dir_path = dir_path or os.getcwd()
-        self.interval = interval
+        ignored = {k: v for k, v in env.items() 
+                   if k not in inspect.signature(cls).parameters}
 
-        sys.path += list(x for x in [os.getcwd(), self.dir_path] if x not in sys.path)
+        kwargs = {k:v for k, v in env.items() if k not in ignored}
+        if ignored:
+            kwargs.setdefault('extra', {}).update(ignored)
+        if ignored:
+            print(f'Warning: Ignoring these parameters (added to "extra"): { ignored }')
 
-        if topology is None:
-            topology = serialization.load_network(network_params,
-                                                  dir_path=self.dir_path)
-        elif isinstance(topology, basestring) or isinstance(topology, dict):
-            topology = json_graph.node_link_graph(topology)
-        self.topology = nx.Graph(topology)
-
-
-        self.environment_params = environment_params or {}
-        self.environment_class = serialization.deserialize(environment_class,
-                                                   known_modules=['soil.environment', ]) or Environment
-
-        environment_agents = environment_agents or []
-        self.environment_agents = agents._convert_agent_types(environment_agents,
-                                                              known_modules=[self.load_module])
-
-        distro = agents.calculate_distribution(network_agents,
-                                               agent_type)
-        self.network_agents = agents._convert_agent_types(distro,
-                                                          known_modules=[self.load_module])
-
-        self.states = agents._validate_states(states,
-                                              self.topology)
-
-        self._history = History(name=self.name,
-                                backup=False)
+        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, *args, **kwargs):
-        if parallel and not os.environ.get('SENPY_DEBUG', None):
-            p = Pool()
-            func = partial(self.run_trial_exceptions,
-                           *args,
-                           **kwargs)
-            for i in p.imap_unordered(func, range(self.num_trials)):
-                if isinstance(i, Exception):
-                    logger.error('Trial failed:\n\t%s', i.message)
-                    continue
-                yield i
-        else:
-            for i in range(self.num_trials):
-                yield self.run_trial(*args,
-                                     **kwargs)
-
-    def run_gen(self, *args, parallel=False, dry_run=False,
-                exporters=[default, ], stats=[], outdir=None, exporter_params={},
-                stats_params={}, log_level=None,
+    def run_gen(self, parallel=False, dry_run=False,
+                exporters=[default, ], outdir=None, exporter_params={},
+                log_level=None,
                 **kwargs):
         '''Run the simulation and yield the resulting environments.'''
         if log_level:
@@ -169,190 +89,133 @@ 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)
-        stats = serialization.deserialize_all(simulation=self,
-                                              names=stats,
-                                              known_modules=['soil.stats',],
-                                              **stats_params)
 
         with utils.timer('simulation {}'.format(self.name)):
-            for stat in stats:
-                stat.start()
-
             for exporter in exporters:
-                exporter.start()
-            for env in self._run_sync_or_async(*args,
-                                               parallel=parallel,
-                                               log_level=log_level,
-                                               **kwargs):
+                exporter.sim_start()
 
-                collected = list(stat.trial(env) for stat in stats)
-
-                saved = self.save_stats(collected, t_step=env.now, trial_id=env.name)
+            for env in utils.run_parallel(func=self.run_trial,
+                                          iterable=range(int(self.num_trials)),
+                                          parallel=parallel,
+                                          log_level=log_level,
+                                          **kwargs):
 
                 for exporter in exporters:
-                    exporter.trial(env, saved)
+                    exporter.trial_start(env)
+
+                for exporter in exporters:
+                    exporter.trial_end(env)
 
                 yield env
 
-
-            collected = list(stat.end() for stat in stats)
-            saved = self.save_stats(collected)
-
             for exporter in exporters:
-                exporter.end(saved)
-
-
-    def save_stats(self, collection, **kwargs):
-        stats = dict(kwargs)
-        for stat in collection:
-            stats.update(stat)
-        self._history.save_stats(utils.flatten_dict(stats))
-        return stats
-
-    def get_stats(self, **kwargs):
-        return self._history.get_stats(**kwargs)
-
-    def log_stats(self, stats):
-        logger.info('Stats: \n{}'.format(yaml.dump(stats, default_flow_style=False)))
-    
+                exporter.sim_end()
 
     def get_env(self, trial_id=0, **kwargs):
         '''Create an environment for a trial of the simulation'''
-        opts = self.environment_params.copy()
-        opts.update({
-            'name': trial_id,
-            'topology': self.topology.copy(),
-            'network_params': self.network_params,
-            'seed': '{}_trial_{}'.format(self.seed, trial_id),
-            'initial_time': 0,
-            'interval': self.interval,
-            'network_agents': self.network_agents,
-            'initial_time': 0,
-            'states': self.states,
-            'dir_path': self.dir_path,
-            'default_state': self.default_state,
-            'environment_agents': self.environment_agents,
-        })
-        opts.update(kwargs)
-        env = self.environment_class(**opts)
-        return env
+        def deserialize_reporters(reporters):
+            for (k, v) in reporters.items():
+                if isinstance(v, str) and v.startswith('py:'):
+                    reporters[k] = serialization.deserialize(value.lsplit(':', 1)[1])
 
-    def run_trial(self, 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_{}'.format(self.name, time.time()).replace('.', '-')
         if log_level:
             logger.setLevel(log_level)
-        # Set-up trial environment and graph
-        until = until or self.max_time
-        env = self.get_env(trial_id=trial_id, **opts)
-        # Set up agents on nodes
+        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)):
-            env.run(until)
-        return env
+            return self.run_model(model=model, trial_id=trial_id, until=until, log_level=log_level)
 
-    def run_trial_exceptions(self, *args, **kwargs):
-        '''
-        A wrapper for run_trial that catches exceptions and returns them.
-        It is meant for async simulations
-        '''
-        try:
-            return self.run_trial(*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_model(self, model, until=None, **opts):
+        # Set-up trial environment and graph
+        until = float(until or self.max_time or 'inf')
+
+        # Set up agents on nodes
+        def is_done():
+            return False
+
+        if until and hasattr(model.schedule, 'time'):
+            prev = is_done
+
+            def is_done():
+                return prev() or model.schedule.time >= until
+
+        if self.max_steps and self.max_steps > 0 and hasattr(model.schedule, 'steps'):
+            prev_steps = is_done
+
+            def is_done():
+                return prev_steps() or model.schedule.steps >= self.max_steps
+
+        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.__getstate__()
+        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.to_dict())
 
 
-    def dump_yaml(self, f=None, outdir=None):
-        if not f and not outdir:
-            raise ValueError('specify a file or an output directory')
-
-        if not f:
-            f = os.path.join(outdir, '{}.dumped.yml'.format(self.name))
-
-        with utils.open_or_reuse(f, 'w') as f:
-            f.write(self.to_yaml())
-
-    def dump_pickle(self, f=None, outdir=None):
-        if not outdir and not f:
-            raise ValueError('specify a file or an output directory')
-
-        if not f:
-            f = os.path.join(outdir,
-                             '{}.simulation.pickle'.format(self.name))
-        with utils.open_or_reuse(f, 'wb') as f:
-            pickle.dump(self, f)
-
-    def dump_sqlite(self, f):
-        return self._history.dump(f)
-
-    def __getstate__(self):
-        state={}
-        for k, v in self.__dict__.items():
-            if k[0] != '_':
-                state[k] = v
-                state['topology'] = json_graph.node_link_data(self.topology)
-                state['network_agents'] = agents.serialize_definition(self.network_agents,
-                                                                      known_modules = [])
-                state['environment_agents'] = agents.serialize_definition(self.environment_agents,
-                                                                          known_modules = [])
-                state['environment_class'] = serialization.serialize(self.environment_class,
-                                                                     known_modules=['soil.environment'])[1]  # func, name
-        if state['load_module'] is None:
-            del state['load_module']
-        return state
-
-    def __setstate__(self, state):
-        self.__dict__ = state
-        self.load_module = getattr(self, 'load_module', None)
-        if self.dir_path not in sys.path:
-            sys.path += [self.dir_path, os.getcwd()]
-        self.topology = json_graph.node_link_graph(state['topology'])
-        self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents))
-        self.environment_agents = agents._convert_agent_types(self.environment_agents,
-                                                              known_modules=[self.load_module])
-        self.environment_class = serialization.deserialize(self.environment_class,
-                                                   known_modules=[self.load_module, 'soil.environment', ])  # func, name
-
-
-def all_from_config(config):
-    configs = list(serialization.load_config(config))
-    for config, _ in configs:
-        sim = Simulation(**config)
-        yield sim
+def iter_from_config(*cfgs):
+    for config in cfgs:
+        configs = list(serialization.load_config(config))
+        for config, path in configs:
+            d = dict(config)
+            if 'dir_path' not in d:
+                d['dir_path'] = os.path.dirname(path)
+            yield Simulation.from_dict(d)
 
 
 def from_config(conf_or_path):
-    config = list(serialization.load_config(conf_or_path))
-    if len(config) > 1:
+    lst = list(iter_from_config(conf_or_path))
+    if len(lst) > 1:
         raise AttributeError('Provide only one configuration')
-    config = config[0][0]
-    sim = Simulation(**config)
-    return sim
+    return lst[0]
 
 
 def run_from_config(*configs, **kwargs):
-    for config_def in configs:
-        # logger.info("Found {} config(s)".format(len(ls)))
-        for config, path in serialization.load_config(config_def):
-            name = config.get('name', 'unnamed')
-            logger.info("Using config(s): {name}".format(name=name))
-
-            dir_path = config.pop('dir_path', os.path.dirname(path))
-            sim = Simulation(dir_path=dir_path,
-                             **config)
-            sim.run_simulation(**kwargs)
+    for sim in iter_from_config(*configs):
+        logger.info(f"Using config(s): {sim.name}")
+        sim.run_simulation(**kwargs)

soil/stats.py

@@ -1,106 +0,0 @@
-import pandas as pd
-
-from collections import Counter
-
-class Stats:
-    '''
-    Interface for all stats. It is not necessary, but it is useful
-    if you don't plan to implement all the methods.
-    '''
-
-    def __init__(self, simulation):
-        self.simulation = simulation
-
-    def start(self):
-        '''Method to call when the simulation starts'''
-        pass
-
-    def end(self):
-        '''Method to call when the simulation ends'''
-        return {}
-
-    def trial(self, env):
-        '''Method to call when a trial ends'''
-        return {}
-
-
-class distribution(Stats):
-    '''
-    Calculate the distribution of agent states at the end of each trial,
-    the mean value, and its deviation.
-    '''
-
-    def start(self):
-        self.means = []
-        self.counts = []
-
-    def trial(self, env):
-        df = env[None, None, None].df()
-        df = df.drop('SEED', axis=1)
-        ix = df.index[-1]
-        attrs = df.columns.get_level_values(0)
-        vc = {}
-        stats = {
-            'mean': {},
-            'count': {},
-        }
-        for a in attrs:
-            t = df.loc[(ix, a)]
-            try:
-                stats['mean'][a] = t.mean()
-                self.means.append(('mean', a, t.mean()))
-            except TypeError:
-                pass
-
-            for name, count in t.value_counts().iteritems():
-                if a not in stats['count']:
-                    stats['count'][a] = {}
-                stats['count'][a][name] = count
-                self.counts.append(('count', a, name, count))
-
-        return stats
-
-    def end(self):
-        dfm = pd.DataFrame(self.means, columns=['metric', 'key', 'value'])
-        dfc = pd.DataFrame(self.counts, columns=['metric', 'key', 'value', 'count'])
-
-        count = {}
-        mean = {}
-
-        if self.means:
-            res = dfm.groupby(by=['key']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
-            mean = res['value'].to_dict()
-        if self.counts:
-            res = dfc.groupby(by=['key', 'value']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
-            for k,v in res['count'].to_dict().items():
-                if k not in count:
-                    count[k] = {}
-                for tup, times in v.items():
-                    subkey, subcount = tup
-                    if subkey not in count[k]:
-                        count[k][subkey] = {}
-                    count[k][subkey][subcount] = times
-
-
-        return {'count': count, 'mean': mean}
-
-
-class defaultStats(Stats):
-
-    def trial(self, env):
-        c = Counter()
-        c.update(a.__class__.__name__ for a in env.network_agents)
-
-        c2 = Counter()
-        c2.update(a['id'] for a in env.network_agents)
-
-        return {
-            'network ': {
-                'n_nodes': env.G.number_of_nodes(),
-                'n_edges': env.G.number_of_edges(),
-            },
-            'agents': {
-                'model_count': dict(c),
-                'state_count': dict(c2),
-            }
-        }

soil/time.py

@@ -1,20 +1,27 @@
 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
+from mesa import Agent as MesaAgent
 
 
+INFINITY = float('inf')
+
 class When:
     def __init__(self, time):
-        self._time = float(time)
+        if isinstance(time, When):
+            return time
+        self._time = time
 
     def abs(self, time):
         return self._time
 
 
-class Delta:
+NEVER = When(INFINITY)
+
+
+class Delta(When):
     def __init__(self, delta):
         self._delta = delta
 
@@ -31,57 +38,62 @@ 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: Agent):
-        if agent.unique_id not in self._agents:
-            heappush(self._queue, (self.time, agent.unique_id))
-            super().add(agent)
+    def add(self, agent: MesaAgent, when=None):
+        if when is None:
+            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, until: float =float('inf')) -> None:
+    def step(self) -> None:
         """
         Executes agents in order, one at a time. After each step,
         an agent will signal when it wants to be scheduled next.
         """
 
-        when = None
-        agent_id = None
-        unsched = []
-        until = until or float('inf')
+        self.logger.debug(f'Simulation step {self.next_time}')
+        if not self.model.running:
+            return
+
+        self.time = self.next_time
+        when = self.time
+
+        while self._queue and self._queue[0][0] == self.time:
+            (when, agent_id) = heappop(self._queue)
+            self.logger.debug(f'Stepping agent {agent_id}')
+
+            agent = self._agents[agent_id]
+            returned = agent.step()
+
+            if not agent.alive:
+                self.remove(agent)
+                continue
+
+            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
 
         if not self._queue:
-            self.time = until
-            self.next_time = float('inf')
-            return
+            self.time = INFINITY
+            self.next_time = INFINITY
+            self.model.running = False
+            return self.time
 
-        (when, agent_id) = self._queue[0]
-
-        if until and when > until:
-            self.time = until
-            self.next_time = when
-            return
-
-        self.time = when
-        next_time = float("inf")
-
-        while when == self.time:
-            heappop(self._queue)
-            logger.debug(f'Stepping agent {agent_id}')
-            when = (self._agents[agent_id].step() or Delta(1)).abs(self.time)
-            heappush(self._queue, (when, agent_id))
-            if when < next_time:
-                next_time = when
-
-            if not self._queue or self._queue[0][0] > self.time:
-                agent_id = None
-                break
-            else:
-                (when, agent_id) = self._queue[0]
-
-        if when and when < self.time:
-            raise Exception("Invalid scheduling time")
-
-        self.next_time = next_time
-        self.steps += 1
+        self.next_time = self._queue[0][0]
+        self.logger.debug(f'Next step: {self.next_time}')

soil/utils.py

@@ -1,55 +1,70 @@
 import logging
-import time
+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
 def timer(name='task', pre="", function=logger.info, to_object=None):
-    start = time.time()
+    start = current_time()
     function('{}Starting {} at {}.'.format(pre, name,
-                                           time.strftime("%X", time.gmtime(start))))
+                                           strftime("%X", gmtime(start))))
     yield start
-    end = time.time()
+    end = current_time()
     function('{}Finished {} at {} in {} seconds'.format(pre, name,
-                                                        time.strftime("%X", time.gmtime(end)),
+                                                        strftime("%X", gmtime(end)),
                                                         str(end-start)))
     if to_object:
         to_object.start = start
         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):
         os.makedirs(outdir)
     if backup and 'w' in mode and os.path.exists(path):
         creation = os.path.getctime(path)
-        stamp = time.strftime('%Y-%m-%d_%H.%M.%S', time.localtime(creation))
+        stamp = strftime('%Y-%m-%d_%H.%M.%S', localtime(creation))
 
         backup_dir = os.path.join(outdir, 'backup')
         if not os.path.exists(backup_dir):
             os.makedirs(backup_dir)
         newpath = os.path.join(backup_dir, '{}@{}'.format(os.path.basename(path),
-                                                               stamp))
+                                                          stamp))
         copyfile(path, newpath)
     return open(path, mode=mode, **kwargs)
 
 
+@contextmanager
 def open_or_reuse(f, *args, **kwargs):
     try:
-        return safe_open(f, *args, **kwargs)
+        with safe_open(f, *args, **kwargs) as f:
+            yield f
     except (AttributeError, TypeError):
-        return f
+        yield f
 
 def flatten_dict(d):
     if not isinstance(d, dict):
@@ -87,3 +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

test-requirements.txt

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

tests/complete_converted.yml

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

tests/old_complete.yml

@@ -0,0 +1,37 @@
+---
+name: simple
+group: tests
+dir_path: "/tmp/"
+num_trials: 3
+max_time: 100
+interval: 1
+seed: "CompleteSeed!"
+network_params:
+  generator: complete_graph
+  n: 4
+network_agents:
+  - agent_class: CounterModel
+    weight: 0.25
+    state:
+      state_id: 0
+      times: 1
+  - agent_class: AggregatedCounter
+    weight: 0.5
+    state:
+      times: 2
+environment_agents:
+  - agent_id: 'Environment Agent 1'
+    agent_class: BaseAgent
+    state:
+      times: 10
+environment_class: Environment
+environment_params:
+  am_i_complete: true
+agent_class: CounterModel
+default_state:
+  times: 1
+states:
+  1:
+    name: 'Node 1'
+  2:
+    name: 'Node 2'

tests/test_agents.py

@@ -0,0 +1,24 @@
+from unittest import TestCase
+import pytest
+
+from soil import agents, environment
+from soil import time as stime
+
+class Dead(agents.FSM):
+    @agents.default_state
+    @agents.state
+    def only(self):
+        return self.die()
+
+class TestMain(TestCase):
+    def test_die_raises_exception(self):
+        d = Dead(unique_id=0, model=environment.Environment())
+        d.step()
+        with pytest.raises(agents.DeadAgent):
+            d.step()
+
+    def test_die_returns_infinity(self):
+        d = Dead(unique_id=0, model=environment.Environment())
+        ret = d.step().abs(0)
+        print(ret, 'next')
+        assert ret == stime.INFINITY

tests/test_analysis.py

@@ -1,90 +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,
-            '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

@@ -0,0 +1,152 @@
+from unittest import TestCase
+import os
+import yaml
+import copy
+from os.path import join
+
+from soil import simulation, serialization, config, network, agents, utils
+
+ROOT = os.path.abspath(os.path.dirname(__file__))
+EXAMPLES = join(ROOT, '..', 'examples')
+
+FORCE_TESTS = os.environ.get('FORCE_TESTS', '')
+
+
+def isequal(a, b):
+    if isinstance(a, dict):
+        for (k, v) in a.items():
+            if v:
+                isequal(a[k], b[k])
+            else:
+                assert not b.get(k, None)
+        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')
+        })
+        net = network.from_config(netconfig, dir_path=ROOT)
+        assert len(net.nodes) == 2
+        assert len(net.edges) == 1
+
+    def test_env_from_config(self):
+        """
+        Simple configuration that tests that the graph is loaded, and that
+        network agents are initialized properly.
+        """
+        cfg = {
+            'name': 'CounterAgent',
+            'network_params': {
+                'path': join(ROOT, 'test.gexf')
+            },
+            'agent_class': 'CounterModel',
+            # 'states': [{'times': 10}, {'times': 20}],
+            'max_time': 2,
+            'dry_run': True,
+            'num_trials': 1,
+            'environment_params': {
+            }
+        }
+        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].G == env.topologies['default']
+
+    def test_agents_from_config(self):
+        '''We test that the known complete configuration produces
+        the right agents in the right groups'''
+        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) == 4
+        assert len(env.agents(group='network')) == 4
+        assert len(env.agents(group='environment')) == 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_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):
+        root = os.getcwd()
+        print(path)
+        s = simulation.from_config(cfg)
+        # for s in simulation.all_from_config(path):
+        #     iterations = s.config.max_time * s.config.num_trials
+        #     if iterations > 1000:
+        #         s.config.max_time = 100
+        #         s.config.num_trials = 1
+        #     if config.get('skip_test', False) and not FORCE_TESTS:
+        #         self.skipTest('Example ignored.')
+        #     envs = s.run_simulation(dry_run=True)
+        #     assert envs
+        #     for env in envs:
+        #         assert env
+        #         try:
+        #             n = config['network_params']['n']
+        #             assert len(list(env.network_agents)) == n
+        #             assert env.now > 0  # It has run
+        #             assert env.now <= config['max_time']  # But not further than allowed
+        #         except KeyError:
+        #             pass
+    return wrapped
+
+
+def add_example_tests():
+    for config, path in serialization.load_files(
+            join(EXAMPLES, '*', '*.yml'),
+            join(EXAMPLES, '*.yml'),
+    ):
+        p = make_example_test(path=path, cfg=config)
+        fname = os.path.basename(path)
+        p.__name__ = 'test_example_file_%s' % fname
+        p.__doc__ = '%s should be a valid configuration' % fname
+        setattr(TestConfig, p.__name__, p)
+        del p
+
+
+add_example_tests()

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):
-            iterations = s.max_time * s.num_trials
-            if iterations > 1000:
-                s.max_time = 100
+        for s in simulation.iter_from_config(cfg):
+            iterations = s.max_steps * s.num_trials
+            if iterations < 0 or iterations > 1000:
+                s.max_steps = 100
                 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

@@ -2,13 +2,12 @@ import os
 import io
 import tempfile
 import shutil
-from time import time
 
 from unittest import TestCase
 from soil import exporters
 from soil import simulation
+from soil import agents
 
-from soil.stats import distribution
 
 class Dummy(exporters.Exporter):
     started = False
@@ -19,45 +18,53 @@ class Dummy(exporters.Exporter):
     called_trial = 0
     called_end = 0
 
-    def start(self):
+    def sim_start(self):
         self.__class__.called_start += 1
         self.__class__.started = True
 
-    def trial(self, env, stats):
+    def trial_end(self, env):
         assert env
         self.__class__.trials += 1
         self.__class__.total_time += env.now
         self.__class__.called_trial += 1
 
-    def end(self, stats):
+    def sim_end(self):
         self.__class__.ended = True
         self.__class__.called_end += 1
 
 
 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': {},
-            'agent_type': 'CounterModel',
-            'max_time': 2,
-            'num_trials': 5,
-            'environment_params': {}
+            'model_params': {
+                'agents': [{
+                    'agent_class': agents.BaseAgent
+                }]
+            },
+            '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.trials == 5
-        assert Dummy.total_time == 2*5
+        assert Dummy.called_trial == num_trials
+        assert Dummy.trials == num_trials
+        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',
@@ -65,9 +72,10 @@ class Exporters(TestCase):
                 'generator': 'complete_graph',
                 'n': 4
             },
-            'agent_type': 'CounterModel',
+            'agent_class': 'CounterModel',
             'max_time': 2,
             'num_trials': n_trials,
+            'dry_run': False,
             'environment_params': {}
         }
         output = io.StringIO()
@@ -76,9 +84,8 @@ class Exporters(TestCase):
         envs = s.run_simulation(exporters=[
                                     exporters.default,
                                     exporters.csv,
-                                    exporters.gexf,
                                 ],
-                                stats=[distribution,],
+                                dry_run=False,
                                 outdir=tmpdir,
                                 exporter_params={'copy_to': output})
         result = output.getvalue()
@@ -90,11 +97,7 @@ class Exporters(TestCase):
 
         try:
             for e in envs:
-                with open(os.path.join(simdir, '{}.gexf'.format(e.name))) as f:
-                    result = f.read()
-                    assert result
-
-                with open(os.path.join(simdir, '{}.csv'.format(e.name))) as f:
+                with open(os.path.join(simdir, '{}.env.csv'.format(e.id))) as f:
                     result = f.read()
                     assert result
         finally:

tests/test_main.py

@@ -1,23 +1,20 @@
 from unittest import TestCase
 
 import os
-import io
-import yaml
 import pickle
 import networkx as nx
 from functools import partial
 
 from os.path import join
-from soil import (simulation, Environment, agents, serialization,
-                  utils)
+from soil import (simulation, Environment, agents, network, serialization,
+                  utils, config)
 from soil.time import Delta
 
-
 ROOT = os.path.abspath(os.path.dirname(__file__))
 EXAMPLES = join(ROOT, '..', 'examples')
 
 
-class CustomAgent(agents.FSM):
+class CustomAgent(agents.FSM, agents.NetworkAgent):
     @agents.default_state
     @agents.state
     def normal(self):
@@ -27,137 +24,108 @@ class CustomAgent(agents.FSM):
     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 = serialization.load_network(config['network_params'])
-        assert G
-        assert len(G) == 2
-        with self.assertRaises(AttributeError):
-            config = {
-                'network_params': {
-                    'path': join(ROOT, 'unknown.extension')
-                }
-            }
-            G = serialization.load_network(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
-        """
-        config = {
-            'network_params': {
-                'generator': 'barabasi_albert_graph'
-            }
-        }
-        with self.assertRaises(TypeError):
-            G = serialization.load_network(config['network_params'])
-        config['network_params']['n'] = 100
-        config['network_params']['m'] = 10
-        G = serialization.load_network(config['network_params'])
-        assert len(G) == 100
-
     def test_empty_simulation(self):
         """A simulation with a base behaviour should do nothing"""
         config = {
-            'network_params': {
-                'path': join(ROOT, 'test.gexf')
-            },
-            'agent_type': 'BaseAgent',
-            'environment_params': {
+            'model_params': {
+              'network_params': {
+                  'path': join(ROOT, 'test.gexf')
+              },
+              'agent_class': 'BaseAgent',
             }
         }
         s = simulation.from_config(config)
         s.run_simulation(dry_run=True)
 
+
+    def test_network_agent(self):
+        """
+        The initial states should be applied to the agent and the
+        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_class': 'CounterModel',
+                'states': {
+                    0: {'times': 10},
+                    1: {'times': 20},
+                },
+            }
+        }
+        s = simulation.from_config(config)
+
     def test_counter_agent(self):
         """
         The initial states should be applied to the agent and the
         agent should be able to update its state."""
         config = {
+            'version': '2',
             'name': 'CounterAgent',
-            'network_params': {
-                'path': join(ROOT, 'test.gexf')
-            },
-            'agent_type': 'CounterModel',
-            'states': [{'times': 10}, {'times': 20}],
-            'max_time': 2,
+            'dry_run': True,
             'num_trials': 1,
-            'environment_params': {
+            'max_time': 2,
+            'model_params': {
+                'topologies': {
+                    'default': {
+                        'path': join(ROOT, 'test.gexf')
+                    }
+                },
+                'agents': {
+                    'agent_class': 'CounterModel',
+                    'topology': 'default',
+                    'fixed': [{'state': {'times': 10}}, {'state': {'times': 20}}],
+                }
             }
         }
         s = simulation.from_config(config)
-        env = s.run_simulation(dry_run=True)[0]
-        assert env.get_agent(0)['times', 0] == 11
-        assert env.get_agent(0)['times', 1] == 12
-        assert env.get_agent(1)['times', 0] == 21
-        assert env.get_agent(1)['times', 1] == 22
+        env = s.get_env()
+        assert isinstance(env.agents[0], agents.CounterModel)
+        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_counter_agent_history(self):
-        """
-        The evolution of the state should be recorded in the logging agent
-        """
+    def test_init_and_count_agents(self):
+        """Agents should be properly initialized and counting should filter them properly"""
+        #TODO: separate this test into two or more test cases
         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': {
-            }
+            '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_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
+        assert env.agents[0].weight == 1
+        assert env.count_agents() == 2
+        assert env.count_agents(weight=1) == 1
+        assert env.count_agents(weight=3) == 1
+        assert env.count_agents(agent_class=CustomAgent) == 2
 
-    def test_custom_agent(self):
-        """Allow for search of neighbors with a certain state_id"""
-        config = {
-            'network_params': {
-                'path': join(ROOT, 'test.gexf')
-            },
-            'network_agents': [{
-                'agent_type': CustomAgent,
-                'weight': 1
-
-            }],
-            'max_time': 10,
-            'environment_params': {
-            }
-        }
-        s = simulation.from_config(config)
-        env = s.run_simulation(dry_run=True)[0]
-        assert env.get_agent(1).count_agents(state_id='normal') == 2
-        assert env.get_agent(1).count_agents(state_id='normal', limit_neighbors=True) == 1
-        assert env.get_agent(0).neighbors == 1
 
     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['network_params']['path'])
+        config['model_params']['network_params']['path'] = join(EXAMPLES,
+                                                config['model_params']['network_params']['path'])
         s = simulation.from_config(config)
         env = s.run_simulation(dry_run=True)[0]
         for a in env.network_agents:
@@ -175,80 +143,15 @@ class TestMain(TestCase):
                 assert a.state['total'] == 3
                 assert a.state['neighbors'] == 1
 
-    def test_yaml(self):
-        """
-        The YAML version of a newly created simulation
-        should be equivalent to the configuration file used
-        """
-        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)
-        with utils.timer('deleting'):
-            del recovered['topology']
-        assert config == recovered
-
-    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)
-
-        s.run_simulation(dry_run=True)
-        nconfig = s.to_dict()
-        del nconfig['topology']
-        assert config == nconfig
-
-    def test_row_conversion(self):
-        env = Environment()
-        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_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_save_graph(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)
-        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_serialize_class(self):
-        ser, name = serialization.serialize(agents.BaseAgent)
+        ser, name = serialization.serialize(agents.BaseAgent, known_modules=[])
         assert name == 'soil.agents.BaseAgent'
         assert ser == agents.BaseAgent
 
+        ser, name = serialization.serialize(agents.BaseAgent, known_modules=['soil', ])
+        assert name == 'BaseAgent'
+        assert ser == agents.BaseAgent
+
         ser, name = serialization.serialize(CustomAgent)
         assert name == 'test_main.CustomAgent'
         assert ser == CustomAgent
@@ -262,7 +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'
@@ -273,65 +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[1]['agent_type'] == CustomAgent
+        assert converted[0]['agent_class'] == agents.CounterModel
+        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[1]['agent_type'] == 'test_main.CustomAgent'
+        assert converted[0]['agent_class'] == 'CounterModel'
+        assert converted[1]['agent_class'] == 'test_main.CustomAgent'
         pickle.dumps(converted)
 
-    def test_pickle_agent_environment(self):
-        env = Environment(name='Test')
-        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'
-
-    def test_subgraph(self):
-        '''An agent should be able to subgraph the global topology'''
-        G = nx.Graph()
-        G.add_node(3)
-        G.add_edge(1, 2)
-        distro = agents.calculate_distribution(agent_type=agents.NetworkAgent)
-        env = Environment(name='Test', topology=G, network_agents=distro)
-        lst = list(env.network_agents)
-
-        a2 = env.get_agent(2)
-        a3 = env.get_agent(3)
-        assert len(a2.subgraph(limit_neighbors=True)) == 2
-        assert len(a3.subgraph(limit_neighbors=True)) == 1
-        assert len(a3.subgraph(limit_neighbors=True, center=False)) == 0
-        assert len(a3.subgraph(agent_type=agents.NetworkAgent)) == 3
-
     def test_templates(self):
         '''Loading a template should result in several configs'''
         configs = serialization.load_file(join(EXAMPLES, 'template.yml'))
@@ -340,19 +213,23 @@ class TestMain(TestCase):
     def test_until(self):
         config = {
             'name': 'until_sim',
-            'network_params': {},
-            'agent_type': 'CounterModel',
+            'model_params': {
+                'network_params': {},
+                'agents': {
+                    'fixed': [{
+                        'agent_class': agents.BaseAgent,
+                    }]
+                },
+            },
             'max_time': 2,
             'num_trials': 50,
-            'environment_params': {}
         }
         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

tests/test_stats.py

@@ -1,34 +0,0 @@
-from unittest import TestCase
-
-from soil import simulation, stats
-from soil.utils import unflatten_dict
-
-class Stats(TestCase):
-
-    def test_distribution(self):
-        '''The distribution exporter should write the number of agents in each state'''
-        config = {
-            'name': 'exporter_sim',
-            'network_params': {
-                'generator': 'complete_graph',
-                'n': 4
-            },
-            'agent_type': 'CounterModel',
-            'max_time': 2,
-            'num_trials': 5,
-            'environment_params': {}
-        }
-        s = simulation.from_config(config)
-        for env in s.run_simulation(stats=[stats.distribution]):
-            pass
-            # stats_res = unflatten_dict(dict(env._history['stats', -1, None]))
-        allstats = s.get_stats()
-        for stat in allstats:
-            assert 'count' in stat
-            assert 'mean' in stat
-            if 'trial_id' in stat:
-                assert stat['mean']['neighbors'] == 3
-                assert stat['count']['total']['4'] == 4
-            else:
-                assert stat['count']['count']['neighbors']['3'] == 20
-                assert stat['mean']['min']['neighbors'] == stat['mean']['max']['neighbors']