Add seed to cars example

Treating time and conditions as the same entity was getting confusing, and it
added a lot of unnecessary abstraction in a critical part (the scheduler).

The scheduling queue now has the time as a floating number (faster), the agent
id (for ties) and the condition, as well as the agent. The first three
elements (time, id, condition) can be considered as the "key" for the event.

To allow for agent execution to be "randomized" within every step, a new
parameter has been added to the scheduler, which makes it add a random number to
the key in order to change the ordering.

`EventedAgent.received` now checks the messages before returning control to the
user by default.

.gitlab-ci.yml

@@ -37,7 +37,7 @@ push_pypi:
     - 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 --ignore-existing dist/*
+    - TWINE_PASSWORD=$PYPI_PASSWORD TWINE_USERNAME=$PYPI_USERNAME python -m twine upload dist/*
 
 check_pypi:
   only:
@@ -48,3 +48,6 @@ check_pypi:
   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

@@ -3,6 +3,54 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.30 UNRELEASED]
+### Added
+* Simple debugging capabilities in `soil.debugging`, with a custom `pdb.Debugger` subclass that exposes commands to list agents and their status and set breakpoints on states (for FSM agents). Try it with `soil --debug <simulation file>`
+* Ability to run 
+* Ability to 
+* The `soil.exporters` module to export the results of datacollectors (model.datacollector) into files at the end of trials/simulations
+* A modular set of classes for environments/models. Now the ability to configure the agents through an agent definition and a topology through a network configuration is split into two classes (`soil.agents.BaseEnvironment` for agents, `soil.agents.NetworkEnvironment` to add topology).
+* FSM agents can now have generators as states. They work similar to normal states, with one caveat. Only `time` values can be yielded, not a state. This is because the state will not change, it will be resumed after the yield, at the appropriate time. The return value *can* be a state, or a `(state, time)` tuple, just like in normal states.
+### 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
+* Ability
+### 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

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.
+That means both global parameters, such as the probability of disease outbreak.
-The configuration file may specify the initial value of the environment parameters:
+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.
+Agents can be characterized with two variables: agent type (``agent_class``) 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.
+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 +1,54 @@
 ---
+version: '2'
 name: simple
 group: tests
 dir_path: "/tmp/"
 num_trials: 3
-max_time: 100
+max_steps: 100
 interval: 1
 seed: "CompleteSeed!"
-network_params:
+model_class: Environment
-  generator: complete_graph
+model_params:
-  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:
   am_i_complete: true
-default_state:
+  topology:
-  incidents: 0
+    params:
-states:
+      generator: complete_graph
-  - name: 'The first node'
+      n: 12
-  - name: 'The second node'
+  environment:
+  agents:
+    agent_class: CounterModel
+    topology: true
+    state:
+      times: 1
+      # In this group we are not specifying any topology
+    fixed:
+      - name: 'Environment Agent 1'
+        agent_class: BaseAgent
+        group: environment
+        topology: false
+        hidden: true
+        state:
+          times: 10
+      - agent_class: CounterModel
+        id: 0
+        group: fixed_counters
+        state:
+          times: 1
+          total: 0
+      - agent_class: CounterModel
+        group: fixed_counters
+        id: 1
+    distribution:
+      - agent_class: CounterModel
+        weight: 1
+        group: distro_counters
+        state:
+          times: 3
+      - agent_class: AggregatedCounter
+        weight: 0.2
+    override:
+      - 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

@@ -1,27 +1,22 @@
 from networkx import Graph
+import random
 import networkx as nx
-from random import choice
+
 
 def mygenerator(n=5, n_edges=5):
-    '''
+    """
     Just a simple generator that creates a network with n nodes and
     n_edges edges. Edges are assigned randomly, only avoiding self loops.
-    '''
+    """
     G = nx.Graph()
 
     for i in range(n):
         G.add_node(i)
-    
+
     for i in range(n_edges):
         nodes = list(G.nodes)
-        n_in = choice(nodes)
+        n_in = random.choice(nodes)
         nodes.remove(n_in)  # Avoid loops
-        n_out = choice(nodes)
+        n_out = random.choice(nodes)
         G.add_edge(n_in, n_out)
     return G
-    
-
-
-
-
-    

examples/custom_timeouts/custom_timeouts.py

@@ -2,34 +2,37 @@ from soil.agents import FSM, state, default_state
 
 
 class Fibonacci(FSM):
-    '''Agent that only executes in t_steps that are Fibonacci numbers'''
+    """Agent that only executes in t_steps that are Fibonacci numbers"""
 
-    defaults = {
+    defaults = {"prev": 1}
-        'prev': 1
-    }
 
     @default_state
     @state
     def counting(self):
-        self.log('Stopping at {}'.format(self.now))
+        self.log("Stopping at {}".format(self.now))
-        prev, self['prev'] = self['prev'], max([self.now, self['prev']])
+        prev, self["prev"] = self["prev"], max([self.now, self["prev"]])
         return None, self.env.timeout(prev)
 
+
 class Odds(FSM):
-    '''Agent that only executes in odd t_steps'''
+    """Agent that only executes in odd t_steps"""
+
     @default_state
     @state
     def odds(self):
-        self.log('Stopping at {}'.format(self.now))
+        self.log("Stopping at {}".format(self.now))
-        return None, self.env.timeout(1+self.now%2)
+        return None, self.env.timeout(1 + self.now % 2)
 
-if __name__ == '__main__':
+
-    import logging
+if __name__ == "__main__":
-    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_params={"generator": "complete_graph", "n": 2},
+        network_agents=[
-                   max_time=100,
+            {"ids": [0], "agent_class": Fibonacci},
-                   )
+            {"ids": [1], "agent_class": Odds},
+        ],
+        network_params={"generator": "complete_graph", "n": 2},
+        max_time=100,
+    )
     s.run(dry_run=True)

examples/events_and_messages/README.md

@@ -0,0 +1,7 @@
+This example can be run like with command-line options, like this:
+
+```bash
+python cars.py --level DEBUG  -e summary --csv
+```
+
+This will set the `CSV` (save the agent and model data to a CSV) and `summary` (print the a summary of the data to stdout) exporters, and set the log level to DEBUG.

examples/events_and_messages/cars.py

@@ -0,0 +1,243 @@
+"""
+This is an example of a simplified city, where there are Passengers and Drivers that can take those passengers
+from their location to their desired location.
+
+An example scenario could play like the following:
+
+- Drivers start in the `wandering` state, where they wander around the city until they have been assigned a journey
+- Passenger(1) tells every driver that it wants to request a Journey.
+- Each driver receives the request.
+  If Driver(2) is interested in providing the Journey, it asks Passenger(1) to confirm that it accepts Driver(2)'s request
+- When Passenger(1) accepts the request, two things happen:
+    - Passenger(1) changes its state to `driving_home`
+    - Driver(2) starts moving towards the origin of the Journey
+- Once Driver(2) reaches the origin, it starts moving itself and Passenger(1) to the destination of the Journey
+- When Driver(2) reaches the destination (carrying Passenger(1) along):
+    - Driver(2) starts wondering again
+    - Passenger(1) dies, and is removed from the simulation
+- If there are no more passengers available in the simulation, Drivers die
+"""
+from __future__ import annotations
+from soil import *
+from soil import events
+from mesa.space import MultiGrid
+
+
+# More complex scenarios may use more than one type of message  between objects.
+# A common pattern is to use `enum.Enum` to represent state changes in a request.
+@dataclass
+class Journey:
+    """
+    This represents a request for a journey. Passengers and drivers exchange this object.
+
+    A journey may have a driver assigned or not. If the driver has not been assigned, this
+    object is considered a "request for a journey".
+    """
+
+    origin: (int, int)
+    destination: (int, int)
+    tip: float
+
+    passenger: Passenger
+    driver: Driver = None
+
+
+class City(EventedEnvironment):
+    """
+    An environment with a grid where drivers and passengers will be placed.
+
+    The number of drivers and riders is configurable through its parameters:
+
+    :param str n_cars: The total number of drivers to add
+    :param str n_passengers: The number of passengers in the simulation
+    :param list agents: Specific agents to use in the simulation. It overrides the `n_passengers`
+    and `n_cars` params.
+    :param int height: Height of the internal grid
+    :param int width: Width of the internal grid
+    """
+
+    def __init__(
+        self,
+        *args,
+        n_cars=1,
+        n_passengers=10,
+        height=100,
+        width=100,
+        agents=None,
+        model_reporters=None,
+        **kwargs,
+    ):
+        self.grid = MultiGrid(width=width, height=height, torus=False)
+        if agents is None:
+            agents = []
+            for i in range(n_cars):
+                agents.append({"agent_class": Driver})
+            for i in range(n_passengers):
+                agents.append({"agent_class": Passenger})
+        model_reporters = model_reporters or {
+            "earnings": "total_earnings",
+            "n_passengers": "number_passengers",
+        }
+        print("REPORTERS", model_reporters)
+        super().__init__(
+            *args, agents=agents, model_reporters=model_reporters, **kwargs
+        )
+        for agent in self.agents:
+            self.grid.place_agent(agent, (0, 0))
+            self.grid.move_to_empty(agent)
+
+    @property
+    def total_earnings(self):
+        return sum(d.earnings for d in self.agents(agent_class=Driver))
+
+    @property
+    def number_passengers(self):
+        return self.count_agents(agent_class=Passenger)
+
+
+class Driver(Evented, FSM):
+    pos = None
+    journey = None
+    earnings = 0
+
+    def on_receive(self, msg, sender):
+        """This is not a state. It will run (and block) every time check_messages is invoked"""
+        if self.journey is None and isinstance(msg, Journey) and msg.driver is None:
+            msg.driver = self
+            self.journey = msg
+
+    def check_passengers(self):
+        """If there are no more passengers, stop forever"""
+        c = self.count_agents(agent_class=Passenger)
+        self.info(f"Passengers left {c}")
+        if not c:
+            self.die()
+
+    @default_state
+    @state
+    def wandering(self):
+        """Move around the city until a journey is accepted"""
+        target = None
+        self.check_passengers()
+        self.journey = None
+        while self.journey is None:  # No potential journeys detected (see on_receive)
+            if target is None or not self.move_towards(target):
+                target = self.random.choice(
+                    self.model.grid.get_neighborhood(self.pos, moore=False)
+                )
+
+            self.check_passengers()
+            # This will call on_receive behind the scenes, and the agent's status will be updated
+            self.check_messages()
+            yield Delta(30)  # Wait at least 30 seconds before checking again
+
+        try:
+            # Re-send the journey to the passenger, to confirm that we have been selected
+            self.journey = yield self.journey.passenger.ask(self.journey, timeout=60)
+        except events.TimedOut:
+            # No journey has been accepted. Try again
+            self.journey = None
+            return
+
+        return self.driving
+
+    @state
+    def driving(self):
+        """The journey has been accepted. Pick them up and take them to their destination"""
+        while self.move_towards(self.journey.origin):
+            yield
+        while self.move_towards(self.journey.destination, with_passenger=True):
+            yield
+        self.earnings += self.journey.tip
+        self.check_passengers()
+        return self.wandering
+
+    def move_towards(self, target, with_passenger=False):
+        """Move one cell at a time towards a target"""
+        self.info(f"Moving { self.pos } -> { target }")
+        if target[0] == self.pos[0] and target[1] == self.pos[1]:
+            return False
+
+        next_pos = [self.pos[0], self.pos[1]]
+        for idx in [0, 1]:
+            if self.pos[idx] < target[idx]:
+                next_pos[idx] += 1
+                break
+            if self.pos[idx] > target[idx]:
+                next_pos[idx] -= 1
+                break
+        self.model.grid.move_agent(self, tuple(next_pos))
+        if with_passenger:
+            self.journey.passenger.pos = (
+                self.pos
+            )  # This could be communicated through messages
+        return True
+
+
+class Passenger(Evented, FSM):
+    pos = None
+
+    def on_receive(self, msg, sender):
+        """This is not a state. It will be run synchronously every time `check_messages` is run"""
+
+        if isinstance(msg, Journey):
+            self.journey = msg
+            return msg
+
+    @default_state
+    @state
+    def asking(self):
+        destination = (
+            self.random.randint(0, self.model.grid.height),
+            self.random.randint(0, self.model.grid.width),
+        )
+        self.journey = None
+        journey = Journey(
+            origin=self.pos,
+            destination=destination,
+            tip=self.random.randint(10, 100),
+            passenger=self,
+        )
+
+        timeout = 60
+        expiration = self.now + timeout
+        self.model.broadcast(journey, ttl=timeout, sender=self, agent_class=Driver)
+        while not self.journey:
+            self.info(f"Passenger at: { self.pos }. Checking for responses.")
+            try:
+                # This will call check_messages behind the scenes, and the agent's status will be updated
+                # If you want to avoid that, you can call it with: check=False
+                yield self.received(expiration=expiration)
+            except events.TimedOut:
+                self.info(f"Passenger at: { self.pos }. Asking for journey.")
+                self.model.broadcast(
+                    journey, ttl=timeout, sender=self, agent_class=Driver
+                )
+                expiration = self.now + timeout
+        return self.driving_home
+
+    @state
+    def driving_home(self):
+        while (
+            self.pos[0] != self.journey.destination[0]
+            or self.pos[1] != self.journey.destination[1]
+        ):
+            try:
+                yield self.received(timeout=60)
+            except events.TimedOut:
+                pass
+
+        self.info("Got home safe!")
+        self.die()
+
+
+simulation = Simulation(
+    name="RideHailing",
+    model_class=City,
+    model_params={"n_passengers": 2},
+    seed="carsSeed",
+)
+
+if __name__ == "__main__":
+    with easy(simulation) as s:
+        s.run()

examples/mesa/mesa.yml

@@ -3,19 +3,17 @@ name: mesa_sim
 group: tests
 dir_path: "/tmp"
 num_trials: 3
-max_time: 100
+max_steps: 100
 interval: 1
 seed: '1'
-network_params:
+model_class: social_wealth.MoneyEnv
+model_params:
   generator: social_wealth.graph_generator
-  n: 5
+  agents:
-network_agents:
+    topology: true
-  - agent_type: social_wealth.SocialMoneyAgent
+    distribution:
-    weight: 1
+      - agent_class: social_wealth.SocialMoneyAgent
-environment_class: social_wealth.MoneyEnv
+        weight: 1
-environment_params:
-  num_mesa_agents: 5
-  mesa_agent_type: social_wealth.MoneyAgent
   N: 10
   width: 50
   height: 50

examples/mesa/server.py

@@ -2,6 +2,7 @@ from mesa.visualization.ModularVisualization import ModularServer
 from soil.visualization import UserSettableParameter
 from mesa.visualization.modules import ChartModule, NetworkModule, CanvasGrid
 from social_wealth import MoneyEnv, graph_generator, SocialMoneyAgent
+import networkx as nx
 
 
 class MyNetwork(NetworkModule):
@@ -13,15 +14,18 @@ def network_portrayal(env):
     # The model ensures there is 0 or 1 agent per node
 
     portrayal = dict()
+    wealths = {
+        node_id: data["agent"].wealth for (node_id, data) in env.G.nodes(data=True)
+    }
     portrayal["nodes"] = [
         {
-            "id": agent_id,
+            "id": node_id,
-            "size": env.get_agent(agent_id).wealth,
+            "size": 2 * (wealth + 1),
-            # "color": "#CC0000" if not agents or agents[0].wealth == 0 else "#007959",
+            "color": "#CC0000" if wealth == 0 else "#007959",
-            "color": "#CC0000",
+            # "color": "#CC0000",
-            "label": f"{agent_id}: {env.get_agent(agent_id).wealth}",
+            "label": f"{node_id}: {wealth}",
         }
-        for (agent_id) in env.G.nodes
+        for (node_id, wealth) in wealths.items()
     ]
 
     portrayal["edges"] = [
@@ -29,7 +33,6 @@ def network_portrayal(env):
         for edge_id, (source, target) in enumerate(env.G.edges)
     ]
 
-
     return portrayal
 
 
@@ -40,7 +43,7 @@ def gridPortrayal(agent):
     :param agent:  the agent in the simulation
     :return: the portrayal dictionary
     """
-    color = max(10, min(agent.wealth*10, 100))
+    color = max(10, min(agent.wealth * 10, 100))
     return {
         "Shape": "rect",
         "w": 1,
@@ -51,11 +54,11 @@ def gridPortrayal(agent):
         "Text": agent.unique_id,
         "x": agent.pos[0],
         "y": agent.pos[1],
-        "Color": f"rgba(31, 10, 255, 0.{color})"
+        "Color": f"rgba(31, 10, 255, 0.{color})",
     }
 
 
-grid = MyNetwork(network_portrayal, 500, 500, library="sigma")
+grid = MyNetwork(network_portrayal, 500, 500)
 chart = ChartModule(
     [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
 )
@@ -70,7 +73,6 @@ model_params = {
         1,
         description="Choose how many agents to include in the model",
     ),
-    "network_agents": [{"agent_type": SocialMoneyAgent}],
     "height": UserSettableParameter(
         "slider",
         "height",
@@ -79,7 +81,7 @@ model_params = {
         10,
         1,
         description="Grid height",
-        ),
+    ),
     "width": UserSettableParameter(
         "slider",
         "width",
@@ -88,13 +90,20 @@ model_params = {
         10,
         1,
         description="Grid width",
-        ),
+    ),
-    "network_params": {
+    "agent_class": UserSettableParameter(
-        'generator': graph_generator
+        "choice",
-    },
+        "Agent class",
+        value="MoneyAgent",
+        choices=["MoneyAgent", "SocialMoneyAgent"],
+    ),
+    "generator": graph_generator,
 }
 
-canvas_element = CanvasGrid(gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500)
+
+canvas_element = CanvasGrid(
+    gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500
+)
 
 
 server = ModularServer(

examples/mesa/social_wealth.py

@@ -1,23 +1,26 @@
-'''
+"""
 This is an example that adds soil agents and environment in a normal
 mesa workflow.
-'''
+"""
 from mesa import Agent as MesaAgent
 from mesa.space import MultiGrid
+
 # from mesa.time import RandomActivation
 from mesa.datacollection import DataCollector
 from mesa.batchrunner import BatchRunner
 
 import networkx as nx
 
-from soil import NetworkAgent, Environment
+from soil import NetworkAgent, Environment, serialization
+
 
 def compute_gini(model):
     agent_wealths = [agent.wealth for agent in model.agents]
     x = sorted(agent_wealths)
     N = len(list(model.agents))
-    B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
+    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
-    return (1 + (1/N) - 2*B)
+    return 1 + (1 / N) - 2 * B
+
 
 class MoneyAgent(MesaAgent):
     """
@@ -25,15 +28,14 @@ class MoneyAgent(MesaAgent):
     It will only share wealth with neighbors based on grid proximity
     """
 
-    def __init__(self, unique_id, model):
+    def __init__(self, unique_id, model, wealth=1):
         super().__init__(unique_id=unique_id, model=model)
-        self.wealth = 1
+        self.wealth = wealth
 
     def move(self):
         possible_steps = self.model.grid.get_neighborhood(
-            self.pos,
+            self.pos, moore=True, include_center=False
-            moore=True,
+        )
-            include_center=False)
         new_position = self.random.choice(possible_steps)
         self.model.grid.move_agent(self, new_position)
 
@@ -45,7 +47,7 @@ class MoneyAgent(MesaAgent):
             self.wealth -= 1
 
     def step(self):
-        self.info("Crying wolf", self.pos)
+        print("Crying wolf", self.pos)
         self.move()
         if self.wealth > 0:
             self.give_money()
@@ -56,10 +58,10 @@ class SocialMoneyAgent(NetworkAgent, MoneyAgent):
 
     def give_money(self):
         cellmates = set(self.model.grid.get_cell_list_contents([self.pos]))
-        friends = set(self.get_neighboring_agents())
+        friends = set(self.get_neighbors())
         self.info("Trying to give money")
-        self.debug("Cellmates: ", cellmates)
+        self.info("Cellmates: ", cellmates)
-        self.debug("Friends: ", friends)
+        self.info("Friends: ", friends)
 
         nearby_friends = list(cellmates & friends)
 
@@ -69,14 +71,35 @@ class SocialMoneyAgent(NetworkAgent, MoneyAgent):
             self.wealth -= 1
 
 
+def graph_generator(n=5):
+    G = nx.Graph()
+    for ix in range(n):
+        G.add_edge(0, ix)
+    return G
+
+
 class MoneyEnv(Environment):
     """A model with some number of agents."""
-    def __init__(self, N, width, height, *args, network_params, **kwargs):
 
-        network_params['n'] = N
+    def __init__(
-        super().__init__(*args, network_params=network_params, **kwargs)
+        self,
+        width,
+        height,
+        N,
+        generator=graph_generator,
+        agent_class=SocialMoneyAgent,
+        topology=None,
+        **kwargs
+    ):
+
+        generator = serialization.deserialize(generator)
+        agent_class = serialization.deserialize(agent_class, globs=globals())
+        topology = generator(n=N)
+        super().__init__(topology=topology, N=N, **kwargs)
         self.grid = MultiGrid(width, height, False)
 
+        self.populate_network(agent_class=agent_class)
+
         # Create agents
         for agent in self.agents:
             x = self.random.randrange(self.grid.width)
@@ -84,37 +107,31 @@ class MoneyEnv(Environment):
             self.grid.place_agent(agent, (x, y))
 
         self.datacollector = DataCollector(
-            model_reporters={"Gini": compute_gini},
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
-            agent_reporters={"Wealth": "wealth"})
+        )
 
 
-def graph_generator(n=5):
+if __name__ == "__main__":
-    G = nx.Graph()
-    for ix in range(n):
-        G.add_edge(0, ix)
-    return G
 
-if __name__ == '__main__':
+    fixed_params = {
-
+        "generator": nx.complete_graph,
-
+        "width": 10,
-    G = graph_generator()
+        "network_agents": [{"agent_class": SocialMoneyAgent, "weight": 1}],
-    fixed_params = {"topology": G,
+        "height": 10,
-                    "width": 10,
+    }
-                    "network_agents": [{"agent_type": SocialMoneyAgent,
-                                       'weight': 1}],
-                    "height": 10}
 
     variable_params = {"N": range(10, 100, 10)}
 
-    batch_run = BatchRunner(MoneyEnv,
+    batch_run = BatchRunner(
-                            variable_parameters=variable_params,
+        MoneyEnv,
-                            fixed_parameters=fixed_params,
+        variable_parameters=variable_params,
-                            iterations=5,
+        fixed_parameters=fixed_params,
-                            max_steps=100,
+        iterations=5,
-                            model_reporters={"Gini": compute_gini})
+        max_steps=100,
+        model_reporters={"Gini": compute_gini},
+    )
     batch_run.run_all()
 
     run_data = batch_run.get_model_vars_dataframe()
     run_data.head()
     print(run_data.Gini)
-

examples/mesa/wealth.py

@@ -4,24 +4,26 @@ from mesa.time import RandomActivation
 from mesa.datacollection import DataCollector
 from mesa.batchrunner import BatchRunner
 
+
 def compute_gini(model):
     agent_wealths = [agent.wealth for agent in model.schedule.agents]
     x = sorted(agent_wealths)
     N = model.num_agents
-    B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
+    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
-    return (1 + (1/N) - 2*B)
+    return 1 + (1 / N) - 2 * B
+
 
 class MoneyAgent(Agent):
-    """ An agent with fixed initial wealth."""
+    """An agent with fixed initial wealth."""
+
     def __init__(self, unique_id, model):
         super().__init__(unique_id, model)
         self.wealth = 1
 
     def move(self):
         possible_steps = self.model.grid.get_neighborhood(
-            self.pos,
+            self.pos, moore=True, include_center=False
-            moore=True,
+        )
-            include_center=False)
         new_position = self.random.choice(possible_steps)
         self.model.grid.move_agent(self, new_position)
 
@@ -37,8 +39,10 @@ class MoneyAgent(Agent):
         if self.wealth > 0:
             self.give_money()
 
+
 class MoneyModel(Model):
     """A model with some number of agents."""
+
     def __init__(self, N, width, height):
         self.num_agents = N
         self.grid = MultiGrid(width, height, True)
@@ -55,29 +59,29 @@ class MoneyModel(Model):
             self.grid.place_agent(a, (x, y))
 
         self.datacollector = DataCollector(
-            model_reporters={"Gini": compute_gini},
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
-            agent_reporters={"Wealth": "wealth"})
+        )
 
     def step(self):
         self.datacollector.collect(self)
         self.schedule.step()
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
-    fixed_params = {"width": 10,
+    fixed_params = {"width": 10, "height": 10}
-                    "height": 10}
     variable_params = {"N": range(10, 500, 10)}
 
-    batch_run = BatchRunner(MoneyModel,
+    batch_run = BatchRunner(
-                            variable_params,
+        MoneyModel,
-                            fixed_params,
+        variable_params,
-                            iterations=5,
+        fixed_params,
-                            max_steps=100,
+        iterations=5,
-                            model_reporters={"Gini": compute_gini})
+        max_steps=100,
+        model_reporters={"Gini": compute_gini},
+    )
     batch_run.run_all()
 
     run_data = batch_run.get_model_vars_dataframe()
     run_data.head()
     print(run_data.Gini)
-

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

@@ -1,79 +1,87 @@
-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.
-    '''
+    """
-    defaults = {
+
-        'prob_neighbor_spread': 0.5,
+    prob_neighbor_spread = 0.5
-        'prob_tv_spread': 0.1,
+    prob_tv_spread = 0.1
-    }
+    has_been_infected = False
 
     @default_state
     @state
     def neutral(self):
-        if self['has_tv']:
+        if self["has_tv"]:
-            if prob(self.env['prob_tv_spread']):
+            if self.prob(self.model["prob_tv_spread"]):
-                self.set_state(self.infected)
+                return self.infected
+        if self.has_been_infected:
+            return self.infected
 
     @state
     def infected(self):
-        for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
+        for neighbor in self.get_neighbors(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):
-        self.set_state(self.infected)
+        """
+        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.has_been_infected = True
 
 
 class HerdViewer(DumbViewer):
-    '''
+    """
     A viewer whose probability of infection depends on the state of its neighbors.
-    '''
+    """
 
     def infect(self):
-        infected = self.count_neighboring_agents(state_id=self.infected.id)
+        """Notice again that this is NOT a state. See DumbViewer.infect for reference"""
-        total = self.count_neighboring_agents()
+        infected = self.count_neighbors(state_id=self.infected.id)
-        prob_infect = self.env['prob_neighbor_spread'] * infected/total
+        total = self.count_neighbors()
-        self.debug('prob_infect', prob_infect)
+        prob_infect = self.model["prob_neighbor_spread"] * infected / total
-        if prob(prob_infect):
+        self.debug("prob_infect", prob_infect)
-            self.set_state(self.infected.id)
+        if self.prob(prob_infect):
+            self.has_been_infected = True
 
 
 class WiseViewer(HerdViewer):
-    '''
+    """
     A viewer that can change its mind.
-    '''
+    """
 
     defaults = {
-        'prob_neighbor_spread': 0.5,
+        "prob_neighbor_spread": 0.5,
-        'prob_neighbor_cure': 0.25,
+        "prob_neighbor_cure": 0.25,
-        'prob_tv_spread': 0.1,
+        "prob_tv_spread": 0.1,
     }
 
     @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):
+        for neighbor in self.get_neighbors(state_id=self.infected.id):
-            if prob(prob_cure):
+            if self.prob(prob_cure):
                 try:
                     neighbor.cure()
                 except AttributeError:
-                    self.debug('Viewer {} cannot be cured'.format(neighbor.id))
+                    self.debug("Viewer {} cannot be cured".format(neighbor.id))
 
     def cure(self):
-        self.set_state(self.cured.id)
+        self.has_been_cured = True
 
     @state
     def infected(self):
-        cured = max(self.count_neighboring_agents(self.cured.id),
+        if self.has_been_cured:
-                    1.0)
+            return self.cured
-        infected = max(self.count_neighboring_agents(self.infected.id),
+        cured = max(self.count_neighbors(self.cured.id), 1.0)
-                       1.0)
+        infected = max(self.count_neighbors(self.infected.id), 1.0)
-        prob_cure = self.env['prob_neighbor_cure'] * (cured/infected)
+        prob_cure = self.model["prob_neighbor_cure"] * (cured / infected)
-        if prob(prob_cure):
+        if self.prob(prob_cure):
-            return self.cure()
+            return self.cured
-        return self.set_state(super().infected)

examples/programmatic/programmatic.py

@@ -1,6 +1,6 @@
-'''
+"""
 Example of a fully programmatic simulation, without definition files.
-'''
+"""
 from soil import Simulation, agents
 from networkx import Graph
 import logging
@@ -14,25 +14,28 @@ def mygenerator():
 
 
 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',
+s = Simulation(
-               network_params={'generator': mygenerator},
+    name="Programmatic",
-               num_trials=1,
+    network_params={"generator": mygenerator},
-               max_time=100,
+    num_trials=1,
-               agent_type=MyAgent,
+    max_time=100,
-               dry_run=True)
+    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()
+# Uncomment this to output the simulation to a YAML file
-
+# s.dump_yaml('simulation.yaml')
-for env in envs:
-    env.dump_csv()

examples/pubcrawl/pubcrawl.py

@@ -1,12 +1,12 @@
-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
 
 
 class CityPubs(Environment):
-    '''Environment with Pubs'''
+    """Environment with Pubs"""
+
     level = logging.INFO
 
     def __init__(self, *args, number_of_pubs=3, pub_capacity=10, **kwargs):
@@ -14,70 +14,70 @@ class CityPubs(Environment):
         pubs = {}
         for i in range(number_of_pubs):
             newpub = {
-                'name': 'The awesome pub #{}'.format(i),
+                "name": "The awesome pub #{}".format(i),
-                'open': True,
+                "open": True,
-                'capacity': pub_capacity,
+                "capacity": pub_capacity,
-                'occupancy': 0,
+                "occupancy": 0,
             }
-            pubs[newpub['name']] = newpub
+            pubs[newpub["name"]] = newpub
-        self['pubs'] = pubs
+        self["pubs"] = pubs
 
     def enter(self, pub_id, *nodes):
-        '''Agents will try to enter. The pub checks if it is possible'''
+        """Agents will try to enter. The pub checks if it is possible"""
         try:
-            pub = self['pubs'][pub_id]
+            pub = self["pubs"][pub_id]
         except KeyError:
-            raise ValueError('Pub {} is not available'.format(pub_id))
+            raise ValueError("Pub {} is not available".format(pub_id))
-        if not pub['open'] or (pub['capacity'] < (len(nodes) + pub['occupancy'])):
+        if not pub["open"] or (pub["capacity"] < (len(nodes) + pub["occupancy"])):
             return False
-        pub['occupancy'] += len(nodes)
+        pub["occupancy"] += len(nodes)
         for node in nodes:
-            node['pub'] = pub_id
+            node["pub"] = pub_id
         return True
 
     def available_pubs(self):
-        for pub in self['pubs'].values():
+        for pub in self["pubs"].values():
-            if pub['open'] and (pub['occupancy'] < pub['capacity']):
+            if pub["open"] and (pub["occupancy"] < pub["capacity"]):
-                yield pub['name']
+                yield pub["name"]
 
     def exit(self, pub_id, *node_ids):
-        '''Agents will notify the pub they want to leave'''
+        """Agents will notify the pub they want to leave"""
         try:
-            pub = self['pubs'][pub_id]
+            pub = self["pubs"][pub_id]
         except KeyError:
-            raise ValueError('Pub {} is not available'.format(pub_id))
+            raise ValueError("Pub {} is not available".format(pub_id))
         for node_id in node_ids:
             node = self.get_agent(node_id)
-            if pub_id == node['pub']:
+            if pub_id == node["pub"]:
-                del node['pub']
+                del node["pub"]
-                pub['occupancy'] -= 1
+                pub["occupancy"] -= 1
 
 
-class Patron(FSM):
+class Patron(FSM, NetworkAgent):
-    '''Agent that looks for friends to drink with. It will do three things:
+    """Agent that looks for friends to drink with. It will do three things:
-        1) Look for other patrons to drink with
+    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.
+    2) Look for a bar where the agent and other agents in the same group can get in.
-        3) While in the bar, patrons only drink, until they get drunk and taken home.
+    3) While in the bar, patrons only drink, until they get drunk and taken home.
-    '''
+    """
+
     level = logging.DEBUG
 
-    defaults = {
+    pub = None
-        'pub': None,
+    drunk = False
-        'drunk': False,
+    pints = 0
-        'pints': 0,
+    max_pints = 3
-        'max_pints': 3,
+    kicked_out = False
-    }
 
     @default_state
     @state
     def looking_for_friends(self):
-        '''Look for friends to drink with'''
+        """Look for friends to drink with"""
-        self.info('I am looking for friends')
+        self.info("I am looking for friends")
-        available_friends = list(self.get_agents(drunk=False,
+        available_friends = list(
-                                                 pub=None,
+            self.get_agents(drunk=False, pub=None, state_id=self.looking_for_friends.id)
-                                                 state_id=self.looking_for_friends.id))
+        )
         if not available_friends:
-            self.info('Life sucks and I\'m alone!')
+            self.info("Life sucks and I'm alone!")
             return self.at_home
         befriended = self.try_friends(available_friends)
         if befriended:
@@ -85,91 +85,91 @@ class Patron(FSM):
 
     @state
     def looking_for_pub(self):
-        '''Look for a pub that accepts me and my friends'''
+        """Look for a pub that accepts me and my friends"""
-        if self['pub'] != None:
+        if self["pub"] != None:
             return self.sober_in_pub
-        self.debug('I am looking for a pub')
+        self.debug("I am looking for a pub")
-        group = list(self.get_neighboring_agents())
+        group = list(self.get_neighbors())
-        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)))
+            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))
+                self.info("We're all {} getting in {}!".format(len(group), pub))
                 return self.sober_in_pub
 
     @state
     def sober_in_pub(self):
-        '''Drink up.'''
+        """Drink up."""
         self.drink()
-        if self['pints'] > self['max_pints']:
+        if self["pints"] > self["max_pints"]:
             return self.drunk_in_pub
 
     @state
     def drunk_in_pub(self):
-        '''I'm out. Take me home!'''
+        """I'm out. Take me home!"""
-        self.info('I\'m so drunk. Take me home!')
+        self.info("I'm so drunk. Take me home!")
-        self['drunk'] = True
+        self["drunk"] = True
-        pass  # out drunk
+        if self.kicked_out:
+            return self.at_home
+        pass  # out drun
 
     @state
     def at_home(self):
-        '''The end'''
+        """The end"""
         others = self.get_agents(state_id=Patron.at_home.id, limit_neighbors=True)
-        self.debug('I\'m home. Just like {} of my friends'.format(len(others)))
+        self.debug("I'm home. Just like {} of my friends".format(len(others)))
-    
+
     def drink(self):
-        self['pints'] += 1
+        self["pints"] += 1
-        self.debug('Cheers to that')
+        self.debug("Cheers to that")
-    
+
     def kick_out(self):
-        self.set_state(self.at_home)
+        self.kicked_out = True
 
     def befriend(self, other_agent, force=False):
-        '''
+        """
         Try to become friends with another agent. The chances of
         success depend on both agents' openness.
-        '''
+        """
-        if force or self['openness'] > random():
+        if force or self["openness"] > self.random.random():
-            self.env.add_edge(self, other_agent)
+            self.add_edge(self, other_agent)
-            self.info('Made some friend {}'.format(other_agent))
+            self.info("Made some friend {}".format(other_agent))
             return True
         return False
 
     def try_friends(self, others):
-        ''' Look for random agents around me and try to befriend them'''
+        """Look for random agents around me and try to befriend them"""
         befriended = False
-        k = int(10*self['openness'])
+        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
             if friend.befriend(self):
                 self.befriend(friend, force=True)
-                self.debug('Hooray! new friend: {}'.format(friend.id))
+                self.debug("Hooray! new friend: {}".format(friend.id))
                 befriended = True
             else:
-                self.debug('{} does not want to be friends'.format(friend.id))
+                self.debug("{} does not want to be friends".format(friend.id))
         return befriended
 
 
 class Police(FSM):
-    '''Simple agent to take drunk people out of pubs.'''
+    """Simple agent to take drunk people out of pubs."""
+
     level = logging.INFO
 
     @default_state
     @state
     def patrol(self):
-        drunksters = list(self.get_agents(drunk=True,
+        drunksters = list(self.get_agents(drunk=True, state_id=Patron.drunk_in_pub.id))
-                                          state_id=Patron.drunk_in_pub.id))
         for drunk in drunksters:
-            self.info('Kicking out the trash: {}'.format(drunk.id))
+            self.info("Kicking out the trash: {}".format(drunk.id))
             drunk.kick_out()
         else:
-            self.info('No trash to take out. Too bad.')
+            self.info("No trash to take out. Too bad.")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     from soil import simulation
-    simulation.run_from_config('pubcrawl.yml',
+
-                               dry_run=True,
+    simulation.run_from_config("pubcrawl.yml", dry_run=True, dump=None, parallel=False)
-                               dump=None,
-                               parallel=False)

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 +1,14 @@
+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.
+
+The examples can be run directly in the terminal, and they accept command like arguments.
+For example, to enable the CSV exporter and the Summary exporter, while setting `max_time` to `100` and `seed` to `CustomSeed`:
+
+```
+python rabbit_agents.py  --set max_time=100 --csv -e summary  --set 'seed="CustomSeed"'
+```
+
+To learn more about how this functionality works, check out the `soil.easy` function.
+

examples/rabbits/basic/rabbit_agents.py

@@ -0,0 +1,150 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+from collections import Counter
+import logging
+import math
+
+
+class RabbitEnv(Environment):
+    @property
+    def num_rabbits(self):
+        return self.count_agents(agent_class=Rabbit)
+
+    @property
+    def num_males(self):
+        return self.count_agents(agent_class=Male)
+
+    @property
+    def num_females(self):
+        return self.count_agents(agent_class=Female)
+
+
+class Rabbit(NetworkAgent, FSM):
+
+    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(Rabbit):
+    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(Rabbit):
+    gestation = 10
+    pregnancy = -1
+
+    @state
+    def fertile(self):
+        # Just wait for a Male
+        self.age += 1
+        if self.age > self.life_expectancy:
+            return self.dead
+        if self.pregnancy >= 0:
+            return self.pregnant
+
+    def impregnate(self, male):
+        self.info(f"impregnated by {repr(male)}")
+        self.mate = male
+        self.pregnancy = 0
+        self.number_of_babies = int(8 + 4 * self.random.random())
+
+    @state
+    def pregnant(self):
+        self.info("I am pregnant")
+        self.age += 1
+
+        if self.age >= self.life_expectancy:
+            return self.die()
+
+        if self.pregnancy < self.gestation:
+            self.pregnancy += 1
+            return
+
+        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, **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
+        self.pregnancy = -1
+        return self.fertile
+
+    def die(self):
+        if "pregnancy" in self and self["pregnancy"] > -1:
+            self.info("A mother has died carrying a baby!!")
+        return super().die()
+
+
+class RandomAccident(BaseAgent):
+    def step(self):
+        rabbits_alive = self.model.G.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.get_agents(agent_class=Rabbit):
+            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.die()
+        self.debug("Rabbits alive: {}".format(rabbits_alive))
+
+
+if __name__ == "__main__":
+    from soil import easy
+
+    with easy("rabbits.yml") as sim:
+        sim.run()

examples/rabbits/basic/rabbits.yml

@@ -0,0 +1,42 @@
+---
+version: '2'
+name: rabbits_basic
+num_trials: 1
+seed: MySeed
+description: null
+group: null
+interval: 1.0
+max_time: 100
+model_class: rabbit_agents.RabbitEnv
+model_params:
+  agents:
+    topology: true
+    distribution:
+    - agent_class: rabbit_agents.Male
+      weight: 1
+    - agent_class: rabbit_agents.Female
+      weight: 1
+    fixed:
+    - agent_class: rabbit_agents.RandomAccident
+      topology: false
+      hidden: true
+      state:
+        group: environment
+    state:
+      group: network
+      mating_prob: 0.1
+  prob_death: 0.001
+  topology:
+    fixed:
+      directed: true
+      links: []
+      nodes:
+      - id: 1
+      - id: 0
+  model_reporters:
+    num_males: 'num_males'
+    num_females: 'num_females'
+    num_rabbits: |
+      py:lambda env: env.num_males + env.num_females
+extra:
+  visualization_params: {}

examples/rabbits/improved/rabbit_agents.py

@@ -0,0 +1,157 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+from soil.time import Delta
+from enum import Enum
+from collections import Counter
+import logging
+import math
+
+
+class RabbitEnv(Environment):
+    @property
+    def num_rabbits(self):
+        return self.count_agents(agent_class=Rabbit)
+
+    @property
+    def num_males(self):
+        return self.count_agents(agent_class=Male)
+
+    @property
+    def num_females(self):
+        return self.count_agents(agent_class=Female)
+
+
+class Rabbit(FSM, NetworkAgent):
+
+    sexual_maturity = 30
+    life_expectancy = 300
+    birth = None
+
+    @property
+    def age(self):
+        if self.birth is None:
+            return None
+        return self.now - self.birth
+
+    @default_state
+    @state
+    def newborn(self):
+        self.info("I am a newborn.")
+        self.birth = self.now
+        self.offspring = 0
+        return self.youngling, Delta(self.sexual_maturity - self.age)
+
+    @state
+    def youngling(self):
+        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(Rabbit):
+    max_females = 5
+    mating_prob = 0.001
+
+    @state
+    def fertile(self):
+        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  # Do not try to impregnate other females
+
+
+class Female(Rabbit):
+    gestation = 10
+    conception = None
+
+    @state
+    def fertile(self):
+        # Just wait for a Male
+        if self.age > self.life_expectancy:
+            return self.dead
+        if self.conception is not None:
+            return self.pregnant
+
+    @property
+    def pregnancy(self):
+        if self.conception is None:
+            return None
+        return self.now - self.conception
+
+    def impregnate(self, male):
+        self.info(f"impregnated by {repr(male)}")
+        self.mate = male
+        self.conception = self.now
+        self.number_of_babies = int(8 + 4 * self.random.random())
+
+    @state
+    def pregnant(self):
+        self.debug("I am pregnant")
+
+        if self.age > self.life_expectancy:
+            self.info("Dying before giving birth")
+            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, **state)
+                child.add_edge(self)
+                if self.mate:
+                    child.add_edge(self.mate)
+                    self.mate.offspring += 1
+                else:
+                    self.debug("The father has passed away")
+
+                self.offspring += 1
+            self.mate = None
+            return self.fertile
+
+    def die(self):
+        if self.pregnancy is not None:
+            self.info("A mother has died carrying a baby!!")
+        return super().die()
+
+
+class RandomAccident(BaseAgent):
+    def step(self):
+        rabbits_alive = self.model.G.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=Rabbit):
+            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.die()
+        self.debug("Rabbits alive: {}".format(rabbits_alive))
+
+
+if __name__ == "__main__":
+    from soil import easy
+
+    with easy("rabbits.yml") as sim:
+        sim.run()

examples/rabbits/improved/rabbits.yml

@@ -0,0 +1,42 @@
+---
+version: '2'
+name: rabbits_improved
+num_trials: 1
+seed: MySeed
+description: null
+group: null
+interval: 1.0
+max_time: 100
+model_class: rabbit_agents.RabbitEnv
+model_params:
+  agents:
+    topology: true
+    distribution:
+    - agent_class: rabbit_agents.Male
+      weight: 1
+    - agent_class: rabbit_agents.Female
+      weight: 1
+    fixed:
+    - agent_class: rabbit_agents.RandomAccident
+      topology: false
+      hidden: true
+      state:
+        group: environment
+    state:
+      group: network
+      mating_prob: 0.1
+  prob_death: 0.001
+  topology:
+    fixed:
+      directed: true
+      links: []
+      nodes:
+      - id: 1
+      - id: 0
+  model_reporters:
+    num_males: 'num_males'
+    num_females: 'num_females'
+    num_rabbits: |
+      py:lambda env: env.num_males + env.num_females
+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

@@ -1,45 +1,43 @@
-'''
+"""
 Example of setting a 
 Example of a fully programmatic simulation, without definition files.
-'''
+"""
 from soil import Simulation, agents
 from soil.time import Delta
-from random import expovariate
-import logging
-
 
 
 class MyAgent(agents.FSM):
-    '''
+    """
     An agent that first does a ping
-    '''
+    """
 
-    defaults = {'pong_counts': 2}
+    defaults = {"pong_counts": 2}
 
     @agents.default_state
     @agents.state
     def ping(self):
-        self.info('Ping')
+        self.info("Ping")
-        return self.pong, Delta(expovariate(1/16))
+        return self.pong, Delta(self.random.expovariate(1 / 16))
 
     @agents.state
     def pong(self):
-        self.info('Pong')
+        self.info("Pong")
         self.pong_counts -= 1
         self.info(str(self.pong_counts))
         if self.pong_counts < 1:
             return self.die()
-        return None, Delta(expovariate(1/16))
+        return None, Delta(self.random.expovariate(1 / 16))
 
 
-s = Simulation(name='Programmatic',
+s = Simulation(
-               network_agents=[{'agent_type': MyAgent, 'id': 0}],
+    name="Programmatic",
-               topology={'nodes': [{'id': 0}], 'links': []},
+    network_agents=[{"agent_class": MyAgent, "id": 0}],
-               num_trials=1,
+    topology={"nodes": [{"id": 0}], "links": []},
-               max_time=100,
+    num_trials=1,
-               agent_type=MyAgent,
+    max_time=100,
-               dry_run=True)
+    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:
+  model_params:
-    generator: complete_graph
+    network_params:
-    n: 10
+      generator: complete_graph
-  network_agents:
+      n: 10
-    - agent_type: CounterModel
+    network_agents:
-      weight: "{{ x1 }}"
+      - agent_class: CounterModel
-      state:
+        weight: "{{ x1 }}"
-        state_id: 0
+        state:
-    - agent_type: AggregatedCounter
+          state_id: 0
-      weight: "{{ 1 - x1 }}"
+      - agent_class: AggregatedCounter
-  environment_params:
+        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
@@ -21,56 +20,83 @@ class TerroristSpreadModel(FSM, Geo):
     def __init__(self, model=None, unique_id=0, state=()):
         super().__init__(model=model, unique_id=unique_id, state=state)
 
-        self.information_spread_intensity = model.environment_params['information_spread_intensity']
+        self.information_spread_intensity = model.environment_params[
-        self.terrorist_additional_influence = model.environment_params['terrorist_additional_influence']
+            "information_spread_intensity"
-        self.prob_interaction = model.environment_params['prob_interaction']
+        ]
+        self.terrorist_additional_influence = model.environment_params[
+            "terrorist_additional_influence"
+        ]
+        self.prob_interaction = model.environment_params["prob_interaction"]
 
-        if self['id'] == self.civilian.id:       # Civilian
+        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
+        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
+        elif self["id"] == self.leader.id:  # Leader
             self.mean_belief = 1.00
         else:
-            raise Exception('Invalid state id: {}'.format(self['id']))
+            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'] )
-        else :
-            self.vulnerability = random.uniform( 0, model.environment_params['max_vulnerability'] )
 
+        if "min_vulnerability" in model.environment_params:
+            self.vulnerability = self.random.uniform(
+                model.environment_params["min_vulnerability"],
+                model.environment_params["max_vulnerability"],
+            )
+        else:
+            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_neighbors(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(
-            influence = sum( self.degree(i) for i in interactions )
+                n for n in neighbours if self.random.random() <= self.prob_interaction
-            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 )
+            influence = sum(self.degree(i) for i in interactions)
-            self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
+            mean_belief = sum(
-        
+                i.mean_belief * self.degree(i) / influence for i in interactions
+            )
+            mean_belief = (
+                mean_belief * self.information_spread_intensity
+                + self.mean_belief * (1 - self.information_spread_intensity)
+            )
+            self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * (
+                1 - self.vulnerability
+            )
+
         if self.mean_belief >= 0.8:
             return self.terrorist
 
     @state
     def leader(self):
-        self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
+        self.mean_belief = self.mean_belief ** (1 - self.terrorist_additional_influence)
-        for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
+        for neighbour in self.get_neighbors(
+            state_id=[self.terrorist.id, self.leader.id]
+        ):
             if self.betweenness(neighbour) > self.betweenness(self):
                 return self.terrorist
 
     @state
     def terrorist(self):
-        neighbours = self.get_agents(state_id=[self.terrorist.id, self.leader.id],
+        neighbours = self.get_agents(
-                                     agent_type=TerroristSpreadModel,
+            state_id=[self.terrorist.id, self.leader.id],
-                                     limit_neighbors=True)
+            agent_class=TerroristSpreadModel,
+            limit_neighbors=True,
+        )
         if len(neighbours) > 0:
-            influence = sum( self.degree(n) for n in neighbours )
+            influence = sum(self.degree(n) for n in neighbours)
-            mean_belief = sum( n.mean_belief * self.degree(n) / influence for n in neighbours )
+            mean_belief = sum(
-            mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
+                n.mean_belief * self.degree(n) / influence for n in neighbours
-            self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
+            )
+            mean_belief = mean_belief * self.vulnerability + self.mean_belief * (
+                1 - self.vulnerability
+            )
+            self.mean_belief = self.mean_belief ** (
+                1 - self.terrorist_additional_influence
+            )
 
         # Check if there are any leaders in the group
         leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
@@ -82,6 +108,34 @@ 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):
     """
@@ -95,17 +149,20 @@ class TrainingAreaModel(FSM, Geo):
 
     def __init__(self, model=None, unique_id=0, state=()):
         super().__init__(model=model, unique_id=unique_id, state=state)
-        self.training_influence = model.environment_params['training_influence']
+        self.training_influence = model.environment_params["training_influence"]
-        if 'min_vulnerability' in model.environment_params:
+        if "min_vulnerability" in model.environment_params:
-            self.min_vulnerability = model.environment_params['min_vulnerability']
+            self.min_vulnerability = model.environment_params["min_vulnerability"]
-        else: self.min_vulnerability = 0
+        else:
+            self.min_vulnerability = 0
 
     @default_state
     @state
     def terrorist(self):
-        for neighbour in self.get_neighboring_agents(agent_type=TerroristSpreadModel):
+        for neighbour in self.get_neighbors(agent_class=TerroristSpreadModel):
             if neighbour.vulnerability > self.min_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )        
+                neighbour.vulnerability = neighbour.vulnerability ** (
+                    1 - self.training_influence
+                )
 
 
 class HavenModel(FSM, Geo):
@@ -122,14 +179,15 @@ class HavenModel(FSM, Geo):
 
     def __init__(self, model=None, unique_id=0, state=()):
         super().__init__(model=model, unique_id=unique_id, state=state)
-        self.haven_influence = model.environment_params['haven_influence']
+        self.haven_influence = model.environment_params["haven_influence"]
-        if 'min_vulnerability' in model.environment_params:
+        if "min_vulnerability" in model.environment_params:
-            self.min_vulnerability = model.environment_params['min_vulnerability']
+            self.min_vulnerability = model.environment_params["min_vulnerability"]
-        else: self.min_vulnerability = 0
+        else:
-        self.max_vulnerability = model.environment_params['max_vulnerability']
+            self.min_vulnerability = 0
+        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_neighbors(agent_class=TerroristSpreadModel, **kwargs)
 
     @state
     def civilian(self):
@@ -139,14 +197,18 @@ class HavenModel(FSM, Geo):
 
         for neighbour in self.get_occupants():
             if neighbour.vulnerability > self.min_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
+                neighbour.vulnerability = neighbour.vulnerability * (
+                    1 - self.haven_influence
+                )
         return self.civilian
 
     @state
     def terrorist(self):
         for neighbour in self.get_occupants():
             if neighbour.vulnerability < self.max_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
+                neighbour.vulnerability = neighbour.vulnerability ** (
+                    1 - self.haven_influence
+                )
         return self.terrorist
 
 
@@ -165,10 +227,10 @@ class TerroristNetworkModel(TerroristSpreadModel):
     def __init__(self, model=None, unique_id=0, state=()):
         super().__init__(model=model, unique_id=unique_id, state=state)
 
-        self.vision_range = model.environment_params['vision_range']
+        self.vision_range = model.environment_params["vision_range"]
-        self.sphere_influence = model.environment_params['sphere_influence']
+        self.sphere_influence = model.environment_params["sphere_influence"]
-        self.weight_social_distance = model.environment_params['weight_social_distance']
+        self.weight_social_distance = model.environment_params["weight_social_distance"]
-        self.weight_link_distance = model.environment_params['weight_link_distance']
+        self.weight_link_distance = model.environment_params["weight_link_distance"]
 
     @state
     def terrorist(self):
@@ -181,28 +243,47 @@ class TerroristNetworkModel(TerroristSpreadModel):
         return super().leader()
 
     def update_relationships(self):
-        if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
+        if self.count_neighbors(state_id=self.civilian.id) == 0:
-            close_ups = set(self.geo_search(radius=self.vision_range, agent_type=TerroristNetworkModel))
+            close_ups = set(
-            step_neighbours = set(self.ego_search(self.sphere_influence, agent_type=TerroristNetworkModel, center=False))
+                self.geo_search(
-            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_type=TerroristNetworkModel))
+                    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_neighbors(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) )
+                spatial_proximity = 1 - self.get_distance(agent.id)
-                prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
+                prob_new_interaction = (
-                if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
+                    self.weight_social_distance * social_distance
+                    + self.weight_link_distance * spatial_proximity
+                )
+                if (
+                    agent["id"] == agent.civilian.id
+                    and self.random.random() < prob_new_interaction
+                ):
                     self.add_edge(agent)
                     break
 
     def get_distance(self, target):
-        source_x, source_y = nx.get_node_attributes(self.topology, 'pos')[self.id]
+        source_x, source_y = nx.get_node_attributes(self.G, "pos")[self.id]
-        target_x, target_y = nx.get_node_attributes(self.topology, 'pos')[target]
+        target_x, target_y = nx.get_node_attributes(self.G, "pos")[target]
-        dx = abs( source_x - target_x )
+        dx = abs(source_x - target_x)
-        dy = abs( source_y - target_y )
+        dy = abs(source_y - target_y)
-        return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
+        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')
+            return float("inf")

examples/terrorism/TerroristNetworkModel.yml

@@ -1,32 +1,31 @@
 name: TerroristNetworkModel_sim
-load_module: TerroristNetworkModel
+max_steps: 150 
-max_time: 150 
 num_trials: 1
-network_params:
+model_params:
-  generator: random_geometric_graph
+  network_params:
-  radius: 0.2
+    generator: random_geometric_graph
-  # generator: geographical_threshold_graph
+    radius: 0.2
-  # theta: 20
+    # generator: geographical_threshold_graph
-  n: 100
+    # theta: 20
-network_agents:
+    n: 100
-  - agent_type: TerroristNetworkModel
+  network_agents:
-    weight: 0.8
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
-    state:
+      weight: 0.8
-      id: civilian  # Civilians
+      state:
-  - agent_type: TerroristNetworkModel
+        id: civilian  # Civilians
-    weight: 0.1
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
-    state:
+      weight: 0.1
-      id: leader    # Leaders
+      state:
-  - agent_type: TrainingAreaModel
+        id: leader    # Leaders
-    weight: 0.05
+    - agent_class: TerroristNetworkModel.TrainingAreaModel
-    state:
+      weight: 0.05
-      id: terrorist # Terrorism
+      state:
-  - agent_type: HavenModel
+        id: terrorist # Terrorism
-    weight: 0.05
+    - agent_class: TerroristNetworkModel.HavenModel
-    state:
+      weight: 0.05
-      id: civilian  # Civilian
+      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
+model_params:
-default_state:
+  agent_class: CounterModel
-  skill_level: 'beginner'
+  default_state:
-network_params:
+    skill_level: 'beginner'
-  path: 'torvalds.edgelist'
+  network_params:
-states:
+    path: 'torvalds.edgelist'
-  Torvalds:
+  states:
-    skill_level: 'God'
+    Torvalds:
-  balkian:
+      skill_level: 'God'
-    skill_level: 'developer'
+    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
+Mesa>=1.1
-tsih>=0.1.5
+pydantic>=1.9
+sqlalchemy>=1.4

setup.py

@@ -49,9 +49,10 @@ 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 = soil.__init__:main',
+        ['soil = soil.__main__:main',
         'soil-web = soil.web.__init__:main']
     })

soil/VERSION

@@ -1 +1 @@
-0.20.1
+0.30.0rc3

soil/__init__.py

@@ -1,8 +1,11 @@
+from __future__ import annotations
+
 import importlib
 import sys
 import os
-import pdb
 import logging
+import traceback
+from contextlib import contextmanager
 
 from .version import __version__
 
@@ -14,82 +17,235 @@ except NameError:
 from .agents import *
 from . import agents
 from .simulation import *
-from .environment import Environment
+from .environment import Environment, EventedEnvironment
 from . import serialization
-from . import analysis
 from .utils import logger
 from .time import *
 
-def main():
+
+def main(
+    cfg="simulation.yml",
+    exporters=None,
+    parallel=None,
+    output="soil_output",
+    *,
+    do_run=False,
+    debug=False,
+    pdb=False,
+    **kwargs,
+):
+
+    if isinstance(cfg, Simulation):
+        sim = cfg
     import argparse
     from . import simulation
 
-    logger.info('Running SOIL version: {}'.format(__version__))
+    logger.info("Running SOIL version: {}".format(__version__))
 
-    parser = argparse.ArgumentParser(description='Run a SOIL simulation')
+    parser = argparse.ArgumentParser(description="Run a SOIL simulation")
-    parser.add_argument('file', type=str,
+    parser.add_argument(
-                        nargs="?",
+        "file",
-                        default='simulation.yml',
+        type=str,
-                        help='Configuration file for the simulation (e.g., YAML or JSON)')
+        nargs="?",
-    parser.add_argument('--version', action='store_true',
+        default=cfg if sim is None else "",
-                        help='Show version info and exit')
+        help="Configuration file for the simulation (e.g., YAML or JSON)",
-    parser.add_argument('--module', '-m', type=str,
+    )
-                        help='file containing the code of any custom agents.')
+    parser.add_argument(
-    parser.add_argument('--dry-run', '--dry', action='store_true',
+        "--version", action="store_true", help="Show version info and exit"
-                        help='Do not store the results of the simulation.')
+    )
-    parser.add_argument('--pdb', action='store_true',
+    parser.add_argument(
-                        help='Use a pdb console in case of exception.')
+        "--module",
-    parser.add_argument('--graph', '-g', action='store_true',
+        "-m",
-                        help='Dump GEXF graph. Defaults to false.')
+        type=str,
-    parser.add_argument('--csv', action='store_true',
+        help="file containing the code of any custom agents.",
-                        help='Dump history in CSV format. Defaults to false.')
+    )
-    parser.add_argument('--level', type=str,
+    parser.add_argument(
-                        help='Logging level')
+        "--dry-run",
-    parser.add_argument('--output', '-o', type=str, default="soil_output",
+        "--dry",
-                        help='folder to write results to. It defaults to the current directory.')
+        action="store_true",
-    parser.add_argument('--synchronous', action='store_true',
+        help="Do not store the results of the simulation to disk, show in terminal instead.",
-                        help='Run trials serially and synchronously instead of in parallel. Defaults to false.')
+    )
-    parser.add_argument('-e', '--exporter', action='append',
+    parser.add_argument(
-                        help='Export environment and/or simulations using this exporter')
+        "--pdb", action="store_true", help="Use a pdb console in case of exception."
+    )
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="Run a customized version of a pdb console to debug a simulation.",
+    )
+    parser.add_argument(
+        "--graph",
+        "-g",
+        action="store_true",
+        help="Dump each trial's network topology as a GEXF graph. Defaults to false.",
+    )
+    parser.add_argument(
+        "--csv",
+        action="store_true",
+        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=output or "soil_output",
+        help="folder to write results to. It defaults to the current directory.",
+    )
+    if parallel is None:
+        parser.add_argument(
+            "--synchronous",
+            action="store_true",
+            help="Run trials serially and synchronously instead of in parallel. Defaults to false.",
+        )
+
+    parser.add_argument(
+        "-e",
+        "--exporter",
+        action="append",
+        default=[],
+        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
 
+    if parallel is None:
+        parallel = not args.synchronous
+
+    exporters = exporters or [
+        "default",
+    ]
+    for exp in args.exporter:
+        if exp not in exporters:
+            exporters.append(exp)
+    if args.csv:
+        exporters.append("csv")
+    if args.graph:
+        exporters.append("gexf")
+
     if os.getcwd() not in sys.path:
         sys.path.append(os.getcwd())
     if args.module:
         importlib.import_module(args.module)
+    if output is None:
+        output = args.output
 
-    logger.info('Loading config file: {}'.format(args.file))
+    debug = debug or args.debug
 
+    if args.pdb or debug:
+        args.synchronous = True
+        os.environ["SOIL_POSTMORTEM"] = "true"
+
+    res = []
     try:
-        exporters = list(args.exporter or ['default', ])
-        if args.csv:
-            exporters.append('csv')
-        if args.graph:
-            exporters.append('gexf')
         exp_params = {}
-        if args.dry_run:
-            exp_params['copy_to'] = sys.stdout
 
-        if not os.path.exists(args.file):
+        if sim:
-            logger.error('Please, input a valid file')
+            logger.info("Loading simulation instance")
-            return
+            sim.dry_run = args.dry_run
-        simulation.run_from_config(args.file,
+            sim.exporters = exporters
-                                   dry_run=args.dry_run,
+            sim.parallel = parallel
-                                   exporters=exporters,
+            sim.outdir = output
-                                   parallel=(not args.synchronous),
+            sims = [
-                                   outdir=args.output,
+                sim,
-                                   exporter_params=exp_params)
+            ]
-    except Exception:
+        else:
+            logger.info("Loading config file: {}".format(args.file))
+            if not os.path.exists(args.file):
+                logger.error("Please, input a valid file")
+                return
+
+            sims = list(
+                simulation.iter_from_config(
+                    args.file,
+                    dry_run=args.dry_run,
+                    exporters=exporters,
+                    parallel=parallel,
+                    outdir=output,
+                    exporter_params=exp_params,
+                    **kwargs,
+                )
+            )
+
+        for sim in sims:
+
+            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
+            if do_run:
+                res.append(sim.run())
+            else:
+                print("not running")
+                res.append(sim)
+
+    except Exception as ex:
         if args.pdb:
-            pdb.post_mortem()
+            from .debugging import post_mortem
+
+            print(traceback.format_exc())
+            post_mortem()
         else:
             raise
+    if debug:
+        from .debugging import set_trace
+
+        os.environ["SOIL_DEBUG"] = "true"
+        set_trace()
+    return res
 
 
-if __name__ == '__main__':
+@contextmanager
-    main()
+def easy(cfg, pdb=False, debug=False, **kwargs):
+    try:
+        yield main(cfg, debug=debug, pdb=pdb, **kwargs)[0]
+    except Exception as e:
+        if os.environ.get("SOIL_POSTMORTEM"):
+            from .debugging import post_mortem
+
+            print(traceback.format_exc())
+            post_mortem()
+        raise
+
+
+if __name__ == "__main__":
+    main(do_run=True)

soil/__main__.py

@@ -1,4 +1,9 @@
-from . import main
+from . import main as init_main
 
-if __name__ == '__main__':
+
-    main()
+def main():
+    init_main(do_run=True)
+
+
+if __name__ == "__main__":
+    init_main(do_run=True)

soil/agents/BassModel.py

@@ -1,4 +1,3 @@
-import random
 from . import FSM, state, default_state
 
 
@@ -8,6 +7,7 @@ class BassModel(FSM):
         innovation_prob
         imitation_prob
     """
+
     sentimentCorrelation = 0
 
     def step(self):
@@ -16,13 +16,13 @@ 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)
+            aware_neighbors = self.get_neighbors(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
 
 
@@ -7,42 +6,54 @@ class BigMarketModel(FSM):
     Settings:
         Names:
             enterprises [Array]
-            
+
             tweet_probability_enterprises [Array]
         Users:
             tweet_probability_users
-            
+
             tweet_relevant_probability
-            
+
             tweet_probability_about [Array]
-            
+
             sentiment_about [Array]
     """
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.enterprises = self.env.environment_params['enterprises']
+        self.enterprises = self.env.environment_params["enterprises"]
         self.type = ""
 
         if self.id < len(self.enterprises):  # Enterprises
-            self.set_state(self.enterprise.id)
+            self._set_state(self.enterprise.id)
             self.type = "Enterprise"
-            self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id]
+            self.tweet_probability = environment.environment_params[
+                "tweet_probability_enterprises"
+            ][self.id]
         else:  # normal users
             self.type = "User"
-            self.set_state(self.user.id)
+            self._set_state(self.user.id)
-            self.tweet_probability = environment.environment_params['tweet_probability_users']
+            self.tweet_probability = environment.environment_params[
-            self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability']
+                "tweet_probability_users"
-            self.tweet_probability_about = environment.environment_params['tweet_probability_about']  # List
+            ]
-            self.sentiment_about = environment.environment_params['sentiment_about']  # List
+            self.tweet_relevant_probability = environment.environment_params[
+                "tweet_relevant_probability"
+            ]
+            self.tweet_probability_about = environment.environment_params[
+                "tweet_probability_about"
+            ]  # List
+            self.sentiment_about = environment.environment_params[
+                "sentiment_about"
+            ]  # List
 
     @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
+            aware_neighbors = self.get_neighbors(
+                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
@@ -50,39 +61,49 @@ class BigMarketModel(FSM):
                 # Establecemos limites
                 if x.sentiment_about[self.id] > 1:
                     x.sentiment_about[self.id] = 1
-                if x.sentiment_about[self.id]< -1:
+                if x.sentiment_about[self.id] < -1:
                     x.sentiment_about[self.id] = -1
 
-                x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
+                x.attrs[
+                    "sentiment_enterprise_%s" % self.enterprises[self.id]
+                ] = x.sentiment_about[self.id]
 
     @state
     def user(self):
-        if random.random() < self.tweet_probability:  # Tweets
+        if self.random.random() < self.tweet_probability:  # Tweets
-            if random.random() < self.tweet_relevant_probability:  # Tweets something relevant
+            if (
+                self.random.random() < self.tweet_relevant_probability
+            ):  # Tweets something relevant
                 # Tweet probability per enterprise
                 for i in range(len(self.enterprises)):
-                    random_num = random.random()
+                    random_num = self.random.random()
                     if random_num < self.tweet_probability_about[i]:
                         # The condition is fulfilled, sentiments are evaluated towards that enterprise
                         if self.sentiment_about[i] < 0:
                             # NEGATIVO
-                            self.userTweets("negative",i)
+                            self.userTweets("negative", i)
                         elif self.sentiment_about[i] == 0:
                             # NEUTRO
                             pass
                         else:
                             # POSITIVO
-                            self.userTweets("positive",i)
+                            self.userTweets("positive", i)
-        for i in range(len(self.enterprises)):  # So that it never is set to 0 if there are not changes (logs)
+        for i in range(
-            self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
+            len(self.enterprises)
+        ):  # So that it never is set to 0 if there are not changes (logs)
+            self.attrs[
+                "sentiment_enterprise_%s" % self.enterprises[i]
+            ] = self.sentiment_about[i]
 
-    def userTweets(self, sentiment,enterprise):
+    def userTweets(self, sentiment, enterprise):
-        aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbours users
+        aware_neighbors = self.get_neighbors(
+            state_id=self.number_of_enterprises
+        )  # Nodes neighbours users
         for x in aware_neighbors:
             if sentiment == "positive":
-                x.sentiment_about[enterprise] +=0.003
+                x.sentiment_about[enterprise] += 0.003
             elif sentiment == "negative":
-                x.sentiment_about[enterprise] -=0.003
+                x.sentiment_about[enterprise] -= 0.003
             else:
                 pass
 
@@ -92,4 +113,6 @@ class BigMarketModel(FSM):
             if x.sentiment_about[enterprise] < -1:
                 x.sentiment_about[enterprise] = -1
 
-            x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]
+            x.attrs[
+                "sentiment_enterprise_%s" % self.enterprises[enterprise]
+            ] = x.sentiment_about[enterprise]

soil/agents/CounterModel.py

@@ -7,13 +7,17 @@ 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()))
+        neighbors = len(list(self.get_neighbors()))
-        self['times'] = self.get('times', 0) + 1
+        self["times"] = self.get("times", 0) + 1
-        self['neighbors'] = neighbors
+        self["neighbors"] = neighbors
-        self['total'] = total
+        self["total"] = total
 
 
 class AggregatedCounter(NetworkAgent):
@@ -22,17 +26,15 @@ class AggregatedCounter(NetworkAgent):
     in each step and adds it to its state.
     """
 
-    defaults = {
+    times = 0
-        'times': 0,
+    neighbors = 0
-        'neighbors': 0,
+    total = 0
-        'total': 0
-    }
 
     def step(self):
         # Outside effects
-        self['times'] += 1
+        self["times"] += 1
-        neighbors = len(list(self.get_neighboring_agents()))
+        neighbors = len(list(self.get_neighbors()))
-        self['neighbors'] += neighbors
+        self["neighbors"] += neighbors
-        total = len(list(self.get_agents()))
+        total = len(list(self.model.schedule.agents))
-        self['total'] += total
+        self["total"] += total
-        self.debug('Running for step: {}. Total: {}'.format(self.now, total))
+        self.debug("Running for step: {}. Total: {}".format(self.now, total))

soil/agents/Geo.py

@@ -2,20 +2,20 @@ from scipy.spatial import cKDTree as KDTree
 import networkx as nx
 from . import NetworkAgent, as_node
 
+
 class Geo(NetworkAgent):
-    '''In this type of network, nodes have a "pos" attribute.'''
+    """In this type of network, nodes have a "pos" attribute."""
 
     def geo_search(self, radius, node=None, center=False, **kwargs):
-        '''Get a list of nodes whose coordinates are closer than *radius* to *node*.'''
+        """Get a list of nodes whose coordinates are closer than *radius* to *node*."""
         node = as_node(node if node is not None else self)
 
         G = self.subgraph(**kwargs)
 
-        pos = nx.get_node_attributes(G, 'pos')
+        pos = nx.get_node_attributes(G, "pos")
         if not pos:
             return []
         nodes, coords = list(zip(*pos.items()))
         kdtree = KDTree(coords)  # Cannot provide generator.
         indices = kdtree.query_ball_point(pos[node], radius)
         return [nodes[i] for i in indices if center or (nodes[i] != node)]
-

soil/agents/IndependentCascadeModel.py

@@ -1,4 +1,3 @@
-import random
 from . import BaseAgent
 
 
@@ -12,10 +11,10 @@ class IndependentCascadeModel(BaseAgent):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.innovation_prob = self.env.environment_params['innovation_prob']
+        self.innovation_prob = self.env.environment_params["innovation_prob"]
-        self.imitation_prob = self.env.environment_params['imitation_prob']
+        self.imitation_prob = self.env.environment_params["imitation_prob"]
-        self.state['time_awareness'] = 0
+        self.state["time_awareness"] = 0
-        self.state['sentimentCorrelation'] = 0
+        self.state["sentimentCorrelation"] = 0
 
     def step(self):
         self.behaviour()
@@ -23,26 +22,28 @@ 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:
+            if self.state["id"] == 0:
-                self.state['id'] = 1
+                self.state["id"] = 1
-                self.state['sentimentCorrelation'] = 1
+                self.state["sentimentCorrelation"] = 1
-                self.state['time_awareness'] = self.env.now  # To know when they have been infected
+                self.state[
+                    "time_awareness"
+                ] = self.env.now  # To know when they have been infected
             else:
                 pass
 
             return
 
         # Imitation effects
-        if self.state['id'] == 0:
+        if self.state["id"] == 0:
-            aware_neighbors = self.get_neighboring_agents(state_id=1)
+            aware_neighbors = self.get_neighbors(state_id=1)
             for x in aware_neighbors:
-                if x.state['time_awareness'] == (self.env.now-1):
+                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["id"] = 1
-                self.state['sentimentCorrelation'] = 1
+                self.state["sentimentCorrelation"] = 1
             else:
                 pass
 

soil/agents/ModelM2.py

@@ -1,4 +1,3 @@
-import random
 import numpy as np
 from . import BaseAgent
 
@@ -24,84 +23,100 @@ class SpreadModelM2(BaseAgent):
     def __init__(self, model=None, unique_id=0, state=()):
         super().__init__(model=environment, unique_id=unique_id, state=state)
 
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
+        # Use a single generator with the same seed as `self.random`
-                                                           environment.environment_params['standard_variance'])
+        random = np.random.default_rng(seed=self._seed)
+        self.prob_neutral_making_denier = random.normal(
+            environment.environment_params["prob_neutral_making_denier"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
+        self.prob_infect = random.normal(
-                                            environment.environment_params['standard_variance'])
+            environment.environment_params["prob_infect"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
+        self.prob_cured_healing_infected = random.normal(
-                                                            environment.environment_params['standard_variance'])
+            environment.environment_params["prob_cured_healing_infected"],
-        self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
+            environment.environment_params["standard_variance"],
-                                                             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 = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
+        self.prob_vaccinated_healing_infected = random.normal(
-                                                                 environment.environment_params['standard_variance'])
+            environment.environment_params["prob_vaccinated_healing_infected"],
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
+            environment.environment_params["standard_variance"],
-                                                                  environment.environment_params['standard_variance'])
+        )
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
+        self.prob_vaccinated_vaccinate_neutral = random.normal(
-                                                         environment.environment_params['standard_variance'])
+            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):
 
-        if self.state['id'] == 0:  # Neutral
+        if self.state["id"] == 0:  # Neutral
             self.neutral_behaviour()
-        elif self.state['id'] == 1:  # Infected
+        elif self.state["id"] == 1:  # Infected
             self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
+        elif self.state["id"] == 2:  # Cured
             self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
+        elif self.state["id"] == 3:  # Vaccinated
             self.vaccinated_behaviour()
 
     def neutral_behaviour(self):
 
         # Infected
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
+        infected_neighbors = self.get_neighbors(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
+                self.state["id"] = 3  # Vaccinated making denier
 
     def infected_behaviour(self):
 
         # Neutral
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
+        neutral_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 1  # Infected
 
     def cured_behaviour(self):
 
         # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
+        neutral_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
+        infected_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 2  # Cured
 
     def vaccinated_behaviour(self):
 
         # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
+        infected_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 2  # Cured
 
         # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
+        neutral_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Generate anti-rumor
-        infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
+        infected_neighbors_2 = self.get_neighbors(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
+                neighbor.state["id"] = 2  # Cured
 
 
 class ControlModelM2(BaseAgent):
@@ -110,133 +125,146 @@ class ControlModelM2(BaseAgent):
         prob_neutral_making_denier
 
         prob_infect
-        
+
         prob_cured_healing_infected
-        
+
         prob_cured_vaccinate_neutral
-        
+
         prob_vaccinated_healing_infected
-        
+
         prob_vaccinated_vaccinate_neutral
-        
+
         prob_generate_anti_rumor
     """
 
-
     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'],
+        self.prob_neutral_making_denier = np.random.normal(
-                                                           environment.environment_params['standard_variance'])
+            environment.environment_params["prob_neutral_making_denier"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
+        self.prob_infect = np.random.normal(
-                                            environment.environment_params['standard_variance'])
+            environment.environment_params["prob_infect"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
+        self.prob_cured_healing_infected = np.random.normal(
-                                                            environment.environment_params['standard_variance'])
+            environment.environment_params["prob_cured_healing_infected"],
-        self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
+            environment.environment_params["standard_variance"],
-                                                             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_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
+        self.prob_vaccinated_healing_infected = np.random.normal(
-                                                                 environment.environment_params['standard_variance'])
+            environment.environment_params["prob_vaccinated_healing_infected"],
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
+            environment.environment_params["standard_variance"],
-                                                                  environment.environment_params['standard_variance'])
+        )
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
+        self.prob_vaccinated_vaccinate_neutral = np.random.normal(
-                                                         environment.environment_params['standard_variance'])
+            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"],
+            environment.environment_params["standard_variance"],
+        )
 
     def step(self):
 
-        if self.state['id'] == 0:  # Neutral
+        if self.state["id"] == 0:  # Neutral
             self.neutral_behaviour()
-        elif self.state['id'] == 1:  # Infected
+        elif self.state["id"] == 1:  # Infected
             self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
+        elif self.state["id"] == 2:  # Cured
             self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
+        elif self.state["id"] == 3:  # Vaccinated
             self.vaccinated_behaviour()
-        elif self.state['id'] == 4:  # Beacon-off
+        elif self.state["id"] == 4:  # Beacon-off
             self.beacon_off_behaviour()
-        elif self.state['id'] == 5:  # Beacon-on
+        elif self.state["id"] == 5:  # Beacon-on
             self.beacon_on_behaviour()
 
     def neutral_behaviour(self):
-        self.state['visible'] = False
+        self.state["visible"] = False
 
         # Infected
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
+        infected_neighbors = self.get_neighbors(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
+                self.state["id"] = 3  # Vaccinated making denier
 
     def infected_behaviour(self):
 
         # Neutral
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
+        neutral_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 1  # Infected
-        self.state['visible'] = False
+        self.state["visible"] = False
 
     def cured_behaviour(self):
 
-        self.state['visible'] = True
+        self.state["visible"] = True
         # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
+        neutral_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
+        infected_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 2  # Cured
 
     def vaccinated_behaviour(self):
-        self.state['visible'] = True
+        self.state["visible"] = True
 
         # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
+        infected_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 2  # Cured
 
         # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
+        neutral_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Generate anti-rumor
-        infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
+        infected_neighbors_2 = self.get_neighbors(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
+                neighbor.state["id"] = 2  # Cured
 
     def beacon_off_behaviour(self):
-        self.state['visible'] = False
+        self.state["visible"] = False
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
+        infected_neighbors = self.get_neighbors(state_id=1)
         if len(infected_neighbors) > 0:
-            self.state['id'] == 5  # Beacon on
+            self.state["id"] == 5  # Beacon on
 
     def beacon_on_behaviour(self):
-        self.state['visible'] = False
+        self.state["visible"] = False
         # Cure (M2 feature added)
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
+        infected_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 2  # Cured
-            neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
+            neutral_neighbors_infected = neighbor.get_neighbors(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
+                    neighbor.state["id"] = 3  # Vaccinated
-            infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
+            infected_neighbors_infected = neighbor.get_neighbors(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
+                    neighbor.state["id"] = 2  # Cured
 
         # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
+        neutral_neighbors = self.get_neighbors(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
+                neighbor.state["id"] = 3  # Vaccinated

soil/agents/SISaModel.py

@@ -1,4 +1,3 @@
-import random
 import numpy as np
 from . import FSM, state
 
@@ -7,87 +6,97 @@ class SISaModel(FSM):
     """
     Settings:
         neutral_discontent_spon_prob
-        
+
         neutral_discontent_infected_prob
-        
+
         neutral_content_spon_prob
-        
+
         neutral_content_infected_prob
-        
+
         discontent_neutral
-        
+
         discontent_content
-        
+
         variance_d_c
-        
+
         content_discontent
-        
+
         variance_c_d
-        
+
         content_neutral
-        
+
         standard_variance
     """
 
     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.env['standard_variance'])
-        self.neutral_discontent_infected_prob = np.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.env['standard_variance'])
-        self.neutral_content_infected_prob = np.random.normal(self.env['neutral_content_infected_prob'],
-                                                              self.env['standard_variance'])
 
-        self.discontent_neutral = np.random.normal(self.env['discontent_neutral'],
+        self.neutral_discontent_spon_prob = random.normal(
-                                                   self.env['standard_variance'])
+            self.env["neutral_discontent_spon_prob"], self.env["standard_variance"]
-        self.discontent_content = np.random.normal(self.env['discontent_content'],
+        )
-                                                   self.env['variance_d_c'])
+        self.neutral_discontent_infected_prob = random.normal(
+            self.env["neutral_discontent_infected_prob"], self.env["standard_variance"]
+        )
+        self.neutral_content_spon_prob = random.normal(
+            self.env["neutral_content_spon_prob"], self.env["standard_variance"]
+        )
+        self.neutral_content_infected_prob = random.normal(
+            self.env["neutral_content_infected_prob"], self.env["standard_variance"]
+        )
 
-        self.content_discontent = np.random.normal(self.env['content_discontent'],
+        self.discontent_neutral = random.normal(
-                                                   self.env['variance_c_d'])
+            self.env["discontent_neutral"], self.env["standard_variance"]
-        self.content_neutral = np.random.normal(self.env['content_neutral'],
+        )
-                                                self.env['standard_variance'])
+        self.discontent_content = random.normal(
+            self.env["discontent_content"], self.env["variance_d_c"]
+        )
+
+        self.content_discontent = random.normal(
+            self.env["content_discontent"], self.env["variance_c_d"]
+        )
+        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)
+        discontent_neighbors = self.count_neighbors(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)
+        content_neighbors = self.count_neighbors(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)
+        content_neighbors = self.count_neighbors(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)
+        discontent_neighbors = self.count_neighbors(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
 
 
@@ -6,27 +5,31 @@ class SentimentCorrelationModel(BaseAgent):
     """
     Settings:
         outside_effects_prob
-        
+
         anger_prob
-        
+
         joy_prob
-        
+
         sadness_prob
-        
+
         disgust_prob
     """
 
     def __init__(self, environment, unique_id=0, state=()):
         super().__init__(model=environment, unique_id=unique_id, state=state)
-        self.outside_effects_prob = environment.environment_params['outside_effects_prob']
+        self.outside_effects_prob = environment.environment_params[
-        self.anger_prob = environment.environment_params['anger_prob']
+            "outside_effects_prob"
-        self.joy_prob = environment.environment_params['joy_prob']
+        ]
-        self.sadness_prob = environment.environment_params['sadness_prob']
+        self.anger_prob = environment.environment_params["anger_prob"]
-        self.disgust_prob = environment.environment_params['disgust_prob']
+        self.joy_prob = environment.environment_params["joy_prob"]
-        self.state['time_awareness'] = []
+        self.sadness_prob = environment.environment_params["sadness_prob"]
+        self.disgust_prob = environment.environment_params["disgust_prob"]
+        self.state["time_awareness"] = []
         for i in range(4):  # In this model we have 4 sentiments
-            self.state['time_awareness'].append(0)  # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
+            self.state["time_awareness"].append(
-        self.state['sentimentCorrelation'] = 0
+                0
+            )  # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
+        self.state["sentimentCorrelation"] = 0
 
     def step(self):
         self.behaviour()
@@ -38,65 +41,75 @@ class SentimentCorrelationModel(BaseAgent):
         sad_neighbors_1_time_step = []
         disgusted_neighbors_1_time_step = []
 
-        angry_neighbors = self.get_neighboring_agents(state_id=1)
+        angry_neighbors = self.get_neighbors(state_id=1)
         for x in angry_neighbors:
-            if x.state['time_awareness'][0] > (self.env.now-500):
+            if x.state["time_awareness"][0] > (self.env.now - 500):
                 angry_neighbors_1_time_step.append(x)
         num_neighbors_angry = len(angry_neighbors_1_time_step)
 
-        joyful_neighbors = self.get_neighboring_agents(state_id=2)
+        joyful_neighbors = self.get_neighbors(state_id=2)
         for x in joyful_neighbors:
-            if x.state['time_awareness'][1] > (self.env.now-500):
+            if x.state["time_awareness"][1] > (self.env.now - 500):
                 joyful_neighbors_1_time_step.append(x)
         num_neighbors_joyful = len(joyful_neighbors_1_time_step)
 
-        sad_neighbors = self.get_neighboring_agents(state_id=3)
+        sad_neighbors = self.get_neighbors(state_id=3)
         for x in sad_neighbors:
-            if x.state['time_awareness'][2] > (self.env.now-500):
+            if x.state["time_awareness"][2] > (self.env.now - 500):
                 sad_neighbors_1_time_step.append(x)
         num_neighbors_sad = len(sad_neighbors_1_time_step)
 
-        disgusted_neighbors = self.get_neighboring_agents(state_id=4)
+        disgusted_neighbors = self.get_neighbors(state_id=4)
         for x in disgusted_neighbors:
-            if x.state['time_awareness'][3] > (self.env.now-500):
+            if x.state["time_awareness"][3] > (self.env.now - 500):
                 disgusted_neighbors_1_time_step.append(x)
         num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
 
-        anger_prob = self.anger_prob+(len(angry_neighbors_1_time_step)*self.anger_prob)
+        anger_prob = self.anger_prob + (
-        joy_prob = self.joy_prob+(len(joyful_neighbors_1_time_step)*self.joy_prob)
+            len(angry_neighbors_1_time_step) * self.anger_prob
-        sadness_prob = self.sadness_prob+(len(sad_neighbors_1_time_step)*self.sadness_prob)
+        )
-        disgust_prob = self.disgust_prob+(len(disgusted_neighbors_1_time_step)*self.disgust_prob)
+        joy_prob = self.joy_prob + (len(joyful_neighbors_1_time_step) * self.joy_prob)
+        sadness_prob = self.sadness_prob + (
+            len(sad_neighbors_1_time_step) * self.sadness_prob
+        )
+        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:
+        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["sentimentCorrelation"] = self.state[
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
+                "id"
-            self.state['sentiment'] = 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
+            self.state["sentiment"] = self.state["id"]
 
+        if num < anger_prob:
 
-        if(num<anger_prob):
+            self.state["id"] = 1
+            self.state["sentimentCorrelation"] = 1
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
+        elif num < joy_prob + anger_prob and num > anger_prob:
 
-            self.state['id'] = 1
+            self.state["id"] = 2
-            self.state['sentimentCorrelation'] = 1
+            self.state["sentimentCorrelation"] = 2
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
-        elif (num<joy_prob+anger_prob and num>anger_prob):
+        elif num < sadness_prob + anger_prob + joy_prob and num > joy_prob + anger_prob:
 
-            self.state['id'] = 2
+            self.state["id"] = 3
-            self.state['sentimentCorrelation'] = 2
+            self.state["sentimentCorrelation"] = 3
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
-        elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
+        elif (
+            num < disgust_prob + sadness_prob + anger_prob + joy_prob
+            and num > sadness_prob + anger_prob + joy_prob
+        ):
 
-            self.state['id'] = 3
+            self.state["id"] = 4
-            self.state['sentimentCorrelation'] = 3
+            self.state["sentimentCorrelation"] = 4
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
-        elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
 
-            self.state['id'] = 4
+        self.state["sentiment"] = self.state["id"]
-            self.state['sentimentCorrelation'] = 4
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-
-        self.state['sentiment'] = self.state['id']

soil/agents/evented.py

@@ -0,0 +1,77 @@
+from . import BaseAgent
+from ..events import Message, Tell, Ask, TimedOut
+from ..time import BaseCond
+from functools import partial
+from collections import deque
+
+
+class ReceivedOrTimeout(BaseCond):
+    def __init__(
+        self, agent, expiration=None, timeout=None, check=True, ignore=False, **kwargs
+    ):
+        if expiration is None:
+            if timeout is not None:
+                expiration = agent.now + timeout
+        self.expiration = expiration
+        self.ignore = ignore
+        self.check = check
+        super().__init__(**kwargs)
+
+    def expired(self, time):
+        return self.expiration and self.expiration < time
+
+    def ready(self, agent, time):
+        return len(agent._inbox) or self.expired(time)
+
+    def return_value(self, agent):
+        if not self.ignore and self.expired(agent.now):
+            raise TimedOut("No messages received")
+        if self.check:
+            agent.check_messages()
+        return None
+
+    def schedule_next(self, time, delta, first=False):
+        if self._delta is not None:
+            delta = self._delta
+        return (time + delta, self)
+
+    def __repr__(self):
+        return f"ReceivedOrTimeout(expires={self.expiration})"
+
+
+class EventedAgent(BaseAgent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._inbox = deque()
+        self._processed = 0
+
+    def on_receive(self, *args, **kwargs):
+        pass
+
+    def received(self, *args, **kwargs):
+        return ReceivedOrTimeout(self, *args, **kwargs)
+
+    def tell(self, msg, sender=None):
+        self._inbox.append(Tell(timestamp=self.now, payload=msg, sender=sender))
+
+    def ask(self, msg, timeout=None, **kwargs):
+        ask = Ask(timestamp=self.now, payload=msg, sender=self)
+        self._inbox.append(ask)
+        expiration = float("inf") if timeout is None else self.now + timeout
+        return ask.replied(expiration=expiration, **kwargs)
+
+    def check_messages(self):
+        changed = False
+        while self._inbox:
+            msg = self._inbox.popleft()
+            self._processed += 1
+            if msg.expired(self.now):
+                continue
+            changed = True
+            reply = self.on_receive(msg.payload, sender=msg.sender)
+            if isinstance(msg, Ask):
+                msg.reply = reply
+        return changed
+
+
+Evented = EventedAgent

soil/agents/fsm.py

@@ -0,0 +1,140 @@
+from . import MetaAgent, BaseAgent
+
+from functools import partial, wraps
+import inspect
+
+
+def state(name=None):
+    def decorator(func, name=None):
+        """
+        A state function should return either a state id, or a tuple (state_id, when)
+        The default value for state_id is the current state id.
+        The default value for when is the interval defined in the environment.
+        """
+        if inspect.isgeneratorfunction(func):
+            orig_func = func
+
+            @wraps(func)
+            def func(self):
+                while True:
+                    if not self._coroutine:
+                        self._coroutine = orig_func(self)
+
+                    try:
+                        if self._last_except:
+                            n = self._coroutine.throw(self._last_except)
+                        else:
+                            n = self._coroutine.send(self._last_return)
+                        if n:
+                            return None, n
+                        return n
+                    except StopIteration as ex:
+                        self._coroutine = None
+                        next_state = ex.value
+                        if next_state is not None:
+                            self._set_state(next_state)
+                        return next_state
+                    finally:
+                        self._last_return = None
+                        self._last_except = None
+
+        func.id = name or func.__name__
+        func.is_default = False
+        return func
+
+    if callable(name):
+        return decorator(name)
+    else:
+        return partial(decorator, name=name)
+
+
+def default_state(func):
+    func.is_default = True
+    return func
+
+
+class MetaFSM(MetaAgent):
+    def __new__(mcls, name, bases, namespace):
+        states = {}
+        # Re-use states from inherited classes
+        default_state = None
+        for i in bases:
+            if isinstance(i, MetaFSM):
+                for state_id, state in i._states.items():
+                    if state.is_default:
+                        default_state = state
+                    states[state_id] = state
+
+        # Add new states
+        for attr, func in namespace.items():
+            if hasattr(func, "id"):
+                if func.is_default:
+                    default_state = func
+                states[func.id] = func
+
+        namespace.update(
+            {
+                "_default_state": default_state,
+                "_states": states,
+            }
+        )
+
+        return super(MetaFSM, mcls).__new__(
+            mcls=mcls, name=name, bases=bases, namespace=namespace
+        )
+
+
+class FSM(BaseAgent, metaclass=MetaFSM):
+    def __init__(self, **kwargs):
+        super(FSM, self).__init__(**kwargs)
+        if not hasattr(self, "state_id"):
+            if not self._default_state:
+                raise ValueError(
+                    "No default state specified for {}".format(self.unique_id)
+                )
+            self.state_id = self._default_state.id
+
+        self._coroutine = None
+        self._set_state(self.state_id)
+
+    def step(self):
+        self.debug(f"Agent {self.unique_id} @ state {self.state_id}")
+        default_interval = super().step()
+
+        next_state = self._states[self.state_id](self)
+
+        when = None
+        try:
+            next_state, *when = next_state
+            if not when:
+                when = None
+            elif len(when) == 1:
+                when = when[0]
+            else:
+                raise ValueError(
+                    "Too many values returned. Only state (and time) allowed"
+                )
+        except TypeError:
+            pass
+
+        if next_state is not None:
+            self._set_state(next_state)
+
+        return when or default_interval
+
+    def _set_state(self, state, when=None):
+        if hasattr(state, "id"):
+            state = state.id
+        if state not in self._states:
+            raise ValueError("{} is not a valid state".format(state))
+        self.state_id = state
+        if when is not None:
+            self.model.schedule.add(self, when=when)
+        return state
+
+    def die(self):
+        return self.dead, super().die()
+
+    @state
+    def dead(self):
+        return self.die()

soil/agents/network_agents.py

@@ -0,0 +1,85 @@
+from . import BaseAgent
+
+
+class NetworkAgent(BaseAgent):
+    def __init__(self, *args, topology, node_id, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        assert topology is not None
+        assert node_id is not None
+        self.G = topology
+        assert self.G
+        self.node_id = node_id
+
+    def count_neighbors(self, state_id=None, **kwargs):
+        return len(self.get_neighbors(state_id=state_id, **kwargs))
+
+    def get_neighbors(self, **kwargs):
+        return list(self.iter_agents(limit_neighbors=True, **kwargs))
+
+    @property
+    def node(self):
+        return self.G.nodes[self.node_id]
+
+    def iter_agents(self, unique_id=None, *, limit_neighbors=False, **kwargs):
+        unique_ids = None
+        if isinstance(unique_id, list):
+            unique_ids = set(unique_id)
+        elif unique_id is not None:
+            unique_ids = set(
+                [
+                    unique_id,
+                ]
+            )
+
+        if limit_neighbors:
+            neighbor_ids = set()
+            for node_id in self.G.neighbors(self.node_id):
+                if self.G.nodes[node_id].get("agent") is not None:
+                    neighbor_ids.add(node_id)
+            if unique_ids:
+                unique_ids = unique_ids & neighbor_ids
+            else:
+                unique_ids = neighbor_ids
+            if not unique_ids:
+                return
+            unique_ids = list(unique_ids)
+        yield from super().iter_agents(unique_id=unique_ids, **kwargs)
+
+    def subgraph(self, center=True, **kwargs):
+        include = [self] if center else []
+        G = self.G.subgraph(
+            n.node_id for n in list(self.get_agents(**kwargs) + include)
+        )
+        return G
+
+    def remove_node(self):
+        self.debug(f"Removing node for {self.unique_id}: {self.node_id}")
+        self.G.remove_node(self.node_id)
+        self.node_id = None
+
+    def add_edge(self, other, edge_attr_dict=None, *edge_attrs):
+        if self.node_id not in self.G.nodes(data=False):
+            raise ValueError(
+                "{} not in list of existing agents in the network".format(
+                    self.unique_id
+                )
+            )
+        if other.node_id not in self.G.nodes(data=False):
+            raise ValueError(
+                "{} not in list of existing agents in the network".format(other)
+            )
+
+        self.G.add_edge(
+            self.node_id, other.node_id, edge_attr_dict=edge_attr_dict, *edge_attrs
+        )
+
+    def die(self, remove=True):
+        if not self.alive:
+            return None
+        if remove:
+            self.remove_node()
+        return super().die()
+
+
+NetAgent = NetworkAgent

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 +1,270 @@
+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]
+    fixed: 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[bool] = False
+    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("unique_id", None) is not None and values.get("n", 1) > 1:
+            raise ValueError(
+                f"An unique_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
+    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
+
+    topology = None
+    if network:
+        topology = 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"] = False
+            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"] = True
+
+        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"] = True
+        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["topology"] = topology
+    model_params["agents"] = agents
+
+    return Config(version="2", model_params=model_params, **new)

soil/datacollection.py

@@ -1,26 +1,6 @@
 from mesa import DataCollector as MDC
 
+
 class SoilDataCollector(MDC):
-
+    def __init__(self, *args, **kwargs):
-
-    def __init__(self, environment, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        # Populate model and env reporters so they have a key per 
-        # So they can be shown in the web interface
-        self.environment = environment
-
-
-    @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 +1,190 @@
+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("contextlib")
+            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
+
+    def do_continue_state(self, arg):
+        self.do_break_state(arg, temporary=True)
+        return self.do_continue("")
+
+    do_cs = do_continue_state
+
+    @wrapcmd
+    def do_soil_agent():
+        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_aa = do_soil_agent
+
+    def do_break_state(self, arg: str, instances=None, temporary=False):
+        """
+        Break before a specified state is stepped into.
+        """
+
+        klass = None
+        state = arg
+        if not state:
+            self.error("Specify at least a state name")
+            return
+
+        state, *tokens = state.lstrip().split()
+        if tokens:
+            instances = list(eval(token) for token in tokens)
+
+        colon = state.find(":")
+
+        if colon > 0:
+            klass = state[:colon].rstrip()
+            state = state[colon + 1 :].strip()
+
+            print(klass, state, tokens)
+            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)
+            ]
+
+        if not klasses:
+            self.error("No agent classes found")
+
+        for klass in klasses:
+            try:
+                func = getattr(klass, state)
+            except AttributeError:
+                self.error(f"State {state} not found in class {klass}")
+                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
+            if instances:
+                cond = f"self.unique_id in { repr(instances) }"
+
+            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 do_break_state_self(self, arg: str, temporary=False):
+        """
+        Break before a specified state is stepped into, for the current agent
+        """
+        agent = self.curframe.f_locals.get("self")
+        if not agent:
+            self.error("No current agent.")
+            self.error("Try this again when the debugger is stopped inside an agent")
+            return
+
+        arg = f"{agent.__class__.__name__}:{ arg } {agent.unique_id}"
+        return self.do_break_state(arg)
+
+    do_bss = do_break_state_self
+
+
+debugger = None
+
+
+def set_trace(frame=None, **kwargs):
+    global debugger
+    if debugger is None:
+        debugger = Debug(**kwargs)
+    frame = frame or sys._getframe().f_back
+    debugger.set_trace(frame)
+
+
+def post_mortem(traceback=None, **kwargs):
+    global debugger
+    if debugger is None:
+        debugger = Debug(**kwargs)
+    t = sys.exc_info()[2]
+    debugger.reset()
+    debugger.interaction(None, t)

soil/environment.py

@@ -1,377 +1,339 @@
+from __future__ import annotations
+
 import os
 import sqlite3
-import csv
 import math
-import random
+import logging
-import yaml
+import inspect
-import tempfile
+
-import pandas as pd
+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, events
 
-# These properties will be copied when pickling/unpickling the environment
-_CONFIG_PROPS = [ 'name',
-                  'states',
-                  'default_state',
-                  'interval',
-                 ]
 
-class Environment(Model):
+class BaseEnvironment(Model):
     """
-    The environment is key in a simulation. It contains the network topology,
+    The environment is key in a simulation. It controls how agents interact,
-    a reference to network and environment agents, as well as the environment
+    and what information is available to them.
-    params, which are used as shared state between agents.
+
+    This is an opinionated version of `mesa.Model` class, which adds many
+    convenience methods and abstractions.
 
     The environment parameters and the state of every agent can be accessed
-    both by using the environment as a dictionary or with the environment's 
+    both by using the environment as a dictionary and with the environment's
     :meth:`soil.environment.Environment.get` method.
     """
 
-    def __init__(self, name=None,
+    def __init__(
-                 network_agents=None,
+        self,
-                 environment_agents=None,
+        id="unnamed_env",
-                 states=None,
+        seed="default",
-                 default_state=None,
+        schedule_class=time.TimedActivation,
-                 interval=1,
+        dir_path=None,
-                 network_params=None,
+        interval=1,
-                 seed=None,
+        agent_class=None,
-                 topology=None,
+        agents: [tuple[type, Dict[str, Any]]] = {},
-                 schedule=None,
+        agent_reporters: Optional[Any] = None,
-                 initial_time=0,
+        model_reporters: Optional[Any] = None,
-                 environment_params=None,
+        tables: Optional[Any] = None,
-                 history=True,
+        **env_params,
-                 dir_path=None,
+    ):
-                 **kwargs):
 
+        super().__init__(seed=seed)
+        self.env_params = env_params or {}
 
-        super().__init__()
+        self.current_id = -1
 
-        self.schedule = schedule
+        self.id = id
-        if schedule is None:
-            self.schedule = time.TimedActivation()
 
-        self.name = name or 'UnnamedEnvironment'
+        self.dir_path = dir_path or os.getcwd()
-        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 {}
 
-        if topology is None:
+        if schedule_class is None:
-            network_params = network_params or {}
+            schedule_class = time.TimedActivation
-            topology = serialization.load_network(network_params,
+        else:
-                                                  dir_path=dir_path)
+            schedule_class = serialization.deserialize(schedule_class)
-        if not topology:
+        self.schedule = schedule_class(self)
-            topology = nx.Graph()
-        self.G = nx.Graph(topology) 
 
+        self.agent_class = agent_class or agentmod.BaseAgent
 
-        self.environment_params = environment_params or {}
-        self.environment_params.update(kwargs)
-
-        self._env_agents = {}
         self.interval = interval
-        if history:
+        self.init_agents(agents)
-            history = History
-        else:
-            history = NoHistory
-        self._history = history(name=self.name,
-                                backup=True)
-        self['SEED'] = seed
 
-        if network_agents:
+        self.logger = utils.logger.getChild(self.id)
-            distro = agents.calculate_distribution(network_agents)
-            self.network_agents = agents._convert_agent_types(distro)
-        else:
-            self.network_agents = []
 
-        environment_agents = environment_agents or []
+        self.datacollector = DataCollector(
-        if environment_agents:
+            model_reporters=model_reporters,
-            distro = agents.calculate_distribution(environment_agents)
+            agent_reporters=agent_reporters,
-            environment_agents = agents._convert_agent_types(distro)
+            tables=tables,
-        self.environment_agents = environment_agents
+        )
+
+    def _agent_from_dict(self, agent):
+        """
+        Translate an agent dictionary into an 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]]] = {}):
+        """
+        Initialize the agents in the model from either a `soil.config.AgentConfig` or a list of
+        dictionaries that each describes an agent.
+
+        If given a list of dictionaries, an agent will be created for each dictionary. The agent
+        class can be specified through the `agent_class` key. The rest of the items will be used
+        as parameters to the agent.
+        """
+        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 = self._agent_dict_from_config(lst)
+
+        # 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._agent_from_dict(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)
+
+    def _agent_dict_from_config(self, cfg):
+        return agentmod.from_config(cfg, random=self.random)
+
+    @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):
         if self.schedule:
             return self.schedule.time
-        raise Exception('The environment has not been scheduled, so it has no sense of time')
+        raise Exception(
+            "The environment has not been scheduled, so it has no sense of time"
+        )
 
-    @property
+    def add_agent(self, unique_id=None, **kwargs):
-    def agents(self):
+        if unique_id is None:
-        yield from self.environment_agents
+            unique_id = self.next_id()
-        yield from self.network_agents
 
-    @property
+        kwargs["unique_id"] = unique_id
-    def environment_agents(self):
+        a = self._agent_from_dict(kwargs)
-        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)
-        a = None
-        if agent_type:
-            state = defstate
-            a = agent_type(model=self,
-                           unique_id=agent_id)
-
-        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):
+    def log(self, message, *args, level=logging.INFO, **kwargs):
-        agent_id = int(len(self.G.nodes()))
+        if not self.logger.isEnabledFor(level):
-        self.G.add_node(agent_id)
+            return
-        a = self.set_agent(agent_id, agent_type, state)
+        message = message + " ".join(str(i) for i in args)
-        a['visible'] = True
+        message = " @{:>3}: {}".format(self.now, message)
-        return a
+        for k, v in kwargs:
-
+            message += " {k}={v} ".format(k, v)
-    def add_edge(self, agent1, agent2, start=None, **attrs):
+        extra = {}
-        if hasattr(agent1, 'id'):
+        extra["now"] = self.now
-            agent1 = agent1.id
+        extra["id"] = self.id
-        if hasattr(agent2, 'id'):
+        return self.logger.log(level, message, extra=extra)
-            agent2 = agent2.id
-        start = start or self.now
-        return self.G.add_edge(agent1, agent2, **attrs)
 
     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.schedule.steps:^5} - Time: {self.now:^5} ---"
+        )
         self.schedule.step()
-
+        self.datacollector.collect(self)
-    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)
 
     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
+        Get the value of an environment attribute.
-        given point in the simulation (history).
+        Return `default` if the value is not set.
-        If key is an attribute name, this method returns
+        """
-        the current value.
+        return self.env_params.get(key, default)
-        To get values at other times, use a
-        :meth: `soil.history.Key` tuple.
-        '''
-        return self[key] if key in self else default
 
-    def get_agent(self, agent_id):
+    def __getitem__(self, key):
-        return self.G.nodes[agent_id]['agent']
+        return self.env_params.get(key)
 
-    def get_agents(self, nodes=None):
+    def __setitem__(self, key, value):
-        if nodes is None:
+        return self.env_params.__setitem__(key, value)
-            return self.agents
-        return (self.G.nodes[i]['agent'] for i in nodes)
 
-    def dump_csv(self, f):
+    def __str__(self):
-        with utils.open_or_reuse(f, 'w') as f:
+        return str(self.env_params)
-            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")
+class NetworkEnvironment(BaseEnvironment):
+    """
+    The NetworkEnvironment is an environment that includes one or more networkx.Graph intances
+    and methods to associate agents to nodes and vice versa.
+    """
 
-    def dump(self, *args, formats=None, **kwargs):
+    def __init__(
-        if not formats:
+        self, *args, topology: Union[config.NetConfig, nx.Graph] = None, **kwargs
-            return
+    ):
-        functions = {
+        agents = kwargs.pop("agents", None)
-            'csv': self.dump_csv,
+        super().__init__(*args, agents=None, **kwargs)
-            '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):
+        self._set_topology(topology)
-        return self._history.dump(f)
 
-    def state_to_tuples(self, now=None):
+        self.init_agents(agents)
-        if now is None:
-            now = self.now
-        for k, v in self.environment_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):
+    def init_agents(self, *args, **kwargs):
-        return self._history.to_tuples()
+        """Initialize the agents from a"""
+        super().init_agents(*args, **kwargs)
+        for agent in self.schedule._agents.values():
+            if hasattr(agent, "node_id"):
+                self._init_node(agent)
 
-    def history_to_graph(self):
+    def _init_node(self, agent):
-        G = nx.Graph(self.G)
+        """
+        Make sure the node for a given agent has the proper attributes.
+        """
+        self.G.nodes[agent.node_id]["agent"] = agent
 
-        for agent in self.network_agents:
+    def _agent_dict_from_config(self, cfg):
+        return agentmod.from_config(cfg, topology=self.G, random=self.random)
 
-            attributes = {'agent': str(agent.__class__)}
+    def _agent_from_dict(self, agent, unique_id=None):
-            lastattributes = {}
+        agent = dict(agent)
-            spells = []
+
-            lastvisible = False
+        if not agent.get("topology", False):
-            laststep = None
+            return super()._agent_from_dict(agent)
-            history = self[agent.id, None, None]
+
-            if not history:
+        if unique_id is None:
+            unique_id = self.next_id()
+        node_id = agent.get("node_id", None)
+        if node_id is None:
+            node_id = network.find_unassigned(self.G, random=self.random)
+        self.G.nodes[node_id]["agent"] = None
+        agent["node_id"] = node_id
+        agent["unique_id"] = unique_id
+        agent["topology"] = self.G
+        node_attrs = self.G.nodes[node_id]
+        node_attrs.update(agent)
+        agent = node_attrs
+
+        a = super()._agent_from_dict(agent)
+        self._init_node(a)
+
+        return a
+
+    def _set_topology(self, cfg=None, dir_path=None):
+        if cfg is None:
+            cfg = nx.Graph()
+        elif not isinstance(cfg, nx.Graph):
+            cfg = network.from_config(cfg, dir_path=dir_path or self.dir_path)
+
+        self.G = cfg
+
+    @property
+    def network_agents(self):
+        for a in self.schedule._agents:
+            if isinstance(a, agentmod.NetworkAgent):
+                yield a
+
+    def add_node(self, agent_class, unique_id=None, node_id=None, **kwargs):
+        if unique_id is None:
+            unique_id = self.next_id()
+        if node_id is None:
+            node_id = network.find_unassigned(
+                G=self.G, shuffle=True, random=self.random
+            )
+            if node_id is None:
+                node_id = f"node_for_{unique_id}"
+
+        if node_id not in self.G.nodes:
+            self.G.add_node(node_id)
+
+        assert "agent" not in self.G.nodes[node_id]
+        self.G.nodes[node_id]["agent"] = None  # Reserve
+
+        a = self.add_agent(
+            unique_id=unique_id,
+            agent_class=agent_class,
+            topology=self.G,
+            node_id=node_id,
+            **kwargs,
+        )
+        a["visible"] = True
+        return a
+
+    def add_agent(self, *args, **kwargs):
+        a = super().add_agent(*args, **kwargs)
+        if "node_id" in a:
+            assert self.G.nodes[a.node_id]["agent"] == a
+        return a
+
+    def agent_for_node_id(self, node_id):
+        return self.G.nodes[node_id].get("agent")
+
+    def populate_network(self, agent_class, weights=None, **agent_params):
+        if not hasattr(agent_class, "len"):
+            agent_class = [agent_class]
+            weights = None
+        for (node_id, node) in self.G.nodes(data=True):
+            if "agent" in node:
                 continue
-            for t_step, attribute, value in sorted(list(history)):
+            a_class = self.random.choices(agent_class, weights)[0]
-                if attribute == 'visible':
+            self.add_agent(node_id=node_id, agent_class=a_class, **agent_params)
-                    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 = []
 
 
-SoilEnvironment = Environment
+class EventedEnvironment(BaseEnvironment):
+    def broadcast(self, msg, sender=None, expiration=None, ttl=None, **kwargs):
+        for agent in self.agents(**kwargs):
+            if agent == sender:
+                continue
+            self.logger.info(f"Telling {repr(agent)}: {msg} ttl={ttl}")
+            try:
+                inbox = agent._inbox
+            except AttributeError:
+                self.logger.info(
+                    f"Agent {agent.unique_id} cannot receive events because it does not have an inbox"
+                )
+                continue
+            # Allow for AttributeError exceptions in this part of the code
+            inbox.append(
+                events.Tell(
+                    payload=msg,
+                    sender=sender,
+                    expiration=expiration if ttl is None else self.now + ttl,
+                )
+            )
+
+
+class Environment(NetworkEnvironment, EventedEnvironment):
+    """Default environment class, has both network and event capabilities"""

soil/events.py

@@ -0,0 +1,56 @@
+from .time import BaseCond
+from dataclasses import dataclass, field
+from typing import Any
+from uuid import uuid4
+
+
+class Event:
+    pass
+
+
+@dataclass
+class Message:
+    payload: Any
+    sender: Any = None
+    expiration: float = None
+    timestamp: float = None
+    id: int = field(default_factory=uuid4)
+
+    def expired(self, when):
+        return self.expiration is not None and self.expiration < when
+
+
+class Reply(Message):
+    source: Message
+
+
+class ReplyCond(BaseCond):
+    def __init__(self, ask, *args, **kwargs):
+        self._ask = ask
+        super().__init__(*args, **kwargs)
+
+    def ready(self, agent, time):
+        return self._ask.reply is not None or self._ask.expired(time)
+
+    def return_value(self, agent):
+        if self._ask.expired(agent.now):
+            raise TimedOut()
+        return self._ask.reply
+
+    def __repr__(self):
+        return f"ReplyCond({self._ask.id})"
+
+
+class Ask(Message):
+    reply: Message = None
+
+    def replied(self, expiration=None):
+        return ReplyCond(self)
+
+
+class Tell(Message):
+    pass
+
+
+class TimedOut(Exception):
+    pass

soil/exporters.py

@@ -1,17 +1,20 @@
 import os
-import csv as csvlib
+import sys
-import time
+from time import time as current_time
 from io import BytesIO
+from sqlalchemy import create_engine
+from textwrap import dedent, indent
+
 
 import matplotlib.pyplot as plt
 import networkx as nx
 
 
 from .serialization import deserialize
-from .utils import open_or_reuse, logger, timer
+from .utils import try_backup, open_or_reuse, logger, timer
 
 
-from . import utils
+from . import utils, network
 
 
 class DryRunner(BytesIO):
@@ -22,50 +25,58 @@ class DryRunner(BytesIO):
 
     def write(self, txt):
         if self.__copy_to:
-            self.__copy_to.write('{}:::{}'.format(self.__fname, txt))
+            self.__copy_to.write("{}:::{}".format(self.__fname, txt))
         try:
             super().write(txt)
         except TypeError:
-            super().write(bytes(txt, 'utf-8'))
+            super().write(bytes(txt, "utf-8"))
 
     def close(self):
-        content = '(binary data not shown)'
+        content = "(binary data not shown)"
         try:
             content = self.getvalue().decode()
         except UnicodeDecodeError:
             pass
-        logger.info('**Not** written to {} (dry run mode):\n\n{}\n\n'.format(self.__fname, content))
+        logger.info(
+            "**Not** written to {} (dry run mode):\n\n{}\n\n".format(
+                self.__fname, content
+            )
+        )
         super().close()
 
 
 class Exporter:
-    '''
+    """
     Interface for all exporters. It is not necessary, but it is useful
     if you don't plan to implement all the methods.
-    '''
+    """
 
     def __init__(self, simulation, outdir=None, dry_run=None, copy_to=None):
         self.simulation = simulation
-        outdir = outdir or os.path.join(os.getcwd(), 'soil_output')
+        outdir = outdir or os.path.join(os.getcwd(), "soil_output")
-        self.outdir = os.path.join(outdir,
+        self.outdir = os.path.join(outdir, simulation.group or "", simulation.name)
-                                   simulation.group or '',
-                                   simulation.name)
         self.dry_run = dry_run
+        if copy_to is None and dry_run:
+            copy_to = sys.stdout
         self.copy_to = copy_to
 
-    def start(self):
+    def sim_start(self):
-        '''Method to call when the simulation starts'''
+        """Method to call when the simulation starts"""
         pass
 
-    def end(self, stats):
+    def sim_end(self):
-        '''Method to call when the simulation ends'''
+        """Method to call when the simulation ends"""
         pass
 
-    def trial(self, env, stats):
+    def trial_start(self, env):
-        '''Method to call when a trial ends'''
+        """Method to call when a trial start"""
         pass
 
-    def output(self, f, mode='w', **kwargs):
+    def trial_end(self, env):
+        """Method to call when a trial ends"""
+        pass
+
+    def output(self, f, mode="w", **kwargs):
         if self.dry_run:
             f = DryRunner(f, copy_to=self.copy_to)
         else:
@@ -76,83 +87,127 @@ class Exporter:
                 pass
         return open_or_reuse(f, mode=mode, **kwargs)
 
+    def get_dfs(self, env):
+        yield from get_dc_dfs(env.datacollector, trial_id=env.id)
+
+
+def get_dc_dfs(dc, trial_id=None):
+    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)
+    if trial_id:
+        for (name, df) in dfs.items():
+            df["trial_id"] = trial_id
+    yield from dfs.items()
+
 
 class default(Exporter):
-    '''Default exporter. Writes sqlite results, as well as the simulation YAML'''
+    """Default exporter. Writes sqlite results, as well as the simulation YAML"""
 
-    def start(self):
+    def sim_start(self):
-        if not self.dry_run:
+        if self.dry_run:
-            logger.info('Dumping results to %s', self.outdir)
+            logger.info("NOT dumping results")
-            self.simulation.dump_yaml(outdir=self.outdir)
+            return
-        else:
+        logger.info("Dumping results to %s", self.outdir)
-            logger.info('NOT dumping results')
+        with self.output(self.simulation.name + ".dumped.yml") as f:
+            f.write(self.simulation.to_yaml())
+        self.dbpath = os.path.join(self.outdir, f"{self.simulation.name}.sqlite")
+        try_backup(self.dbpath, remove=True)
 
-    def trial(self, env, stats):
+    def trial_end(self, env):
-        if not self.dry_run:
+        if self.dry_run:
-            with timer('Dumping simulation {} trial {}'.format(self.simulation.name,
+            logger.info("Running in DRY_RUN mode, the database will NOT be created")
-                                                               env.name)):
+            return
-                with self.output('{}.sqlite'.format(env.name), mode='wb') as f:
-                    env.dump_sqlite(f)
 
-    def end(self, stats):
+        with timer(
-          with timer('Dumping simulation {}\'s stats'.format(self.simulation.name)):
+            "Dumping simulation {} trial {}".format(self.simulation.name, env.id)
-              with self.output('{}.sqlite'.format(self.simulation.name), mode='wb') as f:
+        ):
-                  self.simulation.dump_sqlite(f)
 
+            engine = create_engine(f"sqlite:///{self.dbpath}", echo=False)
+
+            for (t, df) in self.get_dfs(env):
+                df.to_sql(t, con=engine, if_exists="append")
 
 
 class csv(Exporter):
-    '''Export the state of each environment (and its agents) in a separate CSV file'''
-    def trial(self, env, stats):
-        with timer('[CSV] Dumping simulation {} trial {} @ dir {}'.format(self.simulation.name,
-                                                                          env.name,
-                                                                          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:
+    """Export the state of each environment (and its agents) in a separate CSV file"""
-                statwriter = csvlib.writer(f, delimiter='\t', quotechar='"', quoting=csvlib.QUOTE_ALL)
 
-                for stat in stats:
+    def trial_end(self, env):
-                    statwriter.writerow(stat)
+        with timer(
+            "[CSV] Dumping simulation {} trial {} @ dir {}".format(
+                self.simulation.name, env.id, self.outdir
+            )
+        ):
+            for (df_name, df) in self.get_dfs(env):
+                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')
+            logger.info("Not dumping GEXF in dry_run mode")
             return
 
-        with timer('[GEXF] Dumping simulation {} trial {}'.format(self.simulation.name,
+        with timer(
-                                                                  env.name)):
+            "[GEXF] Dumping simulation {} trial {}".format(self.simulation.name, env.id)
-            with self.output('{}.gexf'.format(env.name), mode='wb') as f:
+        ):
-                env.dump_gexf(f)
+            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 sim_start(self):
+        with self.output("dummy", "w") as f:
+            f.write("simulation started @ {}\n".format(current_time()))
 
-    def start(self):
+    def trial_start(self, env):
-        with self.output('dummy', 'w') as f:
+        with self.output("dummy", "w") as f:
-            f.write('simulation started @ {}\n'.format(time.time()))
+            f.write("trial started@ {}\n".format(current_time()))
 
-    def trial(self, env, stats):
+    def trial_end(self, env):
-        with self.output('dummy', 'w') as f:
+        with self.output("dummy", "w") as f:
-            for i in env.history_to_tuples():
+            f.write("trial ended@ {}\n".format(current_time()))
-                f.write(','.join(map(str, i)))
-                f.write('\n')
-
-    def sim(self, stats):
-        with self.output('dummy', 'a') as f:
-            f.write('simulation ended @ {}\n'.format(time.time()))
 
+    def sim_end(self):
+        with self.output("dummy", "a") as f:
+            f.write("simulation ended @ {}\n".format(current_time()))
 
 
 class graphdrawing(Exporter):
-
+    def trial_end(self, env):
-    def trial(self, env, stats):
         # 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))
+        nx.draw(
-        with open('graph-{}.png'.format(env.name)) as f:
+            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.id)) as f:
             f.savefig(f)
 
+
+class summary(Exporter):
+    """Print a summary of each trial to sys.stdout"""
+
+    def trial_end(self, env):
+        for (t, df) in self.get_dfs(env):
+            if not len(df):
+                continue
+            msg = indent(str(df.describe()), "    ")
+            logger.info(
+                dedent(
+                    f"""
+            Dataframe {t}:
+            """
+                )
+                + msg
+            )

soil/network.py

@@ -0,0 +1,83 @@
+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.fixed, config.Topology):
+        cfg = cfg.fixed.dict()
+
+    if isinstance(cfg, str) or isinstance(cfg, dict):
+        return nx.json_graph.node_link_graph(cfg)
+
+    return nx.Graph()
+
+
+def find_unassigned(G, 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.
+    """
+    candidates = list(G.nodes(data=True))
+    if shuffle:
+        random.shuffle(candidates)
+    for next_id, data in candidates:
+        if "agent" not in data:
+            return next_id
+    return None
+
+
+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

@@ -2,58 +2,27 @@ 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
 
 from jinja2 import Template
 
 
-logger = logging.getLogger('soil')
+logger = logging.getLogger("soil")
-
-
-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)
-
-    elif 'generator' in network_params:
-        net_args = network_params.copy()
-        net_gen = net_args.pop('generator')
-
-        if dir_path not in sys.path:
-            sys.path.append(dir_path)
-
-        method = deserializer(net_gen,
-                              known_modules=['networkx.generators',])
-        G = method(**net_args)
-
-    return G
-
-
 
 
 def load_file(infile):
     folder = os.path.dirname(infile)
     if folder not in sys.path:
         sys.path.append(folder)
-    with open(infile, 'r') as f:
+    with open(infile, "r") as f:
         return list(chain.from_iterable(map(expand_template, load_string(f))))
 
 
@@ -62,14 +31,15 @@ def load_string(string):
 
 
 def expand_template(config):
-    if 'template' not in config:
+    if "template" not in config:
         yield config
         return
-    if 'vars' not in config:
+    if "vars" not in config:
-        raise ValueError(('You must provide a definition of variables'
+        raise ValueError(
-                          ' for the template.'))
+            ("You must provide a definition of variables" " for the template.")
+        )
 
-    template = config['template']
+    template = config["template"]
 
     if not isinstance(template, str):
         template = yaml.dump(template)
@@ -81,9 +51,9 @@ def expand_template(config):
     blank_str = template.render({k: 0 for k in params[0].keys()})
     blank = list(load_string(blank_str))
     if len(blank) > 1:
-        raise ValueError('Templates must not return more than one configuration')
+        raise ValueError("Templates must not return more than one configuration")
-    if 'name' in blank[0]:
+    if "name" in blank[0]:
-        raise ValueError('Templates cannot be named, use group instead')
+        raise ValueError("Templates cannot be named, use group instead")
 
     for ps in params:
         string = template.render(ps)
@@ -92,59 +62,64 @@ def expand_template(config):
 
 
 def params_for_template(config):
-    sampler_config = config.get('sampler', {'N': 100})
+    sampler_config = config.get("sampler", {"N": 100})
-    sampler = sampler_config.pop('method', 'SALib.sample.morris.sample')
+    sampler = sampler_config.pop("method", "SALib.sample.morris.sample")
     sampler = deserializer(sampler)
-    bounds = config['vars']['bounds']
+    bounds = config["vars"]["bounds"]
 
     problem = {
-        'num_vars': len(bounds),
+        "num_vars": len(bounds),
-        'names': list(bounds.keys()),
+        "names": list(bounds.keys()),
-        'bounds': list(v for v in bounds.values())
+        "bounds": list(v for v in bounds.values()),
     }
     samples = sampler(problem, **sampler_config)
 
-    lists = config['vars'].get('lists', {})
+    lists = config["vars"].get("lists", {})
     names = list(lists.keys())
     values = list(lists.values())
     combs = list(product(*values))
 
-    allnames = names + problem['names']
+    allnames = names + problem["names"]
-    allvalues = [(list(i[0])+list(i[1])) for i in product(combs, samples)]
+    allvalues = [(list(i[0]) + list(i[1])) for i in product(combs, samples)]
     params = list(map(lambda x: dict(zip(allnames, x)), allvalues))
     return params
 
 
 def load_files(*patterns, **kwargs):
     for pattern in patterns:
-        for i in glob(pattern, **kwargs):
+        for i in glob(pattern, **kwargs, recursive=True):
-            for config in load_file(i):
+            for cfg in load_file(i):
                 path = os.path.abspath(i)
-                if 'dir_path' not in config:
+                yield Config.from_raw(cfg), path
-                    config['dir_path'] = os.path.dirname(path)
-                yield config, path
 
 
-def load_config(config):
+def load_config(cfg):
-    if isinstance(config, dict):
+    if isinstance(cfg, Config):
-        yield config, os.getcwd()
+        yield cfg, os.getcwd()
+    elif isinstance(cfg, dict):
+        yield Config.from_raw(cfg), os.getcwd()
     else:
-        yield from load_files(config)
+        yield from load_files(cfg)
 
 
-builtins = importlib.import_module('builtins')
+builtins = importlib.import_module("builtins")
 
-def name(value, known_modules=[]):
+KNOWN_MODULES = [
-    '''Return a name that can be imported, to serialize/deserialize an object'''
+    "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'
+        return "None"
     if not isinstance(value, type):  # Get the class name first
         value = type(value)
     tname = value.__name__
     if hasattr(builtins, tname):
         return tname
     modname = value.__module__
-    if modname == '__main__':
+    if modname == "__main__":
         return tname
     if known_modules and modname in known_modules:
         return tname
@@ -154,69 +129,100 @@ def name(value, known_modules=[]):
         module = importlib.import_module(kmod)
         if hasattr(module, tname):
             return tname
-    return '{}.{}'.format(modname, tname)
+    return "{}.{}".format(modname, tname)
 
 
 def serializer(type_):
-    if type_ != 'str' and hasattr(builtins, type_):
+    if type_ != "str" and hasattr(builtins, type_):
         return repr
     return lambda x: x
 
 
-def serialize(v, known_modules=[]):
+def serialize(v, known_modules=KNOWN_MODULES):
-    '''Get a text representation of an object.'''
+    """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':
+    if type_ == "str":
-        return lambda x='': x
+        return lambda x="": x
-    if type_ == 'None':
+    if type_ == "None":
         return lambda x=None: None
     if hasattr(builtins, type_):  # Check if it's a builtin type
         cls = getattr(builtins, type_)
         return lambda x=None: ast.literal_eval(x) if x is not None else cls()
+    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
+    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:
         try:
             module = importlib.import_module(modname)
             cls = getattr(module, tname)
-            return getattr(cls, 'deserialize', cls)
+            return getattr(cls, "deserialize", cls)
         except (ImportError, AttributeError) as ex:
             errors.append((modname, tname, ex))
-    raise Exception('Could not find type {}. Tried: {}'.format(type_, errors))
+    raise ValueError('Could not find type "{}". Tried: {}'.format(type_, errors))
 
 
-def deserialize(type_, value=None, **kwargs):
+def deserialize(type_, value=None, globs=None, **kwargs):
-    '''Get an object from a text representation'''
+    """Get an object from a text representation"""
     if not isinstance(type_, str):
         return type_
-    des = deserializer(type_, **kwargs)
+    if globs and type_ in globs:
+        des = globs[type_]
+    else:
+        try:
+            des = deserializer(type_, **kwargs)
+        except ValueError as ex:
+            try:
+                des = eval(type_)
+            except Exception:
+                raise ex
     if value is None:
         return des
     return des(value)
 
 
-def deserialize_all(names, *args, known_modules=['soil'], **kwargs):
+def deserialize_all(names, *args, known_modules=KNOWN_MODULES, **kwargs):
-    '''Return the set of exporters for a simulation, given the exporter names'''
+    """Return the list of deserialized objects"""
-    exporters = []
+    objects = []
     for name in names:
         mod = deserialize(name, known_modules=known_modules)
-        exporters.append(mod(*args, **kwargs))
+        objects.append(mod(*args, **kwargs))
-    return exporters
+    return objects
-

soil/simulation.py

@@ -1,358 +1,268 @@
 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, List
+
+
+from networkx.readwrite import json_graph
+from functools import partial
 import pickle
 
-from . import serialization, utils, basestring, agents
+from . import serialization, exporters, utils, basestring, agents
 from .environment import Environment
-from .utils import logger
+from .utils import logger, run_and_return_exceptions
-from .exporters import default
+from .config import Config, convert_old
-from .stats import defaultStats
 
 
-#TODO: change documentation for simulation
+# 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 not been provided.
     """
 
-    def __init__(self, name=None, group=None, topology=None, network_params=None,
+    version: str = "2"
-                 network_agents=None, agent_type=None, states=None,
+    name: str = "Unnamed simulation"
-                 default_state=None, interval=1, num_trials=1,
+    description: Optional[str] = ""
-                 max_time=100, load_module=None, seed=None,
+    group: str = None
-                 dir_path=None, environment_agents=None,
+    model_class: Union[str, type] = "soil.Environment"
-                 environment_params=None, environment_class=None,
+    model_params: dict = field(default_factory=dict)
-                 **kwargs):
+    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 = 1
+    parallel: Optional[bool] = None
+    exporters: Optional[List[str]] = field(default_factory=list)
+    outdir: Optional[str] = None
+    exporter_params: Optional[Dict[str, Any]] = field(default_factory=dict)
+    dry_run: bool = False
+    extra: Dict[str, Any] = field(default_factory=dict)
 
-        self.load_module = load_module
+    @classmethod
-        self.network_params = network_params
+    def from_dict(cls, env, **kwargs):
-        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
 
-        sys.path += list(x for x in [os.getcwd(), self.dir_path] if x not in sys.path)
+        ignored = {
+            k: v for k, v in env.items() if k not in inspect.signature(cls).parameters
+        }
 
-        if topology is None:
+        d = {k: v for k, v in env.items() if k not in ignored}
-            topology = serialization.load_network(network_params,
+        if ignored:
-                                                  dir_path=self.dir_path)
+            d.setdefault("extra", {}).update(ignored)
-        elif isinstance(topology, basestring) or isinstance(topology, dict):
+        if ignored:
-            topology = json_graph.node_link_graph(topology)
+            logger.warning(f'Ignoring these parameters (added to "extra"): { ignored }')
-        self.topology = nx.Graph(topology)
+        d.update(kwargs)
 
-
+        return cls(**d)
-        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)
 
     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'''
+        """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):
+    def run_gen(
-        if parallel and not os.environ.get('SENPY_DEBUG', None):
+        self,
-            p = Pool()
+        parallel=False,
-            func = partial(self.run_trial_exceptions,
+        dry_run=None,
-                           *args,
+        exporters=None,
-                           **kwargs)
+        outdir=None,
-            for i in p.imap_unordered(func, range(self.num_trials)):
+        exporter_params={},
-                if isinstance(i, Exception):
+        log_level=None,
-                    logger.error('Trial failed:\n\t%s', i.message)
+        **kwargs,
-                    continue
+    ):
-                yield i
+        """Run the simulation and yield the resulting environments."""
-        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,
-                **kwargs):
-        '''Run the simulation and yield the resulting environments.'''
         if log_level:
             logger.setLevel(log_level)
-        logger.info('Using exporters: %s', exporters or [])
+        outdir = outdir or self.outdir
-        logger.info('Output directory: %s', outdir)
+        logger.info("Using exporters: %s", exporters or [])
-        exporters = serialization.deserialize_all(exporters,
+        logger.info("Output directory: %s", outdir)
-                                                  simulation=self,
+        if dry_run is None:
-                                                  known_modules=['soil.exporters',],
+            dry_run = self.dry_run
-                                                  dry_run=dry_run,
+        if exporters is None:
-                                                  outdir=outdir,
+            exporters = self.exporters
-                                                  **exporter_params)
+        if not exporter_params:
-        stats = serialization.deserialize_all(simulation=self,
+            exporter_params = self.exporter_params
-                                              names=stats,
-                                              known_modules=['soil.stats',],
-                                              **stats_params)
 
-        with utils.timer('simulation {}'.format(self.name)):
+        exporters = serialization.deserialize_all(
-            for stat in stats:
+            exporters,
-                stat.start()
+            simulation=self,
+            known_modules=[
+                "soil.exporters",
+            ],
+            dry_run=dry_run,
+            outdir=outdir,
+            **exporter_params,
+        )
 
+        with utils.timer("simulation {}".format(self.name)):
             for exporter in exporters:
-                exporter.start()
+                exporter.sim_start()
-            for env in self._run_sync_or_async(*args,
-                                               parallel=parallel,
-                                               log_level=log_level,
-                                               **kwargs):
 
-                collected = list(stat.trial(env) for stat in stats)
+            for env in utils.run_parallel(
-
+                func=self.run_trial,
-                saved = self.save_stats(collected, t_step=env.now, trial_id=env.name)
+                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)
+                exporter.sim_end()
 
+    def get_env(self, trial_id=0, model_params=None, **kwargs):
+        """Create an environment for a trial of the simulation"""
 
-    def save_stats(self, collection, **kwargs):
+        def deserialize_reporters(reporters):
-        stats = dict(kwargs)
+            for (k, v) in reporters.items():
-        for stat in collection:
+                if isinstance(v, str) and v.startswith("py:"):
-            stats.update(stat)
+                    reporters[k] = serialization.deserialize(v.split(":", 1)[1])
-        self._history.save_stats(utils.flatten_dict(stats))
+            return reporters
-        return stats
 
-    def get_stats(self, **kwargs):
+        params = self.model_params.copy()
-        return self._history.get_stats(**kwargs)
+        if model_params:
+            params.update(model_params)
+        params.update(kwargs)
 
-    def log_stats(self, stats):
+        agent_reporters = deserialize_reporters(params.pop("agent_reporters", {}))
-        logger.info('Stats: \n{}'.format(yaml.dump(stats, default_flow_style=False)))
+        model_reporters = deserialize_reporters(params.pop("model_reporters", {}))
-    
 
-    def get_env(self, trial_id=0, **kwargs):
+        env = serialization.deserialize(self.model_class)
-        '''Create an environment for a trial of the simulation'''
+        return env(
-        opts = self.environment_params.copy()
+            id=f"{self.name}_trial_{trial_id}",
-        opts.update({
+            seed=f"{self.seed}_trial_{trial_id}",
-            'name': trial_id,
+            dir_path=self.dir_path,
-            'topology': self.topology.copy(),
+            agent_reporters=agent_reporters,
-            'network_params': self.network_params,
+            model_reporters=model_reporters,
-            'seed': '{}_trial_{}'.format(self.seed, trial_id),
+            **params,
-            '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 run_trial(self, until=None, log_level=logging.INFO, **opts):
+    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
+        model = self.get_env(trial_id, **opts)
-        until = until or self.max_time
+        trial_id = trial_id if trial_id is not None else current_time()
-        env = self.get_env(trial_id=trial_id, **opts)
+        with utils.timer("Simulation {} trial {}".format(self.name, trial_id)):
-        # Set up agents on nodes
+            return self.run_model(
-        with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)):
+                model=model, trial_id=trial_id, until=until, log_level=log_level
-            env.run(until)
+            )
-        return env
 
-    def run_trial_exceptions(self, *args, **kwargs):
+    def run_model(self, model, until=None, **opts):
-        '''
+        # Set-up trial environment and graph
-        A wrapper for run_trial that catches exceptions and returns them.
+        until = float(until or self.max_time or "inf")
-        It is meant for async simulations
+
-        '''
+        # Set up agents on nodes
-        try:
+        def is_done():
-            return self.run_trial(*args, **kwargs)
+            return not model.running
-        except Exception as ex:
+
-            if ex.__cause__ is not None:
+        if until and hasattr(model.schedule, "time"):
-                ex = ex.__cause__
+            prev = is_done
-            ex.message = ''.join(traceback.format_exception(type(ex), ex, ex.__traceback__)[:])
+
-            return ex
+            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) }
+
+  Topology size: { len(model.G) if hasattr(model, "G") else 0 }
+        """
+            )
+        )
+
+        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()
+
+        if (
+            model.schedule.time < until
+        ):  # Simulation ended (no more steps) before the expected time
+            model.schedule.time = until
+        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):
+def iter_from_config(*cfgs, **kwargs):
-        if not f and not outdir:
+    for config in cfgs:
-            raise ValueError('specify a file or an output directory')
+        configs = list(serialization.load_config(config))
-
+        for config, path in configs:
-        if not f:
+            d = dict(config)
-            f = os.path.join(outdir, '{}.dumped.yml'.format(self.name))
+            if "dir_path" not in d:
-
+                d["dir_path"] = os.path.dirname(path)
-        with utils.open_or_reuse(f, 'w') as f:
+            yield Simulation.from_dict(d, **kwargs)
-            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 from_config(conf_or_path):
-    config = list(serialization.load_config(conf_or_path))
+    lst = list(iter_from_config(conf_or_path))
-    if len(config) > 1:
+    if len(lst) > 1:
-        raise AttributeError('Provide only one configuration')
+        raise AttributeError("Provide only one configuration")
-    config = config[0][0]
+    return lst[0]
-    sim = Simulation(**config)
-    return sim
 
 
 def run_from_config(*configs, **kwargs):
-    for config_def in configs:
+    for sim in iter_from_config(*configs):
-        # logger.info("Found {} config(s)".format(len(ls)))
+        logger.info(f"Using config(s): {sim.name}")
-        for config, path in serialization.load_config(config_def):
+        sim.run_simulation(**kwargs)
-            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)

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

@@ -2,27 +2,89 @@ from mesa.time import BaseScheduler
 from queue import Empty
 from heapq import heappush, heappop
 import math
+
+from inspect import getsource
+from numbers import Number
+from textwrap import dedent
+
 from .utils import logger
-from mesa import Agent
+from mesa import Agent as MesaAgent
+
+
+INFINITY = float("inf")
+
+
+class DeadAgent(Exception):
+    pass
 
 
 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
 
+    def schedule_next(self, time, delta, first=False):
+        return (self._time, None)
 
-class Delta:
+
+NEVER = When(INFINITY)
+
+
+class Delta(When):
     def __init__(self, delta):
         self._delta = delta
 
-    def __eq__(self, other):
-        return self._delta == other._delta
-
     def abs(self, time):
-        return time + self._delta
+        return self._time + self._delta
+
+    def __eq__(self, other):
+        if isinstance(other, Delta):
+            return self._delta == other._delta
+        return False
+
+    def schedule_next(self, time, delta, first=False):
+        return (time + self._delta, None)
+
+    def __repr__(self):
+        return str(f"Delta({self._delta})")
+
+
+class BaseCond:
+    def __init__(self, msg=None, delta=None, eager=False):
+        self._msg = msg
+        self._delta = delta
+        self.eager = eager
+
+    def schedule_next(self, time, delta, first=False):
+        if first and self.eager:
+            return (time, self)
+        if self._delta:
+            delta = self._delta
+        return (time + delta, self)
+
+    def return_value(self, agent):
+        return None
+
+    def __repr__(self):
+        return self._msg or self.__class__.__name__
+
+
+class Cond(BaseCond):
+    def __init__(self, func, *args, **kwargs):
+        self._func = func
+        super().__init__(*args, **kwargs)
+
+    def ready(self, agent, time):
+        return self._func(agent)
+
+    def __repr__(self):
+        if self._msg:
+            return self._msg
+        return str(f'Cond("{dedent(getsource(self._func)).strip()}")')
 
 
 class TimedActivation(BaseScheduler):
@@ -30,58 +92,115 @@ class TimedActivation(BaseScheduler):
     In each activation, each agent will update its 'next_time'.
     """
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, *args, shuffle=True, **kwargs):
-        super().__init__(self)
+        super().__init__(*args, **kwargs)
+        self._next = {}
         self._queue = []
-        self.next_time = 0
+        self._shuffle = shuffle
+        self.step_interval = 1
+        self.logger = logger.getChild(f"time_{ self.model }")
 
-    def add(self, agent: Agent):
+    def add(self, agent: MesaAgent, when=None):
-        if agent.unique_id not in self._agents:
+        if when is None:
-            heappush(self._queue, (self.time, agent.unique_id))
+            when = self.time
-            super().add(agent)
+        elif isinstance(when, When):
+            when = when.abs()
 
-    def step(self, until: float =float('inf')) -> None:
+        self._schedule(agent, None, when)
+        super().add(agent)
+
+    def _schedule(self, agent, condition=None, when=None):
+        if condition:
+            if not when:
+                when, condition = condition.schedule_next(
+                    when or self.time, self.step_interval
+                )
+        else:
+            if when is None:
+                when = self.time + self.step_interval
+            condition = None
+        if self._shuffle:
+            key = (when, self.model.random.random(), condition)
+        else:
+            key = (when, agent.unique_id, condition)
+        self._next[agent.unique_id] = key
+        heappush(self._queue, (key, agent))
+
+    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
+        self.logger.debug(f"Simulation step {self.time}")
-        agent_id = None
+        if not self.model.running:
-        unsched = []
+            return
-        until = until or float('inf')
+
+        self.logger.debug(f"Queue length: {len(self._queue)}")
+
+        while self._queue:
+            ((when, _id, cond), agent) = self._queue[0]
+            if when > self.time:
+                break
+
+            heappop(self._queue)
+            if cond:
+                if not cond.ready(agent, self.time):
+                    self._schedule(agent, cond)
+                    continue
+                try:
+                    agent._last_return = cond.return_value(agent)
+                except Exception as ex:
+                    agent._last_except = ex
+            else:
+                agent._last_return = None
+                agent._last_except = None
+
+            self.logger.debug(f"Stepping agent {agent}")
+            self._next.pop(agent.unique_id, None)
+
+            try:
+                returned = agent.step()
+            except DeadAgent:
+                agent.alive = False
+                continue
+
+            # Check status for MESA agents
+            if not getattr(agent, "alive", True):
+                continue
+
+            if returned:
+                next_check = returned.schedule_next(
+                    self.time, self.step_interval, first=True
+                )
+                self._schedule(agent, when=next_check[0], condition=next_check[1])
+            else:
+                next_check = (self.time + self.step_interval, None)
+
+                self._schedule(agent)
+
+        self.steps += 1
 
         if not self._queue:
-            self.time = until
+            self.time = INFINITY
-            self.next_time = float('inf')
+            self.model.running = False
-            return
+            return self.time
 
-        (when, agent_id) = self._queue[0]
+        next_time = self._queue[0][0][0]
+        if next_time < self.time:
+            raise Exception(
+                f"An agent has been scheduled for a time in the past, there is probably an error ({when} < {self.time})"
+            )
+        self.logger.debug(f"Updating time step: {self.time} -> {next_time}")
 
-        if until and when > until:
+        self.time = next_time
-            self.time = until
-            self.next_time = when
-            return
 
-        self.time = when
-        next_time = float("inf")
 
-        while when == self.time:
+class ShuffledTimedActivation(TimedActivation):
-            heappop(self._queue)
+    def __init__(self, *args, **kwargs):
-            logger.debug(f'Stepping agent {agent_id}')
+        super().__init__(*args, shuffle=True, **kwargs)
-            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:
+class OrderedTimedActivation(TimedActivation):
-            raise Exception("Invalid scheduling time")
+    def __init__(self, *args, **kwargs):
-
+        super().__init__(*args, shuffle=False, **kwargs)
-        self.next_time = next_time
-        self.steps += 1

soil/utils.py

@@ -1,71 +1,106 @@
 import logging
-import time
+from time import time as current_time, strftime, gmtime, localtime
 import os
+import traceback
 
-from shutil import copyfile
+from functools import partial
+from shutil import copyfile, move
+from multiprocessing import Pool
 
 from contextlib import contextmanager
 
-logger = logging.getLogger('soil')
+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)
+
+logging.basicConfig(
+    level=logging.INFO,
+    handlers=[
+        consoleHandler,
+    ],
+)
 
 
 @contextmanager
-def timer(name='task', pre="", function=logger.info, to_object=None):
+def timer(name="task", pre="", function=logger.info, to_object=None):
-    start = time.time()
+    start = current_time()
-    function('{}Starting {} at {}.'.format(pre, name,
+    function("{}Starting {} at {}.".format(pre, name, strftime("%X", gmtime(start))))
-                                           time.strftime("%X", time.gmtime(start))))
     yield start
-    end = time.time()
+    end = current_time()
-    function('{}Finished {} at {} in {} seconds'.format(pre, name,
+    function(
-                                                        time.strftime("%X", time.gmtime(end)),
+        "{}Finished {} at {} in {} seconds".format(
-                                                        str(end-start)))
+            pre, name, strftime("%X", gmtime(end)), str(end - start)
+        )
+    )
     if to_object:
         to_object.start = start
         to_object.end = end
 
 
-
+def try_backup(path, remove=False):
-
+    if not os.path.exists(path):
-def safe_open(path, mode='r', backup=True, **kwargs):
+        return None
     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)
-        creation = os.path.getctime(path)
+    stamp = strftime("%Y-%m-%d_%H.%M.%S", localtime(creation))
-        stamp = time.strftime('%Y-%m-%d_%H.%M.%S', time.localtime(creation))
 
-        backup_dir = os.path.join(outdir, 'backup')
+    backup_dir = os.path.join(outdir, "backup")
-        if not os.path.exists(backup_dir):
+    if not os.path.exists(backup_dir):
-            os.makedirs(backup_dir)
+        os.makedirs(backup_dir)
-        newpath = os.path.join(backup_dir, '{}@{}'.format(os.path.basename(path),
+    newpath = os.path.join(backup_dir, "{}@{}".format(os.path.basename(path), stamp))
-                                                               stamp))
+    if move:
+        move(path, newpath)
+    else:
         copyfile(path, newpath)
+    return newpath
+
+
+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:
+        try_backup(path)
     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:
-    except (AttributeError, TypeError):
+            yield f
-        return f
+    except (AttributeError, TypeError) as ex:
+        yield f
+
 
 def flatten_dict(d):
     if not isinstance(d, dict):
         return d
     return dict(_flatten_dict(d))
 
-def _flatten_dict(d, prefix=''):
+
+def _flatten_dict(d, prefix=""):
     if not isinstance(d, dict):
         # print('END:', prefix, d)
         yield prefix, d
         return
     if prefix:
-        prefix = prefix + '.'
+        prefix = prefix + "."
     for k, v in d.items():
         # print(k, v)
-        res = list(_flatten_dict(v, prefix='{}{}'.format(prefix, k)))
+        res = list(_flatten_dict(v, prefix="{}{}".format(prefix, k)))
         # print('RES:', res)
         yield from res
 
@@ -77,7 +112,7 @@ def unflatten_dict(d):
         if not isinstance(k, str):
             target[k] = v
             continue
-        tokens = k.split('.')
+        tokens = k.split(".")
         if len(tokens) < 2:
             target[k] = v
             continue
@@ -87,3 +122,33 @@ 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/version.py

@@ -4,7 +4,7 @@ import logging
 logger = logging.getLogger(__name__)
 
 ROOT = os.path.dirname(__file__)
-DEFAULT_FILE = os.path.join(ROOT, 'VERSION')
+DEFAULT_FILE = os.path.join(ROOT, "VERSION")
 
 
 def read_version(versionfile=DEFAULT_FILE):
@@ -12,9 +12,10 @@ def read_version(versionfile=DEFAULT_FILE):
         with open(versionfile) as f:
             return f.read().strip()
     except IOError:  # pragma: no cover
-        logger.error(('Running an unknown version of {}.'
+        logger.error(
-                      'Be careful!.').format(__name__))
+            ("Running an unknown version of {}." "Be careful!.").format(__name__)
-        return '0.0'
+        )
+        return "0.0"
 
 
 __version__ = read_version()

soil/visualization.py

@@ -1,5 +1,6 @@
 from mesa.visualization.UserParam import UserSettableParameter
 
+
 class UserSettableParameter(UserSettableParameter):
     def __str__(self):
         return self.value

soil/web/__init__.py

@@ -20,6 +20,7 @@ from tornado.concurrent import run_on_executor
 from concurrent.futures import ThreadPoolExecutor
 
 from ..simulation import Simulation
+
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
@@ -31,140 +32,183 @@ LOGGING_INTERVAL = 0.5
 # Workaround to let Soil load the required modules
 sys.path.append(ROOT)
 
+
 class PageHandler(tornado.web.RequestHandler):
-    """ Handler for the HTML template which holds the visualization. """
+    """Handler for the HTML template which holds the visualization."""
 
     def get(self):
-        self.render('index.html', port=self.application.port,
+        self.render(
-                    name=self.application.name)
+            "index.html", port=self.application.port, name=self.application.name
+        )
 
 
 class SocketHandler(tornado.websocket.WebSocketHandler):
-    """ Handler for websocket. """
+    """Handler for websocket."""
+
     executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)
 
     def open(self):
         if self.application.verbose:
-            logger.info('Socket opened!')
+            logger.info("Socket opened!")
 
     def check_origin(self, origin):
         return True
 
     def on_message(self, message):
-        """ Receiving a message from the websocket, parse, and act accordingly. """
+        """Receiving a message from the websocket, parse, and act accordingly."""
 
         msg = tornado.escape.json_decode(message)
 
-        if msg['type'] == 'config_file':
+        if msg["type"] == "config_file":
 
             if self.application.verbose:
-                print(msg['data'])
+                print(msg["data"])
 
-            self.config = list(yaml.load_all(msg['data']))
+            self.config = list(yaml.load_all(msg["data"]))
 
             if len(self.config) > 1:
-                error = 'Please, provide only one configuration.'
+                error = "Please, provide only one configuration."
                 if self.application.verbose:
                     logger.error(error)
-                self.write_message({'type': 'error',
+                self.write_message({"type": "error", "error": error})
-                    'error': error})
                 return
 
             self.config = self.config[0]
-            self.send_log('INFO.' + self.simulation_name,
+            self.send_log(
-                          'Using config: {name}'.format(name=self.config['name']))
+                "INFO." + self.simulation_name,
+                "Using config: {name}".format(name=self.config["name"]),
+            )
 
-            if 'visualization_params' in self.config:
+            if "visualization_params" in self.config:
-                self.write_message({'type': 'visualization_params',
+                self.write_message(
-                    'data': self.config['visualization_params']})
+                    {
-            self.name = self.config['name']
+                        "type": "visualization_params",
+                        "data": self.config["visualization_params"],
+                    }
+                )
+            self.name = self.config["name"]
             self.run_simulation()
 
             settings = []
-            for key in self.config['environment_params']: 
+            for key in self.config["environment_params"]:
-                if type(self.config['environment_params'][key]) == float or type(self.config['environment_params'][key]) == int:
+                if (
-                    if self.config['environment_params'][key] <= 1:
+                    type(self.config["environment_params"][key]) == float
-                        setting_type = 'number'
+                    or type(self.config["environment_params"][key]) == int
+                ):
+                    if self.config["environment_params"][key] <= 1:
+                        setting_type = "number"
                     else:
-                        setting_type = 'great_number'
+                        setting_type = "great_number"
-                elif type(self.config['environment_params'][key]) == bool:
+                elif type(self.config["environment_params"][key]) == bool:
-                    setting_type = 'boolean'
+                    setting_type = "boolean"
                 else:
-                    setting_type = 'undefined'
+                    setting_type = "undefined"
 
-                settings.append({
+                settings.append(
-                    'label': key,
+                    {
-                    'type': setting_type,
+                        "label": key,
-                    'value': self.config['environment_params'][key]
+                        "type": setting_type,
-                })
+                        "value": self.config["environment_params"][key],
+                    }
+                )
 
-            self.write_message({'type': 'settings',
+            self.write_message({"type": "settings", "data": settings})
-                'data': settings})
 
-        elif msg['type'] == 'get_trial':
+        elif msg["type"] == "get_trial":
             if self.application.verbose:
-                logger.info('Trial {} requested!'.format(msg['data']))
+                logger.info("Trial {} requested!".format(msg["data"]))
-            self.send_log('INFO.' + __name__, 'Trial {} requested!'.format(msg['data']))
+            self.send_log("INFO." + __name__, "Trial {} requested!".format(msg["data"]))
-            self.write_message({'type': 'get_trial',
+            self.write_message(
-                                'data': self.get_trial(int(msg['data']))})
+                {"type": "get_trial", "data": self.get_trial(int(msg["data"]))}
+            )
 
-        elif msg['type'] == 'run_simulation':
+        elif msg["type"] == "run_simulation":
             if self.application.verbose:
-                logger.info('Running new simulation for {name}'.format(name=self.config['name']))
+                logger.info(
-            self.send_log('INFO.' + self.simulation_name, 'Running new simulation for {name}'.format(name=self.config['name']))
+                    "Running new simulation for {name}".format(name=self.config["name"])
-            self.config['environment_params'] = msg['data']
+                )
+            self.send_log(
+                "INFO." + self.simulation_name,
+                "Running new simulation for {name}".format(name=self.config["name"]),
+            )
+            self.config["environment_params"] = msg["data"]
             self.run_simulation()
 
-        elif msg['type'] == 'download_gexf':
+        elif msg["type"] == "download_gexf":
-            G = self.trials[ int(msg['data']) ].history_to_graph()
+            G = self.trials[int(msg["data"])].history_to_graph()
             for node in G.nodes():
-                if 'pos' in G.nodes[node]:
+                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}}
+                    G.nodes[node]["viz"] = {
-                    del (G.nodes[node]['pos'])
+                        "position": {
-            writer = nx.readwrite.gexf.GEXFWriter(version='1.2draft')
+                            "x": G.nodes[node]["pos"][0],
+                            "y": G.nodes[node]["pos"][1],
+                            "z": 0.0,
+                        }
+                    }
+                    del G.nodes[node]["pos"]
+            writer = nx.readwrite.gexf.GEXFWriter(version="1.2draft")
             writer.add_graph(G)
-            self.write_message({'type': 'download_gexf',
+            self.write_message(
-                'filename': self.config['name'] + '_trial_' + str(msg['data']),
+                {
-                'data': tostring(writer.xml).decode(writer.encoding) })
+                    "type": "download_gexf",
+                    "filename": self.config["name"] + "_trial_" + str(msg["data"]),
+                    "data": tostring(writer.xml).decode(writer.encoding),
+                }
+            )
 
-        elif msg['type'] == 'download_json':
+        elif msg["type"] == "download_json":
-            G = self.trials[ int(msg['data']) ].history_to_graph()
+            G = self.trials[int(msg["data"])].history_to_graph()
             for node in G.nodes():
-                if 'pos' in G.nodes[node]:
+                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}}
+                    G.nodes[node]["viz"] = {
-                    del (G.nodes[node]['pos'])
+                        "position": {
-            self.write_message({'type': 'download_json',
+                            "x": G.nodes[node]["pos"][0],
-                'filename': self.config['name'] + '_trial_' + str(msg['data']),
+                            "y": G.nodes[node]["pos"][1],
-                'data': nx.node_link_data(G) })
+                            "z": 0.0,
+                        }
+                    }
+                    del G.nodes[node]["pos"]
+            self.write_message(
+                {
+                    "type": "download_json",
+                    "filename": self.config["name"] + "_trial_" + str(msg["data"]),
+                    "data": nx.node_link_data(G),
+                }
+            )
 
         else:
             if self.application.verbose:
-                logger.info('Unexpected message!')
+                logger.info("Unexpected message!")
 
     def update_logging(self):
         try:
-            if (not self.log_capture_string.closed and self.log_capture_string.getvalue()):
+            if (
-                for i in range(len(self.log_capture_string.getvalue().split('\n')) - 1):
+                not self.log_capture_string.closed
-                    self.send_log('INFO.' + self.simulation_name, self.log_capture_string.getvalue().split('\n')[i])
+                and self.log_capture_string.getvalue()
+            ):
+                for i in range(len(self.log_capture_string.getvalue().split("\n")) - 1):
+                    self.send_log(
+                        "INFO." + self.simulation_name,
+                        self.log_capture_string.getvalue().split("\n")[i],
+                    )
                 self.log_capture_string.truncate(0)
                 self.log_capture_string.seek(0)
         finally:
             if self.capture_logging:
-                tornado.ioloop.IOLoop.current().call_later(LOGGING_INTERVAL, self.update_logging)
+                tornado.ioloop.IOLoop.current().call_later(
-
+                    LOGGING_INTERVAL, self.update_logging
+                )
 
     def on_close(self):
         if self.application.verbose:
-            logger.info('Socket closed!')
+            logger.info("Socket closed!")
 
     def send_log(self, logger, logging):
-        self.write_message({'type': 'log',
+        self.write_message({"type": "log", "logger": logger, "logging": logging})
-                            'logger': logger,
-                            'logging': logging})
 
     @property
     def simulation_name(self):
-        return self.config.get('name', 'NoSimulationRunning')
+        return self.config.get("name", "NoSimulationRunning")
 
     @run_on_executor
     def nonblocking(self, config):
@@ -174,28 +218,31 @@ class SocketHandler(tornado.websocket.WebSocketHandler):
     @tornado.gen.coroutine
     def run_simulation(self):
         # Run simulation and capture logs
-        logger.info('Running simulation!')
+        logger.info("Running simulation!")
-        if 'visualization_params' in self.config:
+        if "visualization_params" in self.config:
-            del self.config['visualization_params']
+            del self.config["visualization_params"]
         with self.logging(self.simulation_name):
             try:
                 config = dict(**self.config)
-                config['outdir'] = os.path.join(self.application.outdir, config['name'])
+                config["outdir"] = os.path.join(self.application.outdir, config["name"])
-                config['dump'] = self.application.dump
+                config["dump"] = self.application.dump
                 self.trials = yield self.nonblocking(config)
 
-                self.write_message({'type': 'trials',
+                self.write_message(
-                    'data': list(trial.name for trial in self.trials) })
+                    {
+                        "type": "trials",
+                        "data": list(trial.name for trial in self.trials),
+                    }
+                )
             except Exception as ex:
-                error = 'Something went wrong:\n\t{}'.format(ex)
+                error = "Something went wrong:\n\t{}".format(ex)
                 logging.info(error)
-                self.write_message({'type': 'error',
+                self.write_message({"type": "error", "error": error})
-                                    'error': error})
+                self.send_log("ERROR." + self.simulation_name, error)
-                self.send_log('ERROR.' + self.simulation_name, error)
 
     def get_trial(self, trial):
-        logger.info('Available trials: %s ' % len(self.trials))
+        logger.info("Available trials: %s " % len(self.trials))
-        logger.info('Ask for : %s' % trial)
+        logger.info("Ask for : %s" % trial)
         trial = self.trials[trial]
         G = trial.history_to_graph()
         return nx.node_link_data(G)
@@ -215,25 +262,28 @@ class SocketHandler(tornado.websocket.WebSocketHandler):
         self.logger_application.removeHandler(ch)
         self.capture_logging = False
         return self.capture_logging
-    
+
 
 class ModularServer(tornado.web.Application):
-    """ Main visualization application. """
+    """Main visualization application."""
 
     port = 8001
-    page_handler = (r'/', PageHandler)
+    page_handler = (r"/", PageHandler)
-    socket_handler = (r'/ws', SocketHandler)
+    socket_handler = (r"/ws", SocketHandler)
-    static_handler = (r'/(.*)', tornado.web.StaticFileHandler,
+    static_handler = (
-                      {'path': os.path.join(ROOT, 'static')})
+        r"/(.*)",
-    local_handler = (r'/local/(.*)', tornado.web.StaticFileHandler,
+        tornado.web.StaticFileHandler,
-                     {'path': ''})
+        {"path": os.path.join(ROOT, "static")},
+    )
+    local_handler = (r"/local/(.*)", tornado.web.StaticFileHandler, {"path": ""})
 
     handlers = [page_handler, socket_handler, static_handler, local_handler]
-    settings = {'debug': True,
+    settings = {"debug": True, "template_path": ROOT + "/templates"}
-                'template_path': ROOT + '/templates'}
+
+    def __init__(
+        self, dump=False, outdir="output", name="SOIL", verbose=True, *args, **kwargs
+    ):
 
-    def __init__(self, dump=False, outdir='output', name='SOIL', verbose=True, *args, **kwargs):
-        
         self.verbose = verbose
         self.name = name
         self.dump = dump
@@ -243,12 +293,12 @@ class ModularServer(tornado.web.Application):
         super().__init__(self.handlers, **self.settings)
 
     def launch(self, port=None):
-        """ Run the app. """
+        """Run the app."""
-        
+
         if port is not None:
             self.port = port
-        url = 'http://127.0.0.1:{PORT}'.format(PORT=self.port)
+        url = "http://127.0.0.1:{PORT}".format(PORT=self.port)
-        print('Interface starting at {url}'.format(url=url))
+        print("Interface starting at {url}".format(url=url))
         self.listen(self.port)
         # webbrowser.open(url)
         tornado.ioloop.IOLoop.instance().start()
@@ -263,12 +313,22 @@ def run(*args, **kwargs):
 def main():
     import argparse
 
-    parser = argparse.ArgumentParser(description='Visualization of a Graph Model')
+    parser = argparse.ArgumentParser(description="Visualization of a Graph Model")
 
-    parser.add_argument('--name', '-n', nargs=1, default='SOIL', help='name of the simulation')
+    parser.add_argument(
-    parser.add_argument('--dump', '-d', help='dumping results in folder output', action='store_true')
+        "--name", "-n", nargs=1, default="SOIL", help="name of the simulation"
-    parser.add_argument('--port', '-p', nargs=1, default=8001, help='port for launching the server')
+    )
-    parser.add_argument('--verbose', '-v', help='verbose mode', action='store_true')
+    parser.add_argument(
+        "--dump", "-d", help="dumping results in folder output", action="store_true"
+    )
+    parser.add_argument(
+        "--port", "-p", nargs=1, default=8001, help="port for launching the server"
+    )
+    parser.add_argument("--verbose", "-v", help="verbose mode", action="store_true")
     args = parser.parse_args()
 
-    run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose)
+    run(
+        name=args.name,
+        port=(args.port[0] if isinstance(args.port, list) else args.port),
+        verbose=args.verbose,
+    )

soil/web/__main__.py

@@ -2,4 +2,4 @@ from . import main
 
 
 if __name__ == "__main__":
-    main()
+    main()

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

soil/web/run.py

@@ -4,20 +4,33 @@ from simulator import Simulator
 
 
 def run(simulator, name="SOIL", port=8001, verbose=False):
-    server = ModularServer(simulator, name=(name[0] if isinstance(name, list) else name), verbose=verbose)
+    server = ModularServer(
+        simulator, name=(name[0] if isinstance(name, list) else name), verbose=verbose
+    )
     server.port = port
     server.launch()
 
 
 if __name__ == "__main__":
 
-    parser = argparse.ArgumentParser(description='Visualization of a Graph Model')
+    parser = argparse.ArgumentParser(description="Visualization of a Graph Model")
 
-    parser.add_argument('--name', '-n', nargs=1, default='SOIL', help='name of the simulation')
+    parser.add_argument(
-    parser.add_argument('--dump', '-d', help='dumping results in folder output', action='store_true')
+        "--name", "-n", nargs=1, default="SOIL", help="name of the simulation"
-    parser.add_argument('--port', '-p', nargs=1, default=8001, help='port for launching the server')
+    )
-    parser.add_argument('--verbose', '-v', help='verbose mode', action='store_true')
+    parser.add_argument(
+        "--dump", "-d", help="dumping results in folder output", action="store_true"
+    )
+    parser.add_argument(
+        "--port", "-p", nargs=1, default=8001, help="port for launching the server"
+    )
+    parser.add_argument("--verbose", "-v", help="verbose mode", action="store_true")
     args = parser.parse_args()
 
     soil = Simulator(dump=args.dump)
-    run(soil, name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose)
+    run(
+        soil,
+        name=args.name,
+        port=(args.port[0] if isinstance(args.port, list) else args.port),
+        verbose=args.verbose,
+    )

test-requirements.txt

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

tests/complete_converted.yml

@@ -0,0 +1,49 @@
+---
+version: '2'
+name: simple
+group: tests
+dir_path: "/tmp/"
+num_trials: 3
+max_time: 100
+interval: 1
+seed: "CompleteSeed!"
+model_class: Environment
+model_params:
+  topology:
+    params:
+      generator: complete_graph
+      n: 4
+  agents:
+    agent_class: CounterModel
+    state:
+      group: network
+      times: 1
+    topology: true
+    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: false
+        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 +1,163 @@
+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 TestAgents(TestCase):
+    def test_die_returns_infinity(self):
+        """The last step of a dead agent should return time.INFINITY"""
+        d = Dead(unique_id=0, model=environment.Environment())
+        ret = d.step()
+        assert ret == stime.NEVER
+
+    def test_die_raises_exception(self):
+        """A dead agent should raise an exception if it is stepped after death"""
+        d = Dead(unique_id=0, model=environment.Environment())
+        d.step()
+        with pytest.raises(stime.DeadAgent):
+            d.step()
+
+    def test_agent_generator(self):
+        """
+        The step function of an agent could be a generator. In that case, the state of the
+        agent will be resumed after every call to step.
+        """
+        a = 0
+
+        class Gen(agents.BaseAgent):
+            def step(self):
+                nonlocal a
+                for i in range(5):
+                    yield
+                    a += 1
+
+        e = environment.Environment()
+        g = Gen(model=e, unique_id=e.next_id())
+        e.schedule.add(g)
+
+        for i in range(5):
+            e.step()
+            assert a == i
+
+    def test_state_decorator(self):
+        class MyAgent(agents.FSM):
+            run = 0
+
+            @agents.default_state
+            @agents.state("original")
+            def root(self):
+                self.run += 1
+                return self.other
+
+            @agents.state
+            def other(self):
+                self.run += 1
+
+        e = environment.Environment()
+        a = MyAgent(model=e, unique_id=e.next_id())
+        a.step()
+        assert a.run == 1
+        a.step()
+
+    def test_broadcast(self):
+        """
+        An agent should be able to broadcast messages to every other agent, AND each receiver should be able
+        to process it
+        """
+
+        class BCast(agents.Evented):
+            pings_received = 0
+
+            def step(self):
+                print(self.model.broadcast)
+                try:
+                    self.model.broadcast("PING")
+                except Exception as ex:
+                    print(ex)
+                while True:
+                    self.check_messages()
+                    yield
+
+            def on_receive(self, msg, sender=None):
+                self.pings_received += 1
+
+        e = environment.EventedEnvironment()
+
+        for i in range(10):
+            e.add_agent(agent_class=BCast)
+        e.step()
+        pings_received = lambda: [a.pings_received for a in e.agents]
+        assert sorted(pings_received()) == list(range(1, 11))
+        e.step()
+        assert all(x == 10 for x in pings_received())
+
+    def test_ask_messages(self):
+        """
+        An agent should be able to ask another agent, and wait for a response.
+        """
+
+        # There are two agents, they try to send pings
+        # This is arguably a very contrived example. In practice, the or
+        # There should be a delay of one step between agent 0 and 1
+        # On the first step:
+        #   Agent 0 sends a PING, but blocks before a PONG
+        #   Agent 1 detects the PING, responds with a PONG, and blocks after its own PING
+        # After that step, every agent can both receive (there are pending messages) and send.
+        # In each step, for each agent, one message is sent, and another one is received
+        # (although not necessarily in that order).
+
+        # Results depend on ordering (agents are normally shuffled)
+        # so we force the timedactivation not to be shuffled
+
+        pings = []
+        pongs = []
+        responses = []
+
+        class Ping(agents.EventedAgent):
+            def step(self):
+                target_id = (self.unique_id + 1) % self.count_agents()
+                target = self.model.agents[target_id]
+                print("starting")
+                while True:
+                    if pongs or not pings:  # First agent, or anyone after that
+                        pings.append(self.now)
+                        response = yield target.ask("PING")
+                        responses.append(response)
+                    else:
+                        print("NOT sending ping")
+                    print("Checking msgs")
+                    # Do not block if we have already received a PING
+                    if not self.check_messages():
+                        yield self.received()
+                print("done")
+
+            def on_receive(self, msg, sender=None):
+                if msg == "PING":
+                    pongs.append(self.now)
+                    return "PONG"
+                raise Exception("This should never happen")
+
+        e = environment.EventedEnvironment(schedule_class=stime.OrderedTimedActivation)
+        for i in range(2):
+            e.add_agent(agent_class=Ping)
+        assert e.now == 0
+
+        for i in range(5):
+            e.step()
+            time = i + 1
+            assert e.now == time
+            assert len(pings) == 2 * time
+            assert len(pongs) == (2 * time) - 1
+            # Every step between 0 and t appears twice
+            assert sum(pings) == sum(range(time)) * 2
+            # It is the same as pings, without the leading 0
+            assert sum(pongs) == sum(range(time)) * 2

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 +1,147 @@
+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.G.nodes) == 2
+        assert len(env.G.edges) == 1
+        assert len(env.agents) == 2
+        assert env.agents[0].G == env.G
+
+    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.G.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

@@ -2,51 +2,54 @@ 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')
+EXAMPLES = join(ROOT, "..", "examples")
 
-FORCE_TESTS = os.environ.get('FORCE_TESTS', '')
+FORCE_TESTS = os.environ.get("FORCE_TESTS", "")
 
 
 class TestExamples(TestCase):
     pass
 
 
-def make_example_test(path, config):
+def make_example_test(path, cfg):
     def wrapped(self):
         root = os.getcwd()
-        for s in simulation.all_from_config(path):
+        for s in simulation.iter_from_config(cfg):
-            iterations = s.max_time * s.num_trials
+            iterations = s.max_steps * s.num_trials
-            if iterations > 1000:
+            if iterations < 0 or iterations > 1000:
-                s.max_time = 100
+                s.max_steps = 100
                 s.num_trials = 1
-            if config.get('skip_test', False) and not FORCE_TESTS:
+            assert isinstance(cfg, config.Config)
-                self.skipTest('Example ignored.')
+            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"),
-            join(EXAMPLES, '*.yml'),
     ):
-        p = make_example_test(path=path, config=config)
+        if "soil_output" in path:
+            continue
+        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.__name__ = "test_example_file_%s" % fname
-        p.__doc__ = '%s should be a valid configuration' % fname
+        p.__doc__ = "%s should be a valid configuration" % fname
         setattr(TestExamples, p.__name__, p)
         del p
 

tests/test_exporters.py

@@ -2,13 +2,13 @@ import os
 import io
 import tempfile
 import shutil
-from time import time
+import sqlite3
 
 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
@@ -19,82 +19,93 @@ 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',
+            "name": "exporter_sim",
-            'network_params': {},
+            "model_params": {"agents": [{"agent_class": agents.BaseAgent}]},
-            'agent_type': 'CounterModel',
+            "max_time": max_time,
-            'max_time': 2,
+            "num_trials": num_trials,
-            'num_trials': 5,
-            'environment_params': {}
         }
         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 Dummy.started
         assert Dummy.ended
         assert Dummy.called_start == 1
         assert Dummy.called_end == 1
-        assert Dummy.called_trial == 5
+        assert Dummy.called_trial == num_trials
-        assert Dummy.trials == 5
+        assert Dummy.trials == num_trials
-        assert Dummy.total_time == 2*5
+        assert Dummy.total_time == max_time * num_trials
 
     def test_writing(self):
-        '''Try to write CSV, GEXF, sqlite and YAML (without dry_run)'''
+        """Try to write CSV, sqlite and YAML (without dry_run)"""
         n_trials = 5
         config = {
-            'name': 'exporter_sim',
+            "name": "exporter_sim",
-            'network_params': {
+            "network_params": {"generator": "complete_graph", "n": 4},
-                'generator': 'complete_graph',
+            "agent_class": "CounterModel",
-                'n': 4
+            "max_time": 2,
-            },
+            "num_trials": n_trials,
-            'agent_type': 'CounterModel',
+            "dry_run": False,
-            'max_time': 2,
+            "environment_params": {},
-            'num_trials': n_trials,
-            'environment_params': {}
         }
         output = io.StringIO()
         s = simulation.from_config(config)
         tmpdir = tempfile.mkdtemp()
-        envs = s.run_simulation(exporters=[
+        envs = s.run_simulation(
-                                    exporters.default,
+            exporters=[
-                                    exporters.csv,
+                exporters.default,
-                                    exporters.gexf,
+                exporters.csv,
-                                ],
+            ],
-                                stats=[distribution,],
+            model_params={
-                                outdir=tmpdir,
+                "agent_reporters": {"times": "times"},
-                                exporter_params={'copy_to': output})
+                "model_reporters": {
+                    "constant": lambda x: 1,
+                },
+            },
+            dry_run=False,
+            outdir=tmpdir,
+            exporter_params={"copy_to": output},
+        )
         result = output.getvalue()
 
-        simdir = os.path.join(tmpdir, s.group or '', s.name)
+        simdir = os.path.join(tmpdir, s.group or "", s.name)
-        with open(os.path.join(simdir, '{}.dumped.yml'.format(s.name))) as f:
+        with open(os.path.join(simdir, "{}.dumped.yml".format(s.name))) as f:
             result = f.read()
             assert result
 
         try:
             for e in envs:
-                with open(os.path.join(simdir, '{}.gexf'.format(e.name))) as f:
+                db = sqlite3.connect(os.path.join(simdir, f"{s.name}.sqlite"))
-                    result = f.read()
+                cur = db.cursor()
-                    assert result
+                agent_entries = cur.execute("SELECT * from agents").fetchall()
+                env_entries = cur.execute("SELECT * from env").fetchall()
+                assert len(agent_entries) > 0
+                assert len(env_entries) > 0
 
-                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

@@ -1,256 +1,152 @@
 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,
+from soil import simulation, Environment, agents, network, serialization, utils, config
-                  utils)
 from soil.time import Delta
 
-
 ROOT = os.path.abspath(os.path.dirname(__file__))
-EXAMPLES = join(ROOT, '..', 'examples')
+EXAMPLES = join(ROOT, "..", "examples")
 
 
-class CustomAgent(agents.FSM):
+class CustomAgent(agents.FSM, agents.NetworkAgent):
     @agents.default_state
     @agents.state
     def normal(self):
-        self.neighbors = self.count_agents(state_id='normal',
+        self.neighbors = self.count_agents(state_id="normal", limit_neighbors=True)
-                                           limit_neighbors=True)
+
     @agents.state
     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': {
+            "model_params": {
-                'path': join(ROOT, 'test.gexf')
+                "network_params": {"path": join(ROOT, "test.gexf")},
-            },
+                "agent_class": "BaseAgent",
-            'agent_type': 'BaseAgent',
-            'environment_params': {
             }
         }
         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 = {
-            'name': 'CounterAgent',
+            "version": "2",
-            'network_params': {
+            "name": "CounterAgent",
-                'path': join(ROOT, 'test.gexf')
+            "dry_run": True,
+            "num_trials": 1,
+            "max_time": 2,
+            "model_params": {
+                "topology": {"path": join(ROOT, "test.gexf")},
+                "agents": {
+                    "agent_class": "CounterModel",
+                    "topology": True,
+                    "fixed": [{"state": {"times": 10}}, {"state": {"times": 20}}],
+                },
             },
-            'agent_type': 'CounterModel',
-            'states': [{'times': 10}, {'times': 20}],
-            'max_time': 2,
-            'num_trials': 1,
-            'environment_params': {
-            }
         }
         s = simulation.from_config(config)
-        env = s.run_simulation(dry_run=True)[0]
+        env = s.get_env()
-        assert env.get_agent(0)['times', 0] == 11
+        assert isinstance(env.agents[0], agents.CounterModel)
-        assert env.get_agent(0)['times', 1] == 12
+        assert env.agents[0].G == env.G
-        assert env.get_agent(1)['times', 0] == 21
+        assert env.agents[0]["times"] == 10
-        assert env.get_agent(1)['times', 1] == 22
+        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):
+    def test_init_and_count_agents(self):
-        """
+        """Agents should be properly initialized and counting should filter them properly"""
-        The evolution of the state should be recorded in the logging agent
+        # TODO: separate this test into two or more test cases
-        """
         config = {
-            'name': 'CounterAgent',
+            "max_time": 10,
-            'network_params': {
+            "model_params": {
-                'path': join(ROOT, 'test.gexf')
+                "agents": [
+                    {"agent_class": CustomAgent, "weight": 1, "topology": True},
+                    {"agent_class": CustomAgent, "weight": 3, "topology": True},
+                ],
+                "topology": {"path": join(ROOT, "test.gexf")},
             },
-            'network_agents': [{
-                'agent_type': 'AggregatedCounter',
-                'weight': 1,
-                'state': {'state_id': 0}
-
-            }],
-            'max_time': 10,
-            'environment_params': {
-            }
         }
         s = simulation.from_config(config)
         env = s.run_simulation(dry_run=True)[0]
-        for agent in env.network_agents:
+        assert env.agents[0].weight == 1
-            last = 0
+        assert env.count_agents() == 2
-            assert len(agent[None, None]) == 11
+        assert env.count_agents(weight=1) == 1
-            for step, total in sorted(agent['total', None]):
+        assert env.count_agents(weight=3) == 1
-                assert total == last + 2
+        assert env.count_agents(agent_class=CustomAgent) == 2
-                last = total
-
-    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 = serialization.load_file(join(EXAMPLES, "torvalds.yml"))[0]
-        config['network_params']['path'] = join(EXAMPLES,
+        config["model_params"]["network_params"]["path"] = join(
-                                                config['network_params']['path'])
+            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:
-            skill_level = a.state['skill_level']
+            skill_level = a.state["skill_level"]
-            if a.id == 'Torvalds':
+            if a.id == "Torvalds":
-                assert skill_level == 'God'
+                assert skill_level == "God"
-                assert a.state['total'] == 3
+                assert a.state["total"] == 3
-                assert a.state['neighbors'] == 2
+                assert a.state["neighbors"] == 2
-            elif a.id == 'balkian':
+            elif a.id == "balkian":
-                assert skill_level == 'developer'
+                assert skill_level == "developer"
-                assert a.state['total'] == 3
+                assert a.state["total"] == 3
-                assert a.state['neighbors'] == 1
+                assert a.state["neighbors"] == 1
             else:
-                assert skill_level == 'beginner'
+                assert skill_level == "beginner"
-                assert a.state['total'] == 3
+                assert a.state["total"] == 3
-                assert a.state['neighbors'] == 1
+                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 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 name == "test_main.CustomAgent"
         assert ser == CustomAgent
         pickle.dumps(ser)
 
@@ -262,99 +158,43 @@ 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'''
+        """A class from soil.agents should be serialized without the module part"""
-        ser = agents.serialize_type(CustomAgent)
+        ser = agents._serialize_type(CustomAgent)
-        assert ser == 'test_main.CustomAgent'
+        assert ser == "test_main.CustomAgent"
-        ser = agents.serialize_type(agents.BaseAgent)
+        ser = agents._serialize_type(agents.BaseAgent)
-        assert ser == 'BaseAgent'
+        assert ser == "BaseAgent"
         pickle.dumps(ser)
-    
-    def test_deserialize_agent_distribution(self):
-        agent_distro = [
-            {
-                'agent_type': 'CounterModel',
-                'weight': 1
-            },
-            {
-                'agent_type': 'test_main.CustomAgent',
-                'weight': 2
-            },
-        ]
-        converted = agents.deserialize_definition(agent_distro)
-        assert converted[0]['agent_type'] == agents.CounterModel
-        assert converted[1]['agent_type'] == CustomAgent
-        pickle.dumps(converted)
-
-    def test_serialize_agent_distribution(self):
-        agent_distro = [
-            {
-                'agent_type': agents.CounterModel,
-                'weight': 1
-            },
-            {
-                'agent_type': CustomAgent,
-                'weight': 2
-            },
-        ]
-        converted = agents.serialize_definition(agent_distro)
-        assert converted[0]['agent_type'] == 'CounterModel'
-        assert converted[1]['agent_type'] == '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'''
+        """Loading a template should result in several configs"""
-        configs = serialization.load_file(join(EXAMPLES, 'template.yml'))
+        configs = serialization.load_file(join(EXAMPLES, "template.yml"))
         assert len(configs) > 0
 
     def test_until(self):
-        config = {
+        n_runs = 0
-            'name': 'until_sim',
+
-            'network_params': {},
+        class CheckRun(agents.BaseAgent):
-            'agent_type': 'CounterModel',
+            def step(self):
-            'max_time': 2,
+                nonlocal n_runs
-            'num_trials': 50,
+                n_runs += 1
-            'environment_params': {}
+                return super().step()
-        }
+
-        s = simulation.from_config(config)
+        n_trials = 50
+        max_time = 2
+        s = simulation.Simulation(
+            model_params={"agents": [{"agent_class": CheckRun}]},
+            num_trials=n_trials,
+            max_time=max_time,
+        )
         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(runs) == n_trials
         assert len(over) == 0
 
-
     def test_fsm(self):
-        '''Basic state change'''
+        """Basic state change"""
+
         class ToggleAgent(agents.FSM):
             @agents.default_state
             @agents.state
@@ -373,7 +213,8 @@ class TestMain(TestCase):
         assert a.state_id == a.ping.id
 
     def test_fsm_when(self):
-        '''Basic state change'''
+        """Basic state change"""
+
         class ToggleAgent(agents.FSM):
             @agents.default_state
             @agents.state

tests/test_mesa.py

@@ -1,4 +1,4 @@
-'''
+"""
 Mesa-SOIL integration tests
 
 We have to test that:
@@ -8,13 +8,15 @@ We have to test that:
 
 - Mesa visualizations work with SOIL simulations
 
-'''
+"""
 from mesa import Agent, Model
 from mesa.time import RandomActivation
 from mesa.space import MultiGrid
 
+
 class MoneyAgent(Agent):
-    """ An agent with fixed initial wealth."""
+    """An agent with fixed initial wealth."""
+
     def __init__(self, unique_id, model):
         super().__init__(unique_id, model)
         self.wealth = 1
@@ -33,15 +35,15 @@ class MoneyAgent(Agent):
 
     def move(self):
         possible_steps = self.model.grid.get_neighborhood(
-            self.pos,
+            self.pos, moore=True, include_center=False
-            moore=True,
+        )
-            include_center=False)
         new_position = self.random.choice(possible_steps)
         self.model.grid.move_agent(self, new_position)
 
 
 class MoneyModel(Model):
     """A model with some number of agents."""
+
     def __init__(self, N, width, height):
         self.num_agents = N
         self.grid = MultiGrid(width, height, True)
@@ -58,7 +60,7 @@ class MoneyModel(Model):
             self.grid.place_agent(a, (x, y))
 
     def step(self):
-        '''Advance the model by one step.'''
+        """Advance the model by one step."""
         self.schedule.step()
 
 

tests/test_network.py

@@ -0,0 +1,110 @@
+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.G
+        network.dump_gexf(env.G, f)
+
+    def test_networkenvironment_creation(self):
+        """Networkenvironment should accept netconfig as parameters"""
+        model_params = {
+            "topology": {"path": join(ROOT, "test.gexf")},
+            "agents": {
+                "topology": True,
+                "distribution": [
+                    {
+                        "agent_class": CustomAgent,
+                    }
+                ],
+            },
+        }
+        env = environment.Environment(**model_params)
+        assert env.G
+        env.step()
+        assert len(env.G) == 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].count_neighbors() == 1
+
+    def test_custom_agent_neighbors(self):
+        """Allow for search of neighbors with a certain state_id"""
+        config = {
+            "model_params": {
+                "topology": {"path": join(ROOT, "test.gexf")},
+                "agents": {
+                    "topology": True,
+                    "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].count_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=True)
+        env = environment.Environment(name="Test", topology=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']

tests/test_time.py

@@ -0,0 +1,74 @@
+from unittest import TestCase
+
+from soil import time, agents, environment
+
+
+class TestMain(TestCase):
+    def test_cond(self):
+        """
+        A condition should match a When if the concition is True
+        """
+
+        t = time.Cond(lambda t: True)
+        f = time.Cond(lambda t: False)
+        for i in range(10):
+            w = time.When(i)
+            assert w == t
+            assert w is not f
+
+    def test_cond(self):
+        """
+        Comparing a Cond to a Delta should always return False
+        """
+
+        c = time.Cond(lambda t: False)
+        d = time.Delta(1)
+        assert c is not d
+
+    def test_cond_env(self):
+        """ """
+
+        times_started = []
+        times_awakened = []
+        times_asleep = []
+        times = []
+        done = []
+
+        class CondAgent(agents.BaseAgent):
+            def step(self):
+                nonlocal done
+                times_started.append(self.now)
+                while True:
+                    times_asleep.append(self.now)
+                    yield time.Cond(lambda agent: agent.now >= 10, delta=2)
+                    times_awakened.append(self.now)
+                    if self.now >= 10:
+                        break
+                done.append(self.now)
+
+        env = environment.Environment(agents=[{"agent_class": CondAgent}])
+
+        while env.schedule.time < 11:
+            times.append(env.now)
+            env.step()
+
+        assert env.schedule.time == 11
+        assert times_started == [0]
+        assert times_awakened == [10]
+        assert done == [10]
+        # The first time will produce the Cond.
+        assert env.schedule.steps == 6
+        assert len(times) == 6
+
+        while env.schedule.time < 13:
+            times.append(env.now)
+            env.step()
+
+        assert times == [0, 2, 4, 6, 8, 10, 11]
+        assert env.schedule.time == 13
+        assert times_started == [0, 11]
+        assert times_awakened == [10]
+        assert done == [10]
+        # Once more to yield the cond, another one to continue
+        assert env.schedule.steps == 7
+        assert len(times) == 7