black formatting

Documentation needs some improvement

The API has been simplified to only allow for ONE topology per
NetworkEnvironment.
This covers the main use case, and simplifies the code.

.gitignore

@@ -8,3 +8,4 @@ soil_output
 docs/_build*
 build/*
 dist/*
+prof

.gitlab-ci.yml

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

CHANGELOG.md

@@ -3,6 +3,108 @@ 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).
 
+## [0.3 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
+## [0.20.1]
+### Fixed
+* Agents would run another step after dying.
+## [0.20.0]
+### Added
+* Integration with MESA
+* `not_agent_ids` parameter to get sql in history
+### Changed
+* `soil.Environment` now also inherits from `mesa.Model`
+* `soil.Agent` now also inherits from `mesa.Agent`
+* `soil.time` to replace `simpy` events, delays, duration, etc.
+* `agent.id` is not `agent.unique_id` to be compatible with `mesa`. A property `BaseAgent.id` has been added for compatibility.
+* `agent.environment` is now `agent.model`, for the same reason as above. The parameter name in `BaseAgent.__init__` has also been renamed.
+### Removed
+* `simpy` dependency and compatibility. Each agent used to be a simpy generator, but that made debugging and error handling more complex. That has been replaced by a scheduler within the `soil.Environment` class, similar to how `mesa` does it.
+* `soil.history` is now a separate package named `tsih`. The keys namedtuple uses `dict_id` instead of `agent_id`.
+### Added
+* An option to choose whether a database should be used for history 
+## [0.15.2]
+### Fixed
+* Pass the right known_modules and parameters to stats discovery in simulation
+* The configuration file must exist when launching through the CLI. If it doesn't, an error will be logged
+* Minor changes in the documentation of the CLI arguments
+### Changed
+* Stats are now exported by default
+## [0.15.1]
+### Added
+* read-only `History`
+### Fixed
+* Serialization problem with the `Environment` on parallel mode.
+* Analysis functions now work as they should in the tutorial
+## [0.15.0]
+### Added
+* Control logging level in CLI and simulation
+* `Stats` to calculate trial and simulation-wide statistics
+* Simulation statistics are stored in a separate table in history (see `History.get_stats` and `History.save_stats`, as well as `soil.stats`)
+* Aliased `NetworkAgent.G` to `NetworkAgent.topology`.
+### Changed
+* Templates in config files can be given as dictionaries in addition to strings
+* Samplers are used more explicitly
+* Removed nxsim dependency. We had already made a lot of changes, and nxsim has not been updated in 5 years.
+* Exporter methods renamed to `trial` and `end`. Added `start`.
+* `Distribution` exporter now a stats class
+* `global_topology` renamed to `topology`
+* Moved topology-related methods to `NetworkAgent`
+### Fixed
+* Temporary files used for history in dry_run mode are not longer left open 
+
+## [0.14.9]
+### Changed
+* Seed random before environment initialization
 ## [0.14.8]
 ### Fixed
 * Invalid directory names in Windows gsi-upm/soil#5

README.md

@@ -5,6 +5,45 @@ 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 
+
+
 If you use Soil in your research, don't forget to cite this paper:
 
 ```bibtex
@@ -28,7 +67,6 @@ If you use Soil in your research, don't forget to cite this paper:
 
 ```
 
-@Copyright GSI - Universidad Politécnica de Madrid 2017
+@Copyright GSI - Universidad Politécnica de Madrid 2017-2021
 
 [![SOIL](logo_gsi.png)](https://www.gsi.upm.es)
-

docs/conf.py

@@ -31,7 +31,7 @@
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = []
+extensions = ['IPython.sphinxext.ipython_console_highlighting']
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
@@ -69,7 +69,7 @@ language = None
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This patterns also effect to html_static_path and html_extra_path
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints']
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = 'sphinx'

docs/configuration.rst

@@ -13,7 +13,7 @@ Here's an example (``example.yml``).
 
 
 This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
-The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil.
+The 100 nodes in the network will be SISaModel agents (``network_agents.agent_class``), which is an agent behavior that is included in Soil.
 10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
 All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
 The state of the agents will be updated every 2 seconds (``interval``).
@@ -88,9 +88,18 @@ For example, the following configuration is equivalent to :code:`nx.complete_gra
 
 Environment
 ============
+
 The environment is the place where the shared state of the simulation is stored.
-For instance, the probability of disease outbreak.
-The configuration file may specify the initial value of the environment parameters:
+That means both global parameters, such as the probability of disease outbreak.
+But it also means other data, such as a map, or a network topology that connects multiple agents.
+As a result, it is also typical to add custom functions in an environment that help agents interact with each other and with the state of the simulation.
+
+Last but not least, an environment controls when and how its agents will be executed.
+By default, soil environments incorporate a ``soil.time.TimedActivation`` model for agent execution (more on this on the following section).
+
+Soil environments are very similar, and often interchangeable with, mesa models (``mesa.Model``).
+
+A configuration may specify the initial value of the environment parameters:
 
 .. code:: yaml
 
@@ -98,23 +107,33 @@ The configuration file may specify the initial value of the environment paramete
         daily_probability_of_earthquake: 0.001
         number_of_earthquakes: 0
 
-All agents have access to the environment parameters.
+All agents have access to the environment (and its parameters).
 
 In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
 For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.
 
-
 Agents
 ======
+
 Agents are a way of modelling behavior.
-Agents can be characterized with two variables: agent type (``agent_type``) and state.
-Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters.
+Agents can be characterized with two variables: agent type (``agent_class``) and state.
+The agent type is a ``soil.Agent`` class, which contains the code that encapsulates the behavior of the agent.
+The state is a set of variables, which may change during the simulation, and that the code may use to control the behavior.
+All agents provide a ``step`` method either explicitly or implicitly (by inheriting it from a superclass), which controls how the agent will behave in each step of the simulation.
+
+When and how agent steps are executed in a simulation depends entirely on the ``environment``.
+Most environments will internally use a scheduler (``mesa.time.BaseScheduler``), which controls the activation of agents.
+
+In soil, we generally used the ``soil.time.TimedActivation`` scheduler, which allows agents to specify when their next activation will happen, defaulting to a 
+
+When an agent's step is executed (generally, every ``interval`` seconds), the agent has access to its state and the environment.
 Through the environment, it can access the network topology and the state of other agents.
 
-There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
+There are two types of agents according to how they are added to the simulation: network agents and environment agent.
 
 Network Agents
 ##############
+
 Network agents are attached to a node in the topology.
 The configuration file allows you to specify how agents will be mapped to topology nodes.
 
@@ -123,17 +142,19 @@ Hence, every node in the network will be associated to an agent of that type.
 
 .. code:: yaml
 
-   agent_type: SISaModel
+   agent_class: SISaModel
 
-It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property).
+It is also possible to add more than one type of agent to the simulation.
+
+To control the ratio of each type (using the ``weight`` property).
 For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
 
 .. code:: yaml
 
     network_agents:
-          - agent_type: SISaModel
+          - agent_class: SISaModel
             weight: 1
-          - agent_type: CounterModel
+          - agent_class: CounterModel
             weight: 5
 
 The third option is to specify the type of agent on the node itself, e.g.:
@@ -144,10 +165,10 @@ The third option is to specify the type of agent on the node itself, e.g.:
    topology:
        nodes:
            - id: first
-   agent_type: BaseAgent
+   agent_class: BaseAgent
    states:
        first:
-         agent_type: SISaModel
+         agent_class: SISaModel
           
 
 This would also work with a randomly generated network:
@@ -158,9 +179,9 @@ This would also work with a randomly generated network:
    network:
        generator: complete
        n: 5
-   agent_type: BaseAgent
+   agent_class: BaseAgent
    states:
-       - agent_type: SISaModel
+       - agent_class: SISaModel
 
               
 
@@ -171,11 +192,11 @@ e.g., to populate the network with SISaModel, roughly 10% of them with a discont
 .. code:: yaml
 
     network_agents:
-        - agent_type: SISaModel
+        - agent_class: SISaModel
             weight: 9
             state:
             id: neutral
-        - agent_type: SISaModel
+        - agent_class: SISaModel
             weight: 1
             state:
             id: discontent
@@ -185,7 +206,7 @@ For instance, to add a state for the two nodes in this configuration:
 
 .. code:: yaml
 
-   agent_type: SISaModel
+   agent_class: SISaModel
    network:
       generator: complete_graph
       n: 2
@@ -210,11 +231,32 @@ 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.
 They are also useful to add behavior that has little to do with the network and the interactions within that network.
+
+Templating
+==========
+
+Sometimes, it is useful to parameterize a simulation and run it over a range of values in order to compare each run and measure the effect of those parameters in the simulation.
+For instance, you may want to run a simulation with different agent distributions.
+
+This can be done in Soil using **templates**.
+A template is a configuration where some of the values are specified with a variable.
+e.g.,  ``weight: "{{ var1 }}"`` instead of ``weight: 1``.
+There are two types of variables, depending on how their values are decided:
+
+* Fixed. A list of values is provided, and a new simulation is run for each possible value. If more than a variable is given, a new simulation will be run per combination of values.
+* Bounded/Sampled. The bounds of the variable are provided, along with a sampler method, which will be used to compute all the configuration combinations.
+
+When fixed and bounded variables are mixed, Soil generates a new configuration per combination of fixed values and bounded values.
+
+Here is an example with a single fixed variable and two bounded variable:
+
+.. literalinclude:: ../examples/template.yml
+   :language: yaml

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

@@ -0,0 +1 @@
+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

@@ -47,12 +47,6 @@ There are three main elements in a soil simulation:
 -  The environment. It assigns agents to nodes in the network, and
    stores the environment parameters (shared state for all agents).
 
-Soil is based on ``simpy``, which is an event-based network simulation
-library. Soil provides several abstractions over events to make
-developing agents easier. This means you can use events (timeouts,
-delays) in soil, but for the most part we will assume your models will
-be step-based.
-
 Modeling behaviour
 ------------------
 
@@ -217,11 +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},
@@ -291,14 +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
@@ -306,7 +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",
@@ -500,7 +500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.8.5"
   },
   "toc": {
    "colors": {

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",
@@ -80800,7 +80800,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.8.6"
   }
  },
  "nbformat": 4,

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:
-  generator: complete_graph
-  n: 10
-network_agents:
-  - agent_type: CounterModel
-    weight: 1
-    state:
-      id: 0
-  - agent_type: AggregatedCounter
-    weight: 0.2
-environment_agents: []
-environment_class: Environment
-environment_params:
+model_class: Environment
+model_params:
   am_i_complete: true
-default_state:
-  incidents: 0
-states:
-  - name: 'The first node'
-  - name: 'The second node'
+  topology:
+    params:
+      generator: complete_graph
+      n: 12
+  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:
-      id: 0
+      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 = {
-        'prev': 1
-    }
+    defaults = {"prev": 1}
 
     @default_state
     @state
     def counting(self):
-        self.log('Stopping at {}'.format(self.now))
-        prev, self['prev'] = self['prev'], max([self.now, self['prev']])
+        self.log("Stopping at {}".format(self.now))
+        prev, self["prev"] = self["prev"], max([self.now, self["prev"]])
         return None, self.env.timeout(prev)
 
+
 class Odds(FSM):
-    '''Agent that only executes in odd t_steps'''
+    """Agent that only executes in odd t_steps"""
+
     @default_state
     @state
     def odds(self):
-        self.log('Stopping at {}'.format(self.now))
-        return None, self.env.timeout(1+self.now%2)
+        self.log("Stopping at {}".format(self.now))
+        return None, self.env.timeout(1 + self.now % 2)
 
-if __name__ == '__main__':
-    import logging
-    logging.basicConfig(level=logging.INFO)
+
+if __name__ == "__main__":
     from soil import Simulation
-    s = Simulation(network_agents=[{'ids': [0], 'agent_type': Fibonacci},
-                                   {'ids': [1], 'agent_type': Odds}],
-                   network_params={"generator": "complete_graph", "n": 2},
-                   max_time=100,
-                   )
+
+    s = Simulation(
+        network_agents=[
+            {"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/mesa/mesa.yml

@@ -0,0 +1,19 @@
+---
+name: mesa_sim
+group: tests
+dir_path: "/tmp"
+num_trials: 3
+max_steps: 100
+interval: 1
+seed: '1'
+model_class: social_wealth.MoneyEnv
+model_params:
+  generator: social_wealth.graph_generator
+  agents:
+    topology: true
+    distribution:
+      - agent_class: social_wealth.SocialMoneyAgent
+        weight: 1
+  N: 10
+  width: 50
+  height: 50

examples/mesa/server.py

@@ -0,0 +1,114 @@
+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):
+    def render(self, model):
+        return self.portrayal_method(model)
+
+
+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": node_id,
+            "size": 2 * (wealth + 1),
+            "color": "#CC0000" if wealth == 0 else "#007959",
+            # "color": "#CC0000",
+            "label": f"{node_id}: {wealth}",
+        }
+        for (node_id, wealth) in wealths.items()
+    ]
+
+    portrayal["edges"] = [
+        {"id": edge_id, "source": source, "target": target, "color": "#000000"}
+        for edge_id, (source, target) in enumerate(env.G.edges)
+    ]
+
+    return portrayal
+
+
+def gridPortrayal(agent):
+    """
+    This function is registered with the visualization server to be called
+    each tick to indicate how to draw the agent in its current state.
+    :param agent:  the agent in the simulation
+    :return: the portrayal dictionary
+    """
+    color = max(10, min(agent.wealth * 10, 100))
+    return {
+        "Shape": "rect",
+        "w": 1,
+        "h": 1,
+        "Filled": "true",
+        "Layer": 0,
+        "Label": agent.unique_id,
+        "Text": agent.unique_id,
+        "x": agent.pos[0],
+        "y": agent.pos[1],
+        "Color": f"rgba(31, 10, 255, 0.{color})",
+    }
+
+
+grid = MyNetwork(network_portrayal, 500, 500)
+chart = ChartModule(
+    [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
+)
+
+model_params = {
+    "N": UserSettableParameter(
+        "slider",
+        "N",
+        5,
+        1,
+        10,
+        1,
+        description="Choose how many agents to include in the model",
+    ),
+    "height": UserSettableParameter(
+        "slider",
+        "height",
+        5,
+        5,
+        10,
+        1,
+        description="Grid height",
+    ),
+    "width": UserSettableParameter(
+        "slider",
+        "width",
+        5,
+        5,
+        10,
+        1,
+        description="Grid width",
+    ),
+    "agent_class": UserSettableParameter(
+        "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
+)
+
+
+server = ModularServer(
+    MoneyEnv, [grid, chart, canvas_element], "Money Model", model_params
+)
+server.port = 8521
+
+server.launch(open_browser=False)

examples/mesa/social_wealth.py

@@ -0,0 +1,137 @@
+"""
+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, 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))
+    return 1 + (1 / N) - 2 * B
+
+
+class MoneyAgent(MesaAgent):
+    """
+    A MESA agent with fixed initial wealth.
+    It will only share wealth with neighbors based on grid proximity
+    """
+
+    def __init__(self, unique_id, model, wealth=1):
+        super().__init__(unique_id=unique_id, model=model)
+        self.wealth = wealth
+
+    def move(self):
+        possible_steps = self.model.grid.get_neighborhood(
+            self.pos, moore=True, include_center=False
+        )
+        new_position = self.random.choice(possible_steps)
+        self.model.grid.move_agent(self, new_position)
+
+    def give_money(self):
+        cellmates = self.model.grid.get_cell_list_contents([self.pos])
+        if len(cellmates) > 1:
+            other = self.random.choice(cellmates)
+            other.wealth += 1
+            self.wealth -= 1
+
+    def step(self):
+        print("Crying wolf", self.pos)
+        self.move()
+        if self.wealth > 0:
+            self.give_money()
+
+
+class SocialMoneyAgent(NetworkAgent, MoneyAgent):
+    wealth = 1
+
+    def give_money(self):
+        cellmates = set(self.model.grid.get_cell_list_contents([self.pos]))
+        friends = set(self.get_neighboring_agents())
+        self.info("Trying to give money")
+        self.info("Cellmates: ", cellmates)
+        self.info("Friends: ", friends)
+
+        nearby_friends = list(cellmates & friends)
+
+        if len(nearby_friends):
+            other = self.random.choice(nearby_friends)
+            other.wealth += 1
+            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,
+        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)
+            y = self.random.randrange(self.grid.height)
+            self.grid.place_agent(agent, (x, y))
+
+        self.datacollector = DataCollector(
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
+        )
+
+
+if __name__ == "__main__":
+
+    fixed_params = {
+        "generator": nx.complete_graph,
+        "width": 10,
+        "network_agents": [{"agent_class": SocialMoneyAgent, "weight": 1}],
+        "height": 10,
+    }
+
+    variable_params = {"N": range(10, 100, 10)}
+
+    batch_run = BatchRunner(
+        MoneyEnv,
+        variable_parameters=variable_params,
+        fixed_parameters=fixed_params,
+        iterations=5,
+        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

@@ -0,0 +1,87 @@
+from mesa import Agent, Model
+from mesa.space import MultiGrid
+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))
+    return 1 + (1 / N) - 2 * B
+
+
+class MoneyAgent(Agent):
+    """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, moore=True, include_center=False
+        )
+        new_position = self.random.choice(possible_steps)
+        self.model.grid.move_agent(self, new_position)
+
+    def give_money(self):
+        cellmates = self.model.grid.get_cell_list_contents([self.pos])
+        if len(cellmates) > 1:
+            other = self.random.choice(cellmates)
+            other.wealth += 1
+            self.wealth -= 1
+
+    def step(self):
+        self.move()
+        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)
+        self.schedule = RandomActivation(self)
+        self.running = True
+
+        # Create agents
+        for i in range(self.num_agents):
+            a = MoneyAgent(i, self)
+            self.schedule.add(a)
+            # Add the agent to a random grid cell
+            x = self.random.randrange(self.grid.width)
+            y = self.random.randrange(self.grid.height)
+            self.grid.place_agent(a, (x, y))
+
+        self.datacollector = DataCollector(
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
+        )
+
+    def step(self):
+        self.datacollector.collect(self)
+        self.schedule.step()
+
+
+if __name__ == "__main__":
+
+    fixed_params = {"width": 10, "height": 10}
+    variable_params = {"N": range(10, 500, 10)}
+
+    batch_run = BatchRunner(
+        MoneyModel,
+        variable_params,
+        fixed_params,
+        iterations=5,
+        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
@@ -56,24 +54,23 @@ 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
-    id: neutral
+    state_id: neutral
   weight: 1
-- agent_type: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
-    id: neutral
+    state_id: neutral
   weight: 1
 network_params:
   generator: barabasi_albert_graph
@@ -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
-    id: neutral
+    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
-    id: neutral
+    state_id: neutral
   weight: 1
-- agent_type: WiseViewer
+- agent_class: newsspread.WiseViewer
   state:
     has_tv: true
   weight: 1

examples/newsspread/newsspread.py

@@ -1,81 +1,85 @@
-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_tv_spread': 0.1,
+        "prob_neighbor_spread": 0.5,
+        "prob_tv_spread": 0.1,
     }
 
     @default_state
     @state
     def neutral(self):
-        if self['has_tv']:
-            if prob(self.env['prob_tv_spread']):
-                self.set_state(self.infected)
+        if self["has_tv"]:
+            if self.prob(self.model["prob_tv_spread"]):
+                return self.infected
 
     @state
     def infected(self):
         for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
-            if prob(self.env['prob_neighbor_spread']):
+            if self.prob(self.model["prob_neighbor_spread"]):
                 neighbor.infect()
 
     def infect(self):
+        """
+        This is not a state. It is a function that other agents can use to try to
+        infect this agent. DumbViewer always gets infected, but other agents like
+        HerdViewer might not become infected right away
+        """
+
         self.set_state(self.infected)
 
 
 class HerdViewer(DumbViewer):
-    '''
+    """
     A viewer whose probability of infection depends on the state of its neighbors.
-    '''
-
-    level = logging.DEBUG
+    """
 
     def infect(self):
+        """Notice again that this is NOT a state. See DumbViewer.infect for reference"""
         infected = self.count_neighboring_agents(state_id=self.infected.id)
         total = self.count_neighboring_agents()
-        prob_infect = self.env['prob_neighbor_spread'] * infected/total
-        self.debug('prob_infect', prob_infect)
-        if prob(prob_infect):
-            self.set_state(self.infected.id)
+        prob_infect = self.model["prob_neighbor_spread"] * infected / total
+        self.debug("prob_infect", prob_infect)
+        if self.prob(prob_infect):
+            self.set_state(self.infected)
 
 
 class WiseViewer(HerdViewer):
-    '''
+    """
     A viewer that can change its mind.
-    '''
+    """
 
     defaults = {
-        'prob_neighbor_spread': 0.5,
-        'prob_neighbor_cure': 0.25,
-        'prob_tv_spread': 0.1,
+        "prob_neighbor_spread": 0.5,
+        "prob_neighbor_cure": 0.25,
+        "prob_tv_spread": 0.1,
     }
 
     @state
     def cured(self):
-        prob_cure = self.env['prob_neighbor_cure']
+        prob_cure = self.model["prob_neighbor_cure"]
         for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
-            if prob(prob_cure):
+            if self.prob(prob_cure):
                 try:
                     neighbor.cure()
                 except AttributeError:
-                    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)
 
     @state
     def infected(self):
-        cured = max(self.count_neighboring_agents(self.cured.id),
-                    1.0)
-        infected = max(self.count_neighboring_agents(self.infected.id),
-                       1.0)
-        prob_cure = self.env['prob_neighbor_cure'] * (cured/infected)
-        if prob(prob_cure):
-            return self.cure()
+        cured = max(self.count_neighboring_agents(self.cured.id), 1.0)
+        infected = max(self.count_neighboring_agents(self.infected.id), 1.0)
+        prob_cure = self.model["prob_neighbor_cure"] * (cured / infected)
+        if self.prob(prob_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',
-               network_params={'generator': mygenerator},
-               num_trials=1,
-               max_time=100,
-               agent_type=MyAgent,
-               dry_run=True)
+s = Simulation(
+    name="Programmatic",
+    network_params={"generator": mygenerator},
+    num_trials=1,
+    max_time=100,
+    agent_class=MyAgent,
+    dry_run=True,
+)
 
 
+# By default, logging will only print WARNING logs (and above).
+# You need to choose a lower logging level to get INFO/DEBUG traces
 logging.basicConfig(level=logging.INFO)
 envs = s.run()
 
-s.dump_yaml()
-
-for env in envs:
-    env.dump_csv()
+# Uncomment this to output the simulation to a YAML file
+# s.dump_yaml('simulation.yaml')

examples/pubcrawl/pubcrawl.py

@@ -1,12 +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),
-                'open': True,
-                'capacity': pub_capacity,
-                'occupancy': 0,
+                "name": "The awesome pub #{}".format(i),
+                "open": True,
+                "capacity": pub_capacity,
+                "occupancy": 0,
             }
-            pubs[newpub['name']] = newpub
-        self['pubs'] = pubs
+            pubs[newpub["name"]] = newpub
+        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))
-        if not pub['open'] or (pub['capacity'] < (len(nodes) + pub['occupancy'])):
+            raise ValueError("Pub {} is not available".format(pub_id))
+        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():
-            if pub['open'] and (pub['occupancy'] < pub['capacity']):
-                yield pub['name']
+        for pub in self["pubs"].values():
+            if pub["open"] and (pub["occupancy"] < pub["capacity"]):
+                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']:
-                del node['pub']
-                pub['occupancy'] -= 1
+            if pub_id == node["pub"]:
+                del node["pub"]
+                pub["occupancy"] -= 1
 
 
-class Patron(FSM):
-    '''Agent that looks for friends to drink with. It will do three things:
-        1) Look for other patrons to drink with
-        2) Look for a bar where the agent and other agents in the same group can get in.
-        3) While in the bar, patrons only drink, until they get drunk and taken home.
-    '''
+class Patron(FSM, NetworkAgent):
+    """Agent that looks for friends to drink with. It will do three things:
+    1) Look for other patrons to drink with
+    2) Look for a bar where the agent and other agents in the same group can get in.
+    3) While in the bar, patrons only drink, until they get drunk and taken home.
+    """
+
     level = logging.DEBUG
 
-    defaults = {
-        'pub': None,
-        'drunk': False,
-        'pints': 0,
-        'max_pints': 3,
-    }
+    pub = None
+    drunk = False
+    pints = 0
+    max_pints = 3
+    kicked_out = False
 
     @default_state
     @state
     def looking_for_friends(self):
-        '''Look for friends to drink with'''
-        self.info('I am looking for friends')
-        available_friends = list(self.get_agents(drunk=False,
-                                                 pub=None,
-                                                 state_id=self.looking_for_friends.id))
+        """Look for friends to drink with"""
+        self.info("I am looking for friends")
+        available_friends = list(
+            self.get_agents(drunk=False, pub=None, 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'''
-        if self['pub'] != None:
+        """Look for a pub that accepts me and my friends"""
+        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())
-        for pub in self.env.available_pubs():
-            self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group)))
-            if self.env.enter(pub, self, *group):
-                self.info('We\'re all {} getting in {}!'.format(len(group), pub))
+        for pub in self.model.available_pubs():
+            self.debug("We're trying to get into {}: total: {}".format(pub, len(group)))
+            if self.model.enter(pub, self, *group):
+                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!'''
-        self.info('I\'m so drunk. Take me home!')
-        self['drunk'] = True
-        pass  # out drunk
+        """I'm out. Take me home!"""
+        self.info("I'm so drunk. Take me home!")
+        self["drunk"] = True
+        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.debug('Cheers to that')
-    
+        self["pints"] += 1
+        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():
-            self.env.add_edge(self, other_agent)
-            self.info('Made some friend {}'.format(other_agent))
+        """
+        if force or self["openness"] > self.random.random():
+            self.add_edge(self, 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'])
-        shuffle(others)
+        k = int(10 * self["openness"])
+        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,
-                                          state_id=Patron.drunk_in_pub.id))
+        drunksters = list(self.get_agents(drunk=True, 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,
-                               dump=None,
-                               parallel=False)
+
+    simulation.run_from_config("pubcrawl.yml", dry_run=True, 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.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/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

@@ -1,120 +0,0 @@
-from soil.agents import FSM, state, default_state, BaseAgent
-from enum import Enum
-from random import random, choice
-from itertools import islice
-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 = 4*30
-    life_expectancy = 365 * 3
-    gestation = 33
-    pregnancy = -1
-    max_females = 5
-
-    @default_state
-    @state
-    def newborn(self):
-        self['age'] += 1
-
-        if self['age'] >= self.sexual_maturity:
-            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
-        females = self.get_agents(state_id=self.fertile.id, gender=Genders.female.value, limit_neighbors=False)
-        for f in islice(females, 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.global_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(BaseAgent):
-
-    level = logging.DEBUG
-
-    def step(self):
-        rabbits_total = self.global_topology.number_of_nodes()
-        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.global_topology.number_of_nodes():
-            self.die()

examples/rabbits/rabbits.yml

@@ -1,23 +0,0 @@
----
-load_module: rabbit_agents
-name: rabbits_example
-max_time: 500
-interval: 1
-seed: MySeed
-agent_type: RabbitModel
-environment_agents:
-    - agent_type: RandomAccident
-environment_params:
-  prob_death: 0.001
-default_state:
-  mating_prob: 0.01
-topology:
-  nodes:
-    - id: 1
-      state:
-        gender: female
-    - id: 0
-      state:
-        gender: male
-  directed: true
-  links: []

examples/random_delays/random_delays.py

@@ -0,0 +1,43 @@
+"""
+Example of setting a 
+Example of a fully programmatic simulation, without definition files.
+"""
+from soil import Simulation, agents
+from soil.time import Delta
+
+
+class MyAgent(agents.FSM):
+    """
+    An agent that first does a ping
+    """
+
+    defaults = {"pong_counts": 2}
+
+    @agents.default_state
+    @agents.state
+    def ping(self):
+        self.info("Ping")
+        return self.pong, Delta(self.random.expovariate(1 / 16))
+
+    @agents.state
+    def pong(self):
+        self.info("Pong")
+        self.pong_counts -= 1
+        self.info(str(self.pong_counts))
+        if self.pong_counts < 1:
+            return self.die()
+        return None, Delta(self.random.expovariate(1 / 16))
+
+
+s = Simulation(
+    name="Programmatic",
+    network_agents=[{"agent_class": MyAgent, "id": 0}],
+    topology={"nodes": [{"id": 0}], "links": []},
+    num_trials=1,
+    max_time=100,
+    agent_class=MyAgent,
+    dry_run=True,
+)
+
+
+envs = s.run()

examples/template.yml

@@ -1,29 +1,30 @@
 ---
+sampler:
+  method: "SALib.sample.morris.sample"
+  N: 10
+template:
+  group: simple
+  num_trials: 1
+  interval: 1
+  max_steps: 2
+  seed: "CompleteSeed!"
+  dump: false
+  model_params:
+    network_params:
+      generator: complete_graph
+      n: 10
+    network_agents:
+      - agent_class: CounterModel
+        weight: "{{ x1 }}"
+        state:
+          state_id: 0
+      - agent_class: AggregatedCounter
+        weight: "{{ 1 - x1 }}"
+    name: "{{ x3 }}"
+  skip_test: true
 vars:
   bounds:
     x1: [0, 1]
     x2: [1, 2]
   fixed:
     x3: ["a", "b", "c"]
-sampler: "SALib.sample.morris.sample"
-samples: 10
-template: |
-  group: simple
-  num_trials: 1
-  interval: 1
-  max_time: 2
-  seed: "CompleteSeed!"
-  dump: false
-  network_params:
-    generator: complete_graph
-    n: 10
-  network_agents:
-    - agent_type: CounterModel
-      weight: {{ x1 }}
-      state:
-        id: 0
-    - agent_type: AggregatedCounter
-      weight: {{ 1 - x1 }}
-  environment_params:
-    name: {{ x3 }}
-  skip_test: true

examples/terrorism/TerroristNetworkModel.py

@@ -1,4 +1,3 @@
-import random
 import networkx as nx
 from soil.agents import Geo, NetworkAgent, FSM, state, default_state
 from soil import Environment
@@ -18,59 +17,86 @@ class TerroristSpreadModel(FSM, Geo):
         prob_interaction
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=model, unique_id=unique_id, state=state)
 
-        self.information_spread_intensity = environment.environment_params['information_spread_intensity']
-        self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
-        self.prob_interaction = environment.environment_params['prob_interaction']
+        self.information_spread_intensity = model.environment_params[
+            "information_spread_intensity"
+        ]
+        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
-            self.mean_belief = random.uniform(0.00, 0.5)
-        elif self['id'] == self.terrorist.id:     # Terrorist
-            self.mean_belief = random.uniform(0.8, 1.00)
-        elif self['id'] == self.leader.id:     # Leader
+        if self["id"] == self.civilian.id:  # Civilian
+            self.mean_belief = self.random.uniform(0.00, 0.5)
+        elif self["id"] == self.terrorist.id:  # Terrorist
+            self.mean_belief = self.random.uniform(0.8, 1.00)
+        elif self["id"] == self.leader.id:  # Leader
             self.mean_belief = 1.00
         else:
-            raise Exception('Invalid state id: {}'.format(self['id']))
-
-        if 'min_vulnerability' in environment.environment_params:
-            self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
-        else :
-            self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
+            raise Exception("Invalid state id: {}".format(self["id"]))
 
+        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_neighboring_agents(agent_class=TerroristSpreadModel))
         if len(neighbours) > 0:
             # Only interact with some of the neighbors
-            interactions = list(n for n in neighbours if random.random() <= self.prob_interaction)
-            influence = sum( self.degree(i) for i in interactions )
-            mean_belief = sum( i.mean_belief * self.degree(i) / influence for i in interactions )
-            mean_belief = mean_belief * self.information_spread_intensity + self.mean_belief * ( 1 - self.information_spread_intensity )
-            self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
-        
+            interactions = list(
+                n for n in neighbours if self.random.random() <= self.prob_interaction
+            )
+            influence = sum(self.degree(i) for i in interactions)
+            mean_belief = sum(
+                i.mean_belief * self.degree(i) / influence for i in interactions
+            )
+            mean_belief = (
+                mean_belief * self.information_spread_intensity
+                + self.mean_belief * (1 - self.information_spread_intensity)
+            )
+            self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * (
+                1 - self.vulnerability
+            )
+
         if self.mean_belief >= 0.8:
             return self.terrorist
 
     @state
     def leader(self):
-        self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
-        for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
+        self.mean_belief = self.mean_belief ** (1 - self.terrorist_additional_influence)
+        for neighbour in self.get_neighboring_agents(
+            state_id=[self.terrorist.id, self.leader.id]
+        ):
             if self.betweenness(neighbour) > self.betweenness(self):
                 return self.terrorist
 
     @state
     def terrorist(self):
-        neighbours = self.get_agents(state_id=[self.terrorist.id, self.leader.id],
-                                     agent_type=TerroristSpreadModel,
-                                     limit_neighbors=True)
+        neighbours = self.get_agents(
+            state_id=[self.terrorist.id, self.leader.id],
+            agent_class=TerroristSpreadModel,
+            limit_neighbors=True,
+        )
         if len(neighbours) > 0:
-            influence = sum( self.degree(n) for n in neighbours )
-            mean_belief = sum( n.mean_belief * self.degree(n) / influence for n in neighbours )
-            mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
-            self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
+            influence = sum(self.degree(n) for n in neighbours)
+            mean_belief = sum(
+                n.mean_belief * self.degree(n) / influence for n in neighbours
+            )
+            mean_belief = mean_belief * self.vulnerability + self.mean_belief * (
+                1 - self.vulnerability
+            )
+            self.mean_belief = self.mean_belief ** (
+                1 - self.terrorist_additional_influence
+            )
 
         # Check if there are any leaders in the group
         leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
@@ -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):
     """
@@ -93,19 +147,22 @@ class TrainingAreaModel(FSM, Geo):
     Requires TerroristSpreadModel.
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.training_influence = environment.environment_params['training_influence']
-        if 'min_vulnerability' in environment.environment_params:
-            self.min_vulnerability = environment.environment_params['min_vulnerability']
-        else: self.min_vulnerability = 0
+    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"]
+        if "min_vulnerability" in model.environment_params:
+            self.min_vulnerability = model.environment_params["min_vulnerability"]
+        else:
+            self.min_vulnerability = 0
 
     @default_state
     @state
     def terrorist(self):
-        for neighbour in self.get_neighboring_agents(agent_type=TerroristSpreadModel):
+        for neighbour in self.get_neighboring_agents(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):
@@ -120,16 +177,17 @@ class HavenModel(FSM, Geo):
     Requires TerroristSpreadModel.
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.haven_influence = environment.environment_params['haven_influence']
-        if 'min_vulnerability' in environment.environment_params:
-            self.min_vulnerability = environment.environment_params['min_vulnerability']
-        else: self.min_vulnerability = 0
-        self.max_vulnerability = environment.environment_params['max_vulnerability']
+    def __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"]
+        if "min_vulnerability" in model.environment_params:
+            self.min_vulnerability = model.environment_params["min_vulnerability"]
+        else:
+            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_neighboring_agents(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
 
 
@@ -162,13 +224,13 @@ class TerroristNetworkModel(TerroristSpreadModel):
         weight_link_distance
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=model, unique_id=unique_id, state=state)
 
-        self.vision_range = environment.environment_params['vision_range']
-        self.sphere_influence = environment.environment_params['sphere_influence']
-        self.weight_social_distance = environment.environment_params['weight_social_distance']
-        self.weight_link_distance = environment.environment_params['weight_link_distance']
+        self.vision_range = model.environment_params["vision_range"]
+        self.sphere_influence = model.environment_params["sphere_influence"]
+        self.weight_social_distance = model.environment_params["weight_social_distance"]
+        self.weight_link_distance = model.environment_params["weight_link_distance"]
 
     @state
     def terrorist(self):
@@ -182,27 +244,48 @@ class TerroristNetworkModel(TerroristSpreadModel):
 
     def update_relationships(self):
         if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
-            close_ups = set(self.geo_search(radius=self.vision_range, agent_type=TerroristNetworkModel))
-            step_neighbours = set(self.ego_search(self.sphere_influence, agent_type=TerroristNetworkModel, center=False))
-            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_type=TerroristNetworkModel))
+            close_ups = set(
+                self.geo_search(
+                    radius=self.vision_range, agent_class=TerroristNetworkModel
+                )
+            )
+            step_neighbours = set(
+                self.ego_search(
+                    self.sphere_influence,
+                    agent_class=TerroristNetworkModel,
+                    center=False,
+                )
+            )
+            neighbours = set(
+                agent.id
+                for agent in self.get_neighboring_agents(
+                    agent_class=TerroristNetworkModel
+                )
+            )
             search = (close_ups | step_neighbours) - neighbours
             for agent in self.get_agents(search):
                 social_distance = 1 / self.shortest_path_length(agent.id)
-                spatial_proximity = ( 1 - self.get_distance(agent.id) )
-                prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
-                if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
+                spatial_proximity = 1 - self.get_distance(agent.id)
+                prob_new_interaction = (
+                    self.weight_social_distance * social_distance
+                    + self.weight_link_distance * spatial_proximity
+                )
+                if (
+                    agent["id"] == agent.civilian.id
+                    and 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.global_topology, 'pos')[self.id]
-        target_x, target_y = nx.get_node_attributes(self.global_topology, 'pos')[target]
-        dx = abs( source_x - target_x )
-        dy = abs( source_y - target_y )
-        return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
+        source_x, source_y = nx.get_node_attributes(self.G, "pos")[self.id]
+        target_x, target_y = nx.get_node_attributes(self.G, "pos")[target]
+        dx = abs(source_x - target_x)
+        dy = abs(source_y - target_y)
+        return (dx**2 + dy**2) ** (1 / 2)
 
     def shortest_path_length(self, target):
         try:
-            return nx.shortest_path_length(self.global_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_time: 150 
+max_steps: 150 
 num_trials: 1
-network_params:
-  generator: random_geometric_graph
-  radius: 0.2
-  # generator: geographical_threshold_graph
-  # theta: 20
-  n: 100
-network_agents:
-  - agent_type: TerroristNetworkModel
-    weight: 0.8
-    state:
-      id: civilian  # Civilians
-  - agent_type: TerroristNetworkModel
-    weight: 0.1
-    state:
-      id: leader    # Leaders
-  - agent_type: TrainingAreaModel
-    weight: 0.05
-    state:
-      id: terrorist # Terrorism
-  - agent_type: HavenModel
-    weight: 0.05
-    state:
-      id: civilian  # Civilian
+model_params:
+  network_params:
+    generator: random_geometric_graph
+    radius: 0.2
+    # generator: geographical_threshold_graph
+    # theta: 20
+    n: 100
+  network_agents:
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
+      weight: 0.8
+      state:
+        id: civilian  # Civilians
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
+      weight: 0.1
+      state:
+        id: leader    # Leaders
+    - agent_class: TerroristNetworkModel.TrainingAreaModel
+      weight: 0.05
+      state:
+        id: terrorist # Terrorism
+    - agent_class: TerroristNetworkModel.HavenModel
+      weight: 0.05
+      state:
+        id: civilian  # Civilian
 
-environment_params:
   # TerroristSpreadModel
   information_spread_intensity: 0.7
   terrorist_additional_influence: 0.035

examples/torvalds.yml

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

examples/tutorial/soil_tutorial.html

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

requirements.txt

@@ -1,10 +1,10 @@
-nxsim>=0.1.2
-simpy
-networkx>=2.0,<2.4
+networkx>=2.5
 numpy
 matplotlib
 pyyaml>=5.1
-pandas>=0.23
-scipy>=1.3
+pandas>=1
 SALib>=1.3
 Jinja2
+Mesa>=1.1
+pydantic>=1.9
+sqlalchemy>=1.4

setup.py

@@ -16,6 +16,12 @@ def parse_requirements(filename):
 
 install_reqs = parse_requirements("requirements.txt")
 test_reqs = parse_requirements("test-requirements.txt")
+extras_require={
+    'mesa': ['mesa>=0.8.9'],
+    'geo': ['scipy>=1.3'],
+    'web': ['tornado']
+}
+extras_require['all'] = [dep for package in extras_require.values() for dep in package]
 
 
 setup(
@@ -40,15 +46,13 @@ setup(
         'Operating System :: POSIX',
         'Programming Language :: Python :: 3'],
     install_requires=install_reqs,
-    extras_require={
-        'web': ['tornado']
-
-    },
+    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 @@
-0.14.8
+0.20.7

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__
 
@@ -11,72 +14,225 @@ try:
 except NameError:
     basestring = str
 
+from .agents import *
 from . import agents
 from .simulation import *
 from .environment import Environment
-from .history import History
 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,
+):
     import argparse
     from . import simulation
 
-    logging.basicConfig(level=logging.INFO)
-    logging.info('Running SOIL version: {}'.format(__version__))
+    logger.info("Running SOIL version: {}".format(__version__))
 
-    parser = argparse.ArgumentParser(description='Run a SOIL simulation')
-    parser.add_argument('file', type=str,
-                        nargs="?",
-                        default='simulation.yml',
-                        help='python module containing the simulation configuration.')
-    parser.add_argument('--module', '-m', type=str,
-                        help='file containing the code of any custom agents.')
-    parser.add_argument('--dry-run', '--dry', action='store_true',
-                        help='Do not store the results of the simulation.')
-    parser.add_argument('--pdb', action='store_true',
-                        help='Use a pdb console in case of exception.')
-    parser.add_argument('--graph', '-g', action='store_true',
-                        help='Dump GEXF graph. Defaults to false.')
-    parser.add_argument('--csv', action='store_true',
-                        help='Dump history in CSV format. Defaults to false.')
-    parser.add_argument('--output', '-o', type=str, default="soil_output",
-                        help='folder to write results to. It defaults to the current directory.')
-    parser.add_argument('--synchronous', action='store_true',
-                        help='Run trials serially and synchronously instead of in parallel. Defaults to false.')
-    parser.add_argument('-e', '--exporter', action='append',
-                        help='Export environment and/or simulations using this exporter')
+    parser = argparse.ArgumentParser(description="Run a SOIL simulation")
+    parser.add_argument(
+        "file",
+        type=str,
+        nargs="?",
+        default=cfg,
+        help="Configuration file for the simulation (e.g., YAML or JSON)",
+    )
+    parser.add_argument(
+        "--version", action="store_true", help="Show version info and exit"
+    )
+    parser.add_argument(
+        "--module",
+        "-m",
+        type=str,
+        help="file containing the code of any custom agents.",
+    )
+    parser.add_argument(
+        "--dry-run",
+        "--dry",
+        action="store_true",
+        help="Do not store the results of the simulation to disk, show in terminal instead.",
+    )
+    parser.add_argument(
+        "--pdb", action="store_true", help="Use a pdb console in case of exception."
+    )
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="Run a customized version of a pdb console to debug a simulation.",
+    )
+    parser.add_argument(
+        "--graph",
+        "-g",
+        action="store_true",
+        help="Dump each trial's network topology as a GEXF graph. Defaults to false.",
+    )
+    parser.add_argument(
+        "--csv",
+        action="store_true",
+        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()
+    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
 
-    logging.info('Loading config file: {}'.format(args.file))
+    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
-        simulation.run_from_config(args.file,
-                                   dry_run=args.dry_run,
-                                   exporters=exporters,
-                                   parallel=(not args.synchronous),
-                                   outdir=args.output,
-                                   exporter_params=exp_params)
-    except Exception:
+
+        if not os.path.exists(args.file):
+            logger.error("Please, input a valid file")
+            return
+
+        for sim in simulation.iter_from_config(
+            args.file,
+            dry_run=args.dry_run,
+            exporters=exporters,
+            parallel=parallel,
+            outdir=output,
+            exporter_params=exp_params,
+            **kwargs,
+        ):
+            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__':
-    main()
+@contextmanager
+def easy(cfg, pdb=False, debug=False, **kwargs):
+    ex = None
+    try:
+        yield main(cfg, **kwargs)[0]
+    except Exception as e:
+        if os.environ.get("SOIL_POSTMORTEM"):
+            from .debugging import post_mortem
+
+            print(traceback.format_exc())
+            post_mortem()
+        ex = e
+    finally:
+        if ex:
+            raise ex
+
+
+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

@@ -1,40 +1,31 @@
-import random
-from . import BaseAgent
+from . import FSM, state, default_state
 
 
-class BassModel(BaseAgent):
+class BassModel(FSM):
     """
     Settings:
         innovation_prob
         imitation_prob
     """
 
-    def __init__(self, environment, agent_id, state):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        env_params = environment.environment_params
-        self.state['sentimentCorrelation'] = 0
+    sentimentCorrelation = 0
 
     def step(self):
         self.behaviour()
 
-    def behaviour(self):
-        # Outside effects
-        if random.random() < self.state_params['innovation_prob']:
-            if self.state['id'] == 0:
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
-            else:
-                pass
-
-            return
-
-        # Imitation effects
-        if self.state['id'] == 0:
-            aware_neighbors = self.get_neighboring_agents(state_id=1)
+    @default_state
+    @state
+    def innovation(self):
+        if self.prob(self.innovation_prob):
+            self.sentimentCorrelation = 1
+            return self.aware
+        else:
+            aware_neighbors = self.get_neighboring_agents(state_id=self.aware.id)
             num_neighbors_aware = len(aware_neighbors)
-            if random.random() < (self.state_params['imitation_prob']*num_neighbors_aware):
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
+            if self.prob((self["imitation_prob"] * num_neighbors_aware)):
+                self.sentimentCorrelation = 1
+                return self.aware
 
-            else:
-                pass
+    @state
+    def aware(self):
+        self.die()

soil/agents/BigMarketModel.py

@@ -1,57 +1,59 @@
-import random
-from . import BaseAgent
+from . import FSM, state, default_state
 
 
-class BigMarketModel(BaseAgent):
+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, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.enterprises = environment.environment_params['enterprises']
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.enterprises = self.env.environment_params["enterprises"]
         self.type = ""
-        self.number_of_enterprises = len(environment.environment_params['enterprises'])
 
-        if self.id < self.number_of_enterprises:  # Enterprises
-            self.state['id'] = self.id
+        if self.id < len(self.enterprises):  # Enterprises
+            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.state['id'] = self.number_of_enterprises
             self.type = "User"
-            self.tweet_probability = environment.environment_params['tweet_probability_users']
-            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
+            self.set_state(self.user.id)
+            self.tweet_probability = environment.environment_params[
+                "tweet_probability_users"
+            ]
+            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
 
-    def step(self):
+    @state
+    def enterprise(self):
 
-        if self.id < self.number_of_enterprises:  # Enterprise
-            self.enterpriseBehaviour()
-        else:  # Usuario
-            self.userBehaviour()
-            for i in range(self.number_of_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 enterpriseBehaviour(self):
-
-        if random.random() < self.tweet_probability:  # Tweets
-            aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbour users
+        if self.random.random() < self.tweet_probability:  # Tweets
+            aware_neighbors = self.get_neighboring_agents(
+                state_id=self.number_of_enterprises
+            )  # Nodes neighbour users
             for x in aware_neighbors:
-                if random.uniform(0,10) < 5:
+                if self.random.uniform(0, 10) < 5:
                     x.sentiment_about[self.id] += 0.1  # Increments for enterprise
                 else:
                     x.sentiment_about[self.id] -= 0.1  # Decrements for enterprise
@@ -59,37 +61,49 @@ class BigMarketModel(BaseAgent):
                 # 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]
 
-    def userBehaviour(self):
-
-        if random.random() < self.tweet_probability:  # Tweets
-            if random.random() < self.tweet_relevant_probability:  # Tweets something relevant
+    @state
+    def user(self):
+        if self.random.random() < self.tweet_probability:  # Tweets
+            if (
+                self.random.random() < self.tweet_relevant_probability
+            ):  # Tweets something relevant
                 # Tweet probability per enterprise
-                for i in range(self.number_of_enterprises):
-                    random_num = random.random()
+                for i in range(len(self.enterprises)):
+                    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)
+            self.attrs[
+                "sentiment_enterprise_%s" % self.enterprises[i]
+            ] = self.sentiment_about[i]
 
-    def userTweets(self,sentiment,enterprise):
-        aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbours users
+    def userTweets(self, sentiment, enterprise):
+        aware_neighbors = self.get_neighboring_agents(
+            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
 
@@ -99,4 +113,6 @@ class BigMarketModel(BaseAgent):
             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

@@ -1,38 +1,40 @@
-from . import BaseAgent
+from . import NetworkAgent
 
 
-class CounterModel(BaseAgent):
+class CounterModel(NetworkAgent):
     """
     Dummy behaviour. It counts the number of nodes in the network and neighbors
     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_all_agents()))
+        total = len(list(self.model.schedule._agents))
         neighbors = len(list(self.get_neighboring_agents()))
-        self['times'] = self.get('times', 0) + 1
-        self['neighbors'] = neighbors
-        self['total'] = total
+        self["times"] = self.get("times", 0) + 1
+        self["neighbors"] = neighbors
+        self["total"] = total
 
 
-class AggregatedCounter(BaseAgent):
+class AggregatedCounter(NetworkAgent):
     """
     Dummy behaviour. It counts the number of nodes in the network and neighbors
     in each step and adds it to its state.
     """
 
-    defaults = {
-        'times': 0,
-        'neighbors': 0,
-        'total': 0
-    }
+    times = 0
+    neighbors = 0
+    total = 0
 
     def step(self):
         # Outside effects
-        self['times'] += 1
+        self["times"] += 1
         neighbors = len(list(self.get_neighboring_agents()))
-        self['neighbors'] += neighbors
-        total = len(list(self.get_all_agents()))
-        self['total'] += total
-        self.debug('Running for step: {}. Total: {}'.format(self.now, total))
+        self["neighbors"] += neighbors
+        total = len(list(self.model.schedule.agents))
+        self["total"] += total
+        self.debug("Running for step: {}. Total: {}".format(self.now, total))

soil/agents/Geo.py

@@ -0,0 +1,21 @@
+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."""
+
+    def geo_search(self, radius, node=None, center=False, **kwargs):
+        """Get a list of nodes whose coordinates are closer than *radius* to *node*."""
+        node = as_node(node if node is not None else self)
+
+        G = self.subgraph(**kwargs)
+
+        pos = nx.get_node_attributes(G, "pos")
+        if not pos:
+            return []
+        nodes, coords = list(zip(*pos.items()))
+        kdtree = KDTree(coords)  # Cannot provide generator.
+        indices = kdtree.query_ball_point(pos[node], radius)
+        return [nodes[i] for i in indices if center or (nodes[i] != node)]

soil/agents/IndependentCascadeModel.py

@@ -1,4 +1,3 @@
-import random
 from . import BaseAgent
 
 
@@ -10,12 +9,12 @@ class IndependentCascadeModel(BaseAgent):
         imitation_prob
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.innovation_prob = environment.environment_params['innovation_prob']
-        self.imitation_prob = environment.environment_params['imitation_prob']
-        self.state['time_awareness'] = 0
-        self.state['sentimentCorrelation'] = 0
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.innovation_prob = self.env.environment_params["innovation_prob"]
+        self.imitation_prob = self.env.environment_params["imitation_prob"]
+        self.state["time_awareness"] = 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.state['id'] == 0:
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
-                self.state['time_awareness'] = self.env.now  # To know when they have been infected
+        if self.prob(self.innovation_prob):
+            if self.state["id"] == 0:
+                self.state["id"] = 1
+                self.state["sentimentCorrelation"] = 1
+                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)
             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):
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
+            if self.prob(self.imitation_prob * num_neighbors_aware):
+                self.state["id"] = 1
+                self.state["sentimentCorrelation"] = 1
             else:
                 pass
 

soil/agents/ModelM2.py

@@ -1,4 +1,3 @@
-import random
 import numpy as np
 from . import BaseAgent
 
@@ -21,36 +20,52 @@ class SpreadModelM2(BaseAgent):
         prob_generate_anti_rumor
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=environment, unique_id=unique_id, state=state)
 
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
-                                                           environment.environment_params['standard_variance'])
+        # Use a single generator with the same seed as `self.random`
+        random = np.random.default_rng(seed=self._seed)
+        self.prob_neutral_making_denier = random.normal(
+            environment.environment_params["prob_neutral_making_denier"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
-                                            environment.environment_params['standard_variance'])
+        self.prob_infect = random.normal(
+            environment.environment_params["prob_infect"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
-                                                            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_cured_healing_infected = random.normal(
+            environment.environment_params["prob_cured_healing_infected"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_cured_vaccinate_neutral = random.normal(
+            environment.environment_params["prob_cured_vaccinate_neutral"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
-                                                                 environment.environment_params['standard_variance'])
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
-                                                                  environment.environment_params['standard_variance'])
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
-                                                         environment.environment_params['standard_variance'])
+        self.prob_vaccinated_healing_infected = random.normal(
+            environment.environment_params["prob_vaccinated_healing_infected"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_vaccinated_vaccinate_neutral = random.normal(
+            environment.environment_params["prob_vaccinated_vaccinate_neutral"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_generate_anti_rumor = random.normal(
+            environment.environment_params["prob_generate_anti_rumor"],
+            environment.environment_params["standard_variance"],
+        )
 
     def step(self):
 
-        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):
@@ -58,50 +73,50 @@ class SpreadModelM2(BaseAgent):
         # Infected
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         if len(infected_neighbors) > 0:
-            if random.random() < self.prob_neutral_making_denier:
-                self.state['id'] = 3   # Vaccinated making denier
+            if self.prob(self.prob_neutral_making_denier):
+                self.state["id"] = 3  # Vaccinated making denier
 
     def infected_behaviour(self):
 
         # Neutral
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
-                neighbor.state['id'] = 1  # Infected
+            if self.prob(self.prob_infect):
+                neighbor.state["id"] = 1  # Infected
 
     def cured_behaviour(self):
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+            if self.prob(self.prob_cured_vaccinate_neutral):
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_cured_healing_infected):
+                neighbor.state["id"] = 2  # Cured
 
     def vaccinated_behaviour(self):
 
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_cured_healing_infected):
+                neighbor.state["id"] = 2  # Cured
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+            if self.prob(self.prob_cured_vaccinate_neutral):
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Generate anti-rumor
         infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_generate_anti_rumor):
+                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)
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.prob_neutral_making_denier = np.random.normal(
+            environment.environment_params["prob_neutral_making_denier"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
-                                                           environment.environment_params['standard_variance'])
+        self.prob_infect = np.random.normal(
+            environment.environment_params["prob_infect"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
-                                            environment.environment_params['standard_variance'])
+        self.prob_cured_healing_infected = np.random.normal(
+            environment.environment_params["prob_cured_healing_infected"],
+            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_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
-                                                            environment.environment_params['standard_variance'])
-        self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
-                                                             environment.environment_params['standard_variance'])
-
-        self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
-                                                                 environment.environment_params['standard_variance'])
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
-                                                                  environment.environment_params['standard_variance'])
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
-                                                         environment.environment_params['standard_variance'])
+        self.prob_vaccinated_healing_infected = np.random.normal(
+            environment.environment_params["prob_vaccinated_healing_infected"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_vaccinated_vaccinate_neutral = np.random.normal(
+            environment.environment_params["prob_vaccinated_vaccinate_neutral"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_generate_anti_rumor = np.random.normal(
+            environment.environment_params["prob_generate_anti_rumor"],
+            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)
         if len(infected_neighbors) > 0:
-            if random.random() < self.prob_neutral_making_denier:
-                self.state['id'] = 3   # Vaccinated making denier
+            if self.random(self.prob_neutral_making_denier):
+                self.state["id"] = 3  # Vaccinated making denier
 
     def infected_behaviour(self):
 
         # Neutral
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
-                neighbor.state['id'] = 1  # Infected
-        self.state['visible'] = False
+            if self.prob(self.prob_infect):
+                neighbor.state["id"] = 1  # Infected
+        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)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+            if self.prob(self.prob_cured_vaccinate_neutral):
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_cured_healing_infected):
+                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)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_cured_healing_infected):
+                neighbor.state["id"] = 2  # Cured
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+            if self.prob(self.prob_cured_vaccinate_neutral):
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Generate anti-rumor
         infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_generate_anti_rumor):
+                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)
         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)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_generate_anti_rumor):
+                neighbor.state["id"] = 2  # Cured
             neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
             for neighbor in neutral_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
-                    neighbor.state['id'] = 3  # Vaccinated
+                if self.prob(self.prob_generate_anti_rumor):
+                    neighbor.state["id"] = 3  # Vaccinated
             infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
             for neighbor in infected_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
-                    neighbor.state['id'] = 2  # Cured
+                if self.prob(self.prob_generate_anti_rumor):
+                    neighbor.state["id"] = 2  # Cured
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+            if self.prob(self.prob_cured_vaccinate_neutral):
+                neighbor.state["id"] = 3  # Vaccinated

soil/agents/SISaModel.py

@@ -1,4 +1,3 @@
-import random
 import numpy as np
 from . import FSM, state
 
@@ -7,87 +6,99 @@ 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, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    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'],
-                                                             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'])
+        random = np.random.default_rng(seed=self._seed)
 
-        self.discontent_neutral = np.random.normal(self.env['discontent_neutral'],
-                                                   self.env['standard_variance'])
-        self.discontent_content = np.random.normal(self.env['discontent_content'],
-                                                   self.env['variance_d_c'])
+        self.neutral_discontent_spon_prob = random.normal(
+            self.env["neutral_discontent_spon_prob"], self.env["standard_variance"]
+        )
+        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.env['variance_c_d'])
-        self.content_neutral = np.random.normal(self.env['content_neutral'],
-                                                self.env['standard_variance'])
+        self.discontent_neutral = random.normal(
+            self.env["discontent_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)
-        if random.random() < discontent_neighbors * self.neutral_discontent_infected_prob:
+        if self.prob(scontent_neighbors * self.neutral_discontent_infected_prob):
             return self.discontent
         content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
-        if random.random() < content_neighbors * self.neutral_content_infected_prob:
+        if self.prob(s * self.neutral_content_infected_prob):
             return self.content
         return self.neutral
 
     @state
     def discontent(self):
         # Healing
-        if random.random() < self.discontent_neutral:
+        if self.prob(self.discontent_neutral):
             return self.neutral
 
         # Superinfected
         content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
-        if random.random() < content_neighbors * self.discontent_content:
+        if self.prob(s * self.discontent_content):
             return self.content
         return self.discontent
 
     @state
     def content(self):
         # Healing
-        if random.random() < self.content_neutral:
+        if self.prob(self.content_neutral):
             return self.neutral
 
         # Superinfected
-        discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent.id)
-        if random.random() < discontent_neighbors * self.content_discontent:
+        discontent_neighbors = self.count_neighboring_agents(
+            state_id=self.discontent.id
+        )
+        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, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.outside_effects_prob = environment.environment_params['outside_effects_prob']
-        self.anger_prob = environment.environment_params['anger_prob']
-        self.joy_prob = environment.environment_params['joy_prob']
-        self.sadness_prob = environment.environment_params['sadness_prob']
-        self.disgust_prob = environment.environment_params['disgust_prob']
-        self.state['time_awareness'] = []
+    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.anger_prob = environment.environment_params["anger_prob"]
+        self.joy_prob = environment.environment_params["joy_prob"]
+        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['sentimentCorrelation'] = 0
+            self.state["time_awareness"].append(
+                0
+            )  # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
+        self.state["sentimentCorrelation"] = 0
 
     def step(self):
         self.behaviour()
@@ -40,63 +43,73 @@ class SentimentCorrelationModel(BaseAgent):
 
         angry_neighbors = self.get_neighboring_agents(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)
         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)
         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)
         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)
-        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)
+        anger_prob = self.anger_prob + (
+            len(angry_neighbors_1_time_step) * self.anger_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:
-            self.state['id'] = random.randint(1, 4)
+        if num < outside_effects_prob:
+            self.state["id"] = self.random.randint(1, 4)
 
-            self.state['sentimentCorrelation'] = self.state['id'] # It is stored when it has been infected for the dynamic network
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-            self.state['sentiment'] = self.state['id']
+            self.state["sentimentCorrelation"] = self.state[
+                "id"
+            ]  # It is stored when it has been infected for the dynamic network
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
+            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['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"] = 2
+            self.state["sentimentCorrelation"] = 2
+            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:
 
-            self.state['id'] = 2
-            self.state['sentimentCorrelation'] = 2
-            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):
+            self.state["id"] = 3
+            self.state["sentimentCorrelation"] = 3
+            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'] = 3
-            self.state['sentimentCorrelation'] = 3
-            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["sentimentCorrelation"] = 4
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
 
-            self.state['id'] = 4
-            self.state['sentimentCorrelation'] = 4
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-
-        self.state['sentiment'] = self.state['id']
+        self.state["sentiment"] = self.state["id"]

soil/analysis.py

@@ -1,166 +0,0 @@
-import pandas as pd
-
-import glob
-import yaml
-from os.path import join
-
-from . import serialization, 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, '*.db.sqlite'))):
-                df = read_sql(trial_data, **kwargs)
-                yield config_file, df, config
-
-
-def read_sql(db, *args, **kwargs):
-    h = history.History(db_path=db, backup=False)
-    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):
-    '''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_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):
-    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):
-    if keys:
-        df = df[list(keys)]
-    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_value(df, *keys, aggfunc='sum'):
-    if keys:
-        df = df[list(keys)]
-    return df.groupby(axis=1, level=0).agg(aggfunc)
-
-
-def plot_all(*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
-        df.plot(title=config['name'])
-        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):
-        p = func(df, *keys, **kwargs)
-        p.plot(title=config['name'])
-        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

@@ -0,0 +1,6 @@
+from mesa import DataCollector as MDC
+
+
+class SoilDataCollector(MDC):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)

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", [])
+        skip.append("soil")
+        skip.append("contextlib")
+        if skip_soil:
+            skip.append("soil.*")
+            skip.append("mesa.*")
+        super(Debug, self).__init__(*args, skip=skip, **kwargs)
+        self.prompt = "[soil-pdb] "
+
+    @staticmethod
+    def _soil_agents(model, attrs=None, pretty=True, **kwargs):
+        for agent in model.agents(**kwargs):
+            d = agent
+            print(" - " + indent(agent.to_str(keys=attrs, pretty=pretty), "  "))
+
+    @wrapcmd
+    def do_soil_agents():
+        return Debug._soil_agents(model, attrs=attrs or None)
+
+    do_sa = do_soil_agents
+
+    @wrapcmd
+    def do_soil_list():
+        return Debug._soil_agents(model, attrs=["state_id"], pretty=False)
+
+    do_sl = do_soil_list
+
+    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

@@ -1,356 +1,318 @@
+from __future__ import annotations
+
 import os
 import sqlite3
-import time
-import csv
+import math
 import random
-import simpy
-import yaml
-import tempfile
-import pandas as pd
+import logging
+import inspect
+
+from typing import Any, Dict, Optional, Union
+from collections import namedtuple
+from time import time as current_time
 from copy import deepcopy
-from collections import Counter
 from networkx.readwrite import json_graph
 
+
 import networkx as nx
-import nxsim
 
-from . import serialization, agents, analysis, history, utils
+from mesa import Model
+from mesa.datacollection import DataCollector
 
-# These properties will be copied when pickling/unpickling the environment
-_CONFIG_PROPS = [ 'name',
-                 'states',
-                 'default_state',
-                 'interval',
-                 ]
+from . import agents as agentmod, config, serialization, utils, time, network
 
-class Environment(nxsim.NetworkEnvironment):
+
+class BaseEnvironment(Model):
     """
-    The environment is key in a simulation. It contains the network topology,
-    a reference to network and environment agents, as well as the environment
-    params, which are used as shared state between agents.
+    The environment is key in a simulation. It controls how agents interact,
+    and what information is available to them.
+
+    This is an opinionated version of `mesa.Model` class, which adds many
+    convenience methods and abstractions.
 
     The environment parameters and the state of every agent can be accessed
-    both by using the environment as a dictionary or with the environment's 
+    both by using the environment as a dictionary and with the environment's
     :meth:`soil.environment.Environment.get` method.
     """
 
-    def __init__(self, name=None,
-                 network_agents=None,
-                 environment_agents=None,
-                 states=None,
-                 default_state=None,
-                 interval=1,
-                 seed=None,
-                 topology=None,
-                 *args, **kwargs):
-        self.name = name or 'UnnamedEnvironment'
-        if isinstance(states, list):
-            states = dict(enumerate(states))
-        self.states = deepcopy(states) if states else {}
-        self.default_state = deepcopy(default_state) or {}
-        if not topology:
-            topology = nx.Graph()
-        super().__init__(*args, topology=topology, **kwargs)
-        self._env_agents = {}
+    def __init__(
+        self,
+        id="unnamed_env",
+        seed="default",
+        schedule=None,
+        dir_path=None,
+        interval=1,
+        agent_class=None,
+        agents: [tuple[type, Dict[str, Any]]] = {},
+        agent_reporters: Optional[Any] = None,
+        model_reporters: Optional[Any] = None,
+        tables: Optional[Any] = None,
+        **env_params,
+    ):
+
+        super().__init__(seed=seed)
+        self.env_params = env_params or {}
+
+        self.current_id = -1
+
+        self.id = id
+
+        self.dir_path = dir_path or os.getcwd()
+
+        if schedule is None:
+            schedule = time.TimedActivation(self)
+        self.schedule = schedule
+
+        self.agent_class = agent_class or agentmod.BaseAgent
+
         self.interval = interval
-        self._history = history.History(name=self.name,
-                                        backup=True)
-        # Add environment agents first, so their events get
-        # executed before network agents
-        self.environment_agents = environment_agents or []
-        self.network_agents = network_agents or []
-        self['SEED'] = seed or time.time()
-        random.seed(self['SEED'])
+        self.init_agents(agents)
+
+        self.logger = utils.logger.getChild(self.id)
+
+        self.datacollector = DataCollector(
+            model_reporters=model_reporters,
+            agent_reporters=agent_reporters,
+            tables=tables,
+        )
+
+    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):
-        yield from self.environment_agents
-        yield from self.network_agents
+        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 environment_agents(self):
-        for ref in self._env_agents.values():
-            yield ref
+    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"
+        )
 
-    @environment_agents.setter
-    def environment_agents(self, environment_agents):
-        # Set up environmental agent
-        self._env_agents = {}
-        for item in environment_agents:
-            kwargs = deepcopy(item)
-            atype = kwargs.pop('agent_type')
-            kwargs['agent_id'] = kwargs.get('agent_id', atype.__name__)
-            kwargs['state'] = kwargs.get('state', {})
-            a = atype(environment=self, **kwargs)
-            self._env_agents[a.id] = a
+    def add_agent(self, unique_id=None, **kwargs):
+        if unique_id is None:
+            unique_id = self.next_id()
+
+        kwargs["unique_id"] = unique_id
+        a = self._agent_from_dict(kwargs)
+
+        self.schedule.add(a)
+        return a
+
+    def log(self, message, *args, level=logging.INFO, **kwargs):
+        if not self.logger.isEnabledFor(level):
+            return
+        message = message + " ".join(str(i) for i in args)
+        message = " @{:>3}: {}".format(self.now, message)
+        for k, v in kwargs:
+            message += " {k}={v} ".format(k, v)
+        extra = {}
+        extra["now"] = self.now
+        extra["id"] = self.id
+        return self.logger.log(level, message, extra=extra)
+
+    def step(self):
+        """
+        Advance one step in the simulation, and update the data collection and scheduler appropriately
+        """
+        super().step()
+        self.logger.info(
+            f"--- Step: {self.schedule.steps:^5} - Time: {self.now:^5} ---"
+        )
+        self.schedule.step()
+        self.datacollector.collect(self)
+
+    def __contains__(self, key):
+        return key in self.env_params
+
+    def get(self, key, default=None):
+        """
+        Get the value of an environment attribute.
+        Return `default` if the value is not set.
+        """
+        return self.env_params.get(key, default)
+
+    def __getitem__(self, key):
+        return self.env_params.get(key)
+
+    def __setitem__(self, key, value):
+        return self.env_params.__setitem__(key, value)
+
+    def __str__(self):
+        return str(self.env_params)
+
+
+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 __init__(
+        self, *args, topology: Union[config.NetConfig, nx.Graph] = None, **kwargs
+    ):
+        agents = kwargs.pop("agents", None)
+        super().__init__(*args, agents=None, **kwargs)
+
+        self._set_topology(topology)
+
+        self.init_agents(agents)
+
+    def init_agents(self, *args, **kwargs):
+        """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 _init_node(self, agent):
+        """
+        Make sure the node for a given agent has the proper attributes.
+        """
+        self.G.nodes[agent.node_id]["agent"] = agent
+
+    def _agent_dict_from_config(self, cfg):
+        return agentmod.from_config(cfg, topology=self.G, random=self.random)
+
+    def _agent_from_dict(self, agent, unique_id=None):
+        agent = dict(agent)
+
+        if not agent.get("topology", False):
+            return super()._agent_from_dict(agent)
+
+        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 i in self.G.nodes():
-            node = self.G.nodes[i]
-            if 'agent' in node:
-                yield node['agent']
+        for a in self.schedule._agents:
+            if isinstance(a, agentmod.NetworkAgent):
+                yield a
 
-    @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_distribution=network_agents)
+    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}"
 
-    def init_agent(self, agent_id, agent_distribution):
-        node = self.G.nodes[agent_id]
-        init = False
-        state = dict(node)
+        if node_id not in self.G.nodes:
+            self.G.add_node(node_id)
 
-        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']
+        assert "agent" not in self.G.nodes[node_id]
+        self.G.nodes[node_id]["agent"] = None  # Reserve
 
-        if agent_type:
-            agent_type = agents.deserialize_type(agent_type)
-        elif agent_distribution:
-            agent_type, state = agents._agent_from_distribution(agent_distribution, agent_id=agent_id)
-        else:
-            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(environment=self,
-                           agent_id=agent_id,
-                           state=state)
-        node['agent'] = a
+        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_node(self, agent_type, state=None):
-        agent_id = int(len(self.G.nodes()))
-        self.G.add_node(agent_id)
-        a = self.set_agent(agent_id, agent_type, state)
-        a['visible'] = True
+    def add_agent(self, *args, **kwargs):
+        a = super().add_agent(*args, **kwargs)
+        if "node_id" in a:
+            if a.node_id == 24:
+                import pdb
+
+                pdb.set_trace()
+            assert self.G.nodes[a.node_id]["agent"] == a
         return a
 
-    def add_edge(self, agent1, agent2, start=None, **attrs):
-        if hasattr(agent1, 'id'):
-            agent1 = agent1.id
-        if hasattr(agent2, 'id'):
-            agent2 = agent2.id
-        start = start or self.now
-        return self.G.add_edge(agent1, agent2, **attrs)
+    def agent_for_node_id(self, node_id):
+        return self.G.nodes[node_id].get("agent")
 
-    def run(self, *args, **kwargs):
-        self._save_state()
-        self.log_stats()
-        super().run(*args, **kwargs)
-        self._history.flush_cache()
-        self.log_stats()
-
-    def _save_state(self, now=None):
-        serialization.logger.debug('Saving state @{}'.format(self.now))
-        self._history.save_records(self.state_to_tuples(now=now))
-
-    def save_state(self):
-        '''
-        :DEPRECATED:
-        Periodically save the state of the environment and the agents.
-        '''
-        self._save_state()
-        while self.peek() != simpy.core.Infinity:
-            delay = max(self.peek() - self.now, self.interval)
-            serialization.logger.debug('Step: {}'.format(self.now))
-            ev = self.event()
-            ev._ok = True
-            # Schedule the event with minimum priority so
-            # that it executes before all agents
-            self.schedule(ev, -999, delay)
-            yield ev
-            self._save_state()
-
-    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 = history.Key(*key)
-            self._history.save_record(*k,
-                                      value=value)
-            return
-        self.environment_params[key] = value
-        self._history.save_record(agent_id='env',
-                                  t_step=self.now,
-                                  key=key,
-                                  value=value)
-
-    def __contains__(self, key):
-        return key in self.environment_params
-
-    def get(self, key, default=None):
-        '''
-        Get the value of an environment attribute in a
-        given point in the simulation (history).
-        If key is an attribute name, this method returns
-        the current value.
-        To get values at other times, use a
-        :meth: `soil.history.Key` tuple.
-        '''
-        return self[key] if key in self else default
-
-    def get_agent(self, agent_id):
-        return self.G.nodes[agent_id]['agent']
-
-    def get_agents(self, nodes=None):
-        if nodes is None:
-            return list(self.agents)
-        return [self.G.nodes[i]['agent'] for i in nodes]
-
-    def dump_csv(self, f):
-        with utils.open_or_reuse(f, 'w') as f:
-            cr = csv.writer(f)
-            cr.writerow(('agent_id', 't_step', 'key', 'value'))
-            for i in self.history_to_tuples():
-                cr.writerow(i)
-
-    def dump_gexf(self, f):
-        G = self.history_to_graph()
-        # Workaround for geometric models
-        # See soil/soil#4
-        for node in G.nodes():
-            if 'pos' in G.nodes[node]:
-                G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}}
-                del (G.nodes[node]['pos'])
-
-        nx.write_gexf(G, f, version="1.2draft")
-
-    def dump(self, *args, formats=None, **kwargs):
-        if not formats:
-            return
-        functions = {
-            'csv': self.dump_csv,
-            'gexf': self.dump_gexf
-        }
-        for f in formats:
-            if f in functions:
-                functions[f](*args, **kwargs)
-            else:
-                raise ValueError('Unknown format: {}'.format(f))
-
-    def dump_sqlite(self, f):
-        return self._history.dump(f)
-
-    def state_to_tuples(self, now=None):
-        if now is None:
-            now = self.now
-        for k, v in self.environment_params.items():
-            yield history.Record(agent_id='env',
-                                 t_step=now,
-                                 key=k,
-                                 value=v)
-        for agent in self.agents:
-            for k, v in agent.state.items():
-                yield history.Record(agent_id=agent.id,
-                                     t_step=now,
-                                     key=k,
-                                     value=v)
-
-    def history_to_tuples(self):
-        return self._history.to_tuples()
-
-    def history_to_graph(self):
-        G = nx.Graph(self.G)
-
-        for agent in self.network_agents:
-
-            attributes = {'agent': str(agent.__class__)}
-            lastattributes = {}
-            spells = []
-            lastvisible = False
-            laststep = None
-            history = self[agent.id, None, None]
-            if not history:
+    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)):
-                if attribute == 'visible':
-                    nowvisible = value
-                    if nowvisible and not lastvisible:
-                        laststep = t_step
-                    if not nowvisible and lastvisible:
-                        spells.append((laststep, t_step))
-
-                    lastvisible = nowvisible
-                    continue
-                key = 'attr_' + attribute
-                if key not in attributes:
-                    attributes[key] = list()
-                if key not in lastattributes:
-                    lastattributes[key] = (value, t_step)
-                elif lastattributes[key][0] != value:
-                    last_value, laststep = lastattributes[key]
-                    commit_value = (last_value, laststep, t_step)
-                    if key not in attributes:
-                        attributes[key] = list()
-                    attributes[key].append(commit_value)
-                    lastattributes[key] = (value, t_step)
-            for k, v in lastattributes.items():
-                attributes[k].append((v[0], v[1], None))
-            if lastvisible:
-                spells.append((laststep, None))
-            if spells:
-                G.add_node(agent.id, spells=spells, **attributes)
-            else:
-                G.add_node(agent.id, **attributes)
-
-        return G
-
-    def stats(self):
-        stats = {}
-        stats['network'] = {}
-        stats['network']['n_nodes'] = self.G.number_of_nodes()
-        stats['network']['n_edges'] = self.G.number_of_edges()
-        c = Counter()
-        c.update(a.__class__.__name__ for a in self.network_agents)
-        stats['agents'] = {}
-        stats['agents']['model_count'] = dict(c)
-        c2 = Counter()
-        c2.update(a['id'] for a in self.network_agents)
-        stats['agents']['state_count'] = dict(c2)
-        stats['params'] = self.environment_params
-        return stats
-
-    def log_stats(self):
-        stats = self.stats()
-        serialization.logger.info('Environment stats: \n{}'.format(yaml.dump(stats, default_flow_style=False)))
-    
-    def __getstate__(self):
-        state = {}
-        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
-        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']
+            a_class = self.random.choices(agent_class, weights)[0]
+            self.add_agent(node_id=node_id, agent_class=a_class, **agent_params)
 
 
-SoilEnvironment = Environment
+Environment = NetworkEnvironment

soil/exporters.py

@@ -1,25 +1,20 @@
 import os
-import time
+import sys
+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
-import pandas as pd
+
 
 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
-
-
-def for_sim(simulation, names, *args, **kwargs):
-    '''Return the set of exporters for a simulation, given the exporter names'''
-    exporters = []
-    for name in names:
-        mod = deserialize(name, known_modules=['soil.exporters'])
-        exporters.append(mod(simulation, *args, **kwargs))
-    return exporters
+from . import utils, network
 
 
 class DryRunner(BytesIO):
@@ -30,43 +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):
-        logger.info('**Not** written to {} (dry run mode):\n\n{}\n\n'.format(self.__fname,
-                                                                       self.getvalue().decode()))
+        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
+            )
+        )
         super().close()
 
 
 class Exporter:
-    '''
+    """
     Interface for all exporters. It is not necessary, but it is useful
     if you don't plan to implement all the methods.
-    '''
+    """
 
     def __init__(self, simulation, outdir=None, dry_run=None, copy_to=None):
-        self.sim = simulation
-        outdir = outdir or os.path.join(os.getcwd(), 'soil_output')
-        self.outdir = os.path.join(outdir,
-                                   simulation.group or '',
-                                   simulation.name)
+        self.simulation = simulation
+        outdir = outdir or os.path.join(os.getcwd(), "soil_output")
+        self.outdir = os.path.join(outdir, 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):
-        '''Method to call when the simulation starts'''
+    def sim_start(self):
+        """Method to call when the simulation starts"""
+        pass
 
-    def end(self):
-        '''Method to call when the simulation ends'''
+    def sim_end(self):
+        """Method to call when the simulation ends"""
+        pass
+
+    def trial_start(self, env):
+        """Method to call when a trial start"""
+        pass
 
     def trial_end(self, env):
-        '''Method to call when a trial ends'''
+        """Method to call when a trial ends"""
+        pass
 
-    def output(self, f, mode='w', **kwargs):
+    def output(self, f, mode="w", **kwargs):
         if self.dry_run:
             f = DryRunner(f, copy_to=self.copy_to)
         else:
@@ -77,103 +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):
-        if not self.dry_run:
-            logger.info('Dumping results to %s', self.outdir)
-            self.sim.dump_yaml(outdir=self.outdir)
-        else:
-            logger.info('NOT dumping results')
+    def sim_start(self):
+        if self.dry_run:
+            logger.info("NOT dumping results")
+            return
+        logger.info("Dumping results to %s", self.outdir)
+        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_end(self, env):
-        if not self.dry_run:
-            with timer('Dumping simulation {} trial {}'.format(self.sim.name,
-                                                               env.name)):
-                with self.output('{}.sqlite'.format(env.name), mode='wb') as f:
-                    env.dump_sqlite(f)
+        if self.dry_run:
+            logger.info("Running in DRY_RUN mode, the database will NOT be created")
+            return
+
+        with timer(
+            "Dumping simulation {} trial {}".format(self.simulation.name, env.id)
+        ):
+
+            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'''
+
+    """Export the state of each environment (and its agents) in a separate CSV file"""
+
     def trial_end(self, env):
-        with timer('[CSV] Dumping simulation {} trial {} @ dir {}'.format(self.sim.name,
-                                                                          env.name,
-                                                                          self.outdir)):
-            with self.output('{}.csv'.format(env.name)) as f:
-                env.dump_csv(f)
+        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_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.sim.name,
-                                                                  env.name)):
-            with self.output('{}.gexf'.format(env.name), mode='wb') as f:
-                env.dump_gexf(f)
+        with timer(
+            "[GEXF] Dumping simulation {} trial {}".format(self.simulation.name, env.id)
+        ):
+            with self.output("{}.gexf".format(env.id), mode="wb") as f:
+                network.dump_gexf(env.history_to_graph(), f)
+                self.dump_gexf(env, f)
 
 
 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):
-        with self.output('dummy', 'w') as f:
-            f.write('simulation started @ {}\n'.format(time.time()))
+    def trial_start(self, env):
+        with self.output("dummy", "w") as f:
+            f.write("trial started@ {}\n".format(current_time()))
 
     def trial_end(self, env):
-        with self.output('dummy', 'w') as f:
-            for i in env.history_to_tuples():
-                f.write(','.join(map(str, i)))
-                f.write('\n')
+        with self.output("dummy", "w") as f:
+            f.write("trial ended@ {}\n".format(current_time()))
 
-    def end(self):
-        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 distribution(Exporter):
-    '''
-    Write the distribution of agent states at the end of each trial,
-    the mean value, and its deviation.
-    '''
-
-    def start(self):
-        self.means = []
-        self.counts = []
-
-    def trial_end(self, env):
-        df = env[None, None, None].df()
-        ix = df.index[-1]
-        attrs = df.columns.levels[0]
-        vc = {}
-        stats = {}
-        for a in attrs:
-            t = df.loc[(ix, a)]
-            try:
-                self.means.append(('mean', a, t.mean()))
-            except TypeError:
-                for name, count in t.value_counts().iteritems():
-                    self.counts.append(('count', a, name, count))
-
-    def end(self):
-        dfm = pd.DataFrame(self.means, columns=['metric', 'key', 'value'])
-        dfc = pd.DataFrame(self.counts, columns=['metric', 'key', 'value', 'count'])
-        dfm = dfm.groupby(by=['key']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
-        dfc = dfc.groupby(by=['key', 'value']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
-        with self.output('counts.csv') as f:
-            dfc.to_csv(f)
-        with self.output('metrics.csv') as f:
-            dfm.to_csv(f)
-
 class graphdrawing(Exporter):
-
     def trial_end(self, env):
         # Outside effects
         f = plt.figure()
-        nx.draw(env.G, node_size=10, width=0.2, pos=nx.spring_layout(env.G, scale=100), ax=f.add_subplot(111))
-        with open('graph-{}.png'.format(env.name)) as f:
+        nx.draw(
+            env.G,
+            node_size=10,
+            width=0.2,
+            pos=nx.spring_layout(env.G, scale=100),
+            ax=f.add_subplot(111),
+        )
+        with open("graph-{}.png".format(env.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/history.py

@@ -1,315 +0,0 @@
-import time
-import os
-import pandas as pd
-import sqlite3
-import copy
-import logging
-import tempfile
-
-logger = logging.getLogger(__name__)
-
-from collections import UserDict, namedtuple
-
-from . import serialization
-from .utils import open_or_reuse
-
-
-class History:
-    """
-    Store and retrieve values from a sqlite database.
-    """
-
-    def __init__(self, name=None, db_path=None, backup=False):
-        self._db = None
-
-        if db_path is None:
-            if not name:
-                name = time.time()
-            _, db_path = tempfile.mkstemp(suffix='{}.sqlite'.format(name))
-
-        if backup and os.path.exists(db_path):
-            newname = db_path + '.backup{}.sqlite'.format(time.time())
-            os.rename(db_path, newname)
-
-        self.db_path = db_path
-
-        self.db = db_path
-
-        with self.db:
-            logger.debug('Creating database {}'.format(self.db_path))
-            self.db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step int, key text, value text text)''')
-            self.db.execute('''CREATE TABLE IF NOT EXISTS value_types (key text, value_type text)''')
-            self.db.execute('''CREATE UNIQUE INDEX IF NOT EXISTS idx_history ON history (agent_id, t_step, key);''')
-        self._dtypes = {}
-        self._tups = []
-
-    @property
-    def db(self):
-        try:
-            self._db.cursor()
-        except (sqlite3.ProgrammingError, AttributeError):
-            self.db = None  # Reset the database
-        return self._db
-
-    @db.setter
-    def db(self, db_path=None):
-        self._close()
-        db_path = db_path or self.db_path
-        if isinstance(db_path, str):
-            logger.debug('Connecting to database {}'.format(db_path))
-            self._db = sqlite3.connect(db_path)
-        else:
-            self._db = db_path
-
-    def _close(self):
-        if self._db is None:
-            return
-        self.flush_cache()
-        self._db.close()
-        self._db = None
-
-    @property
-    def dtypes(self):
-        self.read_types()
-        return {k:v[0] for k, v in self._dtypes.items()}
-
-    def save_tuples(self, tuples):
-        '''
-        Save a series of tuples, converting them to records if necessary
-        '''
-        self.save_records(Record(*tup) for tup in tuples)
-
-    def save_records(self, records):
-        '''
-        Save a collection of records
-        '''
-        for record in records:
-            if not isinstance(record, Record):
-                record = Record(*record)
-            self.save_record(*record)
-
-    def save_record(self, agent_id, t_step, key, value):
-        '''
-        Save a collection of records to the database.
-        Database writes are cached.
-        '''
-        value = self.convert(key, value)
-        self._tups.append(Record(agent_id=agent_id,
-                                 t_step=t_step,
-                                 key=key,
-                                 value=value))
-        if len(self._tups) > 100:
-            self.flush_cache()
-
-    def convert(self, key, value):
-        """Get the serialized value for a given key."""
-        if key not in self._dtypes:
-            self.read_types()
-            if key not in self._dtypes:
-                name = serialization.name(value)
-                serializer = serialization.serializer(name)
-                deserializer = serialization.deserializer(name)
-                self._dtypes[key] = (name, serializer, deserializer)
-                with self.db:
-                    self.db.execute("replace into value_types (key, value_type) values (?, ?)", (key, name))
-        return self._dtypes[key][1](value)
-
-    def recover(self, key, value):
-        """Get the deserialized value for a given key, and the serialized version."""
-        if key not in self._dtypes:
-            self.read_types()
-        if key not in self._dtypes:
-            raise ValueError("Unknown datatype for {} and {}".format(key, value))
-        return self._dtypes[key][2](value)
-
-    def flush_cache(self):
-        '''
-        Use a cache to save state changes to avoid opening a session for every change.
-        The cache will be flushed at the end of the simulation, and when history is accessed.
-        '''
-        logger.debug('Flushing cache {}'.format(self.db_path))
-        with self.db:
-            for rec in self._tups:
-                self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
-        self._tups = list()
-
-    def to_tuples(self):
-        self.flush_cache()
-        with self.db:
-            res = self.db.execute("select agent_id, t_step, key, value from history ").fetchall()
-        for r in res:
-            agent_id, t_step, key, value = r
-            value = self.recover(key, value)
-            yield agent_id, t_step, key, value
-
-    def read_types(self):
-        with self.db:
-            res = self.db.execute("select key, value_type from value_types ").fetchall()
-        for k, v in res:
-            serializer = serialization.serializer(v)
-            deserializer = serialization.deserializer(v)
-            self._dtypes[k] = (v, serializer, deserializer)
-
-    def __getitem__(self, key):
-        self.flush_cache()
-        key = Key(*key)
-        agent_ids = [key.agent_id] if key.agent_id is not None else []
-        t_steps = [key.t_step] if key.t_step is not None else []
-        keys = [key.key] if key.key is not None else []
-
-        df = self.read_sql(agent_ids=agent_ids,
-                           t_steps=t_steps,
-                           keys=keys)
-        r = Records(df, filter=key, dtypes=self._dtypes)
-        if r.resolved:
-            return r.value()
-        return r
-
-    def read_sql(self, keys=None, agent_ids=None, t_steps=None, convert_types=False, limit=-1):
-
-        self.read_types()
-
-        def escape_and_join(v):
-            if v is None:
-                return
-            return ",".join(map(lambda x: "\'{}\'".format(x), v))
-
-        filters = [("key in ({})".format(escape_and_join(keys)), keys),
-                   ("agent_id in ({})".format(escape_and_join(agent_ids)), agent_ids)
-        ]
-        filters = list(k[0] for k in filters if k[1])
-
-        last_df = None
-        if t_steps:
-            # Look for the last value before the minimum step in the query
-            min_step = min(t_steps)
-            last_filters = ['t_step < {}'.format(min_step),]
-            last_filters = last_filters + filters
-            condition = ' and '.join(last_filters)
-
-            last_query = '''
-            select h1.*
-            from history h1
-            inner join (
-            select agent_id, key, max(t_step) as t_step
-            from history
-            where {condition}
-            group by agent_id, key
-            ) h2
-            on h1.agent_id = h2.agent_id  and
-               h1.key      = h2.key       and
-               h1.t_step   = h2.t_step
-            '''.format(condition=condition)
-            last_df = pd.read_sql_query(last_query, self.db)
-
-            filters.append("t_step >= '{}' and t_step <= '{}'".format(min_step, max(t_steps)))
-
-        condition = ''
-        if filters:
-            condition = 'where {} '.format(' and '.join(filters))
-        query = 'select * from history {} limit {}'.format(condition, limit)
-        df = pd.read_sql_query(query, self.db)
-        if last_df is not None:
-            df = pd.concat([df, last_df])
-
-        df_p = df.pivot_table(values='value', index=['t_step'],
-                              columns=['key', 'agent_id'],
-                              aggfunc='first')
-
-        for k, v in self._dtypes.items():
-            if k in df_p:
-                dtype, _, deserial = v
-                df_p[k] = df_p[k].fillna(method='ffill').astype(dtype)
-        if t_steps:
-            df_p = df_p.reindex(t_steps, method='ffill')
-        return df_p.ffill()
-
-    def __getstate__(self):
-        state = dict(**self.__dict__)
-        del state['_db']
-        del state['_dtypes']
-        return state
-
-    def __setstate__(self, state):
-        self.__dict__ = state
-        self._dtypes = {}
-        self._db = None
-
-    def dump(self, f):
-        self._close()
-        for line in open_or_reuse(self.db_path, 'rb'):
-            f.write(line)
-
-
-class Records():
-
-    def __init__(self, df, filter=None, dtypes=None):
-        if not filter:
-            filter = Key(agent_id=None,
-                         t_step=None,
-                         key=None)
-        self._df = df
-        self._filter = filter
-        self.dtypes = dtypes or {}
-        super().__init__()
-
-    def mask(self, tup):
-        res = ()
-        for i, k in zip(tup[:-1], self._filter):
-            if k is None:
-                res = res + (i,)
-        res = res + (tup[-1],)
-        return res
-
-    def filter(self, newKey):
-        f = list(self._filter)
-        for ix, i in enumerate(f):
-            if i is None:
-                f[ix] = newKey
-        self._filter = Key(*f)
-
-    @property
-    def resolved(self):
-        return sum(1 for i in self._filter if i is not None) == 3
-
-    def __iter__(self):
-        for column, series in self._df.iteritems():
-            key, agent_id = column
-            for t_step, value in series.iteritems():
-                r = Record(t_step=t_step,
-                           agent_id=agent_id,
-                           key=key,
-                           value=value)
-                yield self.mask(r)
-
-    def value(self):
-        if self.resolved:
-            f = self._filter
-            try:
-                i = self._df[f.key][str(f.agent_id)]
-                ix = i.index.get_loc(f.t_step, method='ffill')
-                return i.iloc[ix]
-            except KeyError as ex:
-                return self.dtypes[f.key][2]()
-        return list(self)
-
-    def df(self):
-        return self._df
-
-    def __getitem__(self, k):
-        n = copy.copy(self)
-        n.filter(k)
-        if n.resolved:
-            return n.value()
-        return n
-
-    def __len__(self):
-        return len(self._df)
-
-    def __str__(self):
-        if self.resolved:
-            return str(self.value())
-        return '<Records for [{}]>'.format(self._filter)
-
-Key = namedtuple('Key', ['agent_id', 't_step', 'key'])
-Record = namedtuple('Record', 'agent_id t_step key value')

soil/network.py

@@ -0,0 +1,84 @@
+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.
+    """
+    # TODO: test
+    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

@@ -2,55 +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.setLevel(logging.INFO)
-
-
-def load_network(network_params, dir_path=None):
-    if network_params is None:
-        return nx.Graph()
-    path = network_params.get('path', None)
-    if path:
-        if dir_path and not os.path.isabs(path):
-            path = os.path.join(dir_path, path)
-        extension = os.path.splitext(path)[1][1:]
-        kwargs = {}
-        if extension == 'gexf':
-            kwargs['version'] = '1.2draft'
-            kwargs['node_type'] = int
-        try:
-            method = getattr(nx.readwrite, 'read_' + extension)
-        except AttributeError:
-            raise AttributeError('Unknown format')
-        return method(path, **kwargs)
-
-    net_args = network_params.copy()
-    if 'generator' not in net_args:
-        return nx.Graph()
-
-    net_gen = net_args.pop('generator')
-
-    if dir_path not in sys.path:
-        sys.path.append(dir_path)
-
-    method = deserializer(net_gen,
-                          known_modules=['networkx.generators',])
-
-    return method(**net_args)
+logger = logging.getLogger("soil")
 
 
 def load_file(infile):
-    with open(infile, 'r') as f:
+    folder = os.path.dirname(infile)
+    if folder not in sys.path:
+        sys.path.append(folder)
+    with open(infile, "r") as f:
         return list(chain.from_iterable(map(expand_template, load_string(f))))
 
 
@@ -59,81 +31,95 @@ 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:
-        raise ValueError(('You must provide a definition of variables'
-                          ' for the template.'))
+    if "vars" not in config:
+        raise ValueError(
+            ("You must provide a definition of variables" " for the template.")
+        )
 
-    template = Template(config['template'])
+    template = config["template"]
 
-    sampler_name = config.get('sampler', 'SALib.sample.morris.sample')
-    n_samples = int(config.get('samples', 100))
-    sampler = deserializer(sampler_name)
-    bounds = config['vars']['bounds']
+    if not isinstance(template, str):
+        template = yaml.dump(template)
 
-    problem = {
-        'num_vars': len(bounds),
-        'names': list(bounds.keys()),
-        'bounds': list(v for v in bounds.values())
-    }
-    samples = sampler(problem, n_samples)
+    template = Template(template)
 
-    lists = config['vars'].get('lists', {})
-    names = list(lists.keys())
-    values = list(lists.values())
-    combs = list(product(*values))
+    params = params_for_template(config)
 
-    allnames = names + problem['names']
-    allvalues = [(list(i[0])+list(i[1])) for i in product(combs, samples)]
-    params = list(map(lambda x: dict(zip(allnames, x)), allvalues))
-
-
-    blank_str = template.render({k: 0 for k in allnames})
+    blank_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')
-    if 'name' in blank[0]:
-        raise ValueError('Templates cannot be named, use group instead')
+        raise ValueError("Templates must not return more than one configuration")
+    if "name" in blank[0]:
+        raise ValueError("Templates cannot be named, use group instead")
 
-    confs = []
     for ps in params:
         string = template.render(ps)
         for c in load_string(string):
             yield c
 
 
+def params_for_template(config):
+    sampler_config = config.get("sampler", {"N": 100})
+    sampler = sampler_config.pop("method", "SALib.sample.morris.sample")
+    sampler = deserializer(sampler)
+    bounds = config["vars"]["bounds"]
+
+    problem = {
+        "num_vars": len(bounds),
+        "names": list(bounds.keys()),
+        "bounds": list(v for v in bounds.values()),
+    }
+    samples = sampler(problem, **sampler_config)
+
+    lists = config["vars"].get("lists", {})
+    names = list(lists.keys())
+    values = list(lists.values())
+    combs = list(product(*values))
+
+    allnames = names + problem["names"]
+    allvalues = [(list(i[0]) + list(i[1])) for i in product(combs, samples)]
+    params = list(map(lambda x: dict(zip(allnames, x)), allvalues))
+    return params
+
+
 def load_files(*patterns, **kwargs):
     for pattern in patterns:
-        for i in glob(pattern, **kwargs):
-            for config in load_file(i):
+        for i in glob(pattern, **kwargs, recursive=True):
+            for cfg in load_file(i):
                 path = os.path.abspath(i)
-                if 'dir_path' not in config:
-                    config['dir_path'] = os.path.dirname(path)
-                yield config, path
+                yield Config.from_raw(cfg), path
 
 
-def load_config(config):
-    if isinstance(config, dict):
-        yield config, None
+def load_config(cfg):
+    if isinstance(cfg, Config):
+        yield cfg, os.getcwd()
+    elif isinstance(cfg, dict):
+        yield Config.from_raw(cfg), os.getcwd()
     else:
-        yield from load_files(config)
+        yield from load_files(cfg)
 
 
-builtins = importlib.import_module('builtins')
+builtins = importlib.import_module("builtins")
 
-def name(value, known_modules=[]):
-    '''Return a name that can be imported, to serialize/deserialize an object'''
+KNOWN_MODULES = [
+    "soil",
+]
+
+
+def name(value, known_modules=KNOWN_MODULES):
+    """Return a name that can be imported, to serialize/deserialize an object"""
     if value is None:
-        return 'None'
+        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
@@ -143,59 +129,102 @@ 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=[]):
-    '''Get a text representation of an object.'''
+def serialize(v, known_modules=KNOWN_MODULES):
+    """Get a text representation of an object."""
     tname = name(v, known_modules=known_modules)
     func = serializer(tname)
     return func(v), tname
 
-def deserializer(type_, known_modules=[]):
+
+def serialize_dict(d, known_modules=KNOWN_MODULES):
+    d = dict(d)
+    for (k, v) in d.items():
+        if isinstance(v, dict):
+            d[k] = serialize_dict(v, known_modules=known_modules)
+        elif isinstance(v, list):
+            for ix in range(len(v)):
+                v[ix] = serialize_dict(v[ix], known_modules=known_modules)
+        elif isinstance(v, type):
+            d[k] = serialize(v, known_modules=known_modules)[1]
+    return d
+
+
+IS_CLASS = re.compile(r"<class '(.*)'>")
+
+
+def deserializer(type_, known_modules=KNOWN_MODULES):
     if type(type_) != str:  # Already deserialized
         return type_
-    if type_ == 'str':
-        return lambda x='': x
-    if type_ == 'None':
+    if type_ == "str":
+        return lambda x="": x
+    if type_ == "None":
         return lambda x=None: None
     if hasattr(builtins, type_):  # Check if it's a builtin type
         cls = getattr(builtins, type_)
         return lambda x=None: ast.literal_eval(x) if x is not None else cls()
+    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):
-    '''Get an object from a text representation'''
+def deserialize(type_, value=None, globs=None, **kwargs):
+    """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=KNOWN_MODULES, **kwargs):
+    """Return the list of deserialized objects"""
+    # TODO: remove
+    print("SERIALIZATION", kwargs)
+    objects = []
+    for name in names:
+        mod = deserialize(name, known_modules=known_modules)
+        objects.append(mod(*args, **kwargs))
+    return objects

soil/simulation.py

@@ -1,307 +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 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 nxsim import NetworkSimulation
-
-from . import serialization, utils, basestring, agents
+from . import serialization, exporters, utils, basestring, agents
 from .environment import Environment
-from .utils import logger
-from .exporters import for_sim as exporters_for_sim
+from .utils import logger, run_and_return_exceptions
+from .config import Config, convert_old
 
 
-class Simulation(NetworkSimulation):
+# TODO: change documentation for simulation
+@dataclass
+class Simulation:
     """
-    Subclass of 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,
-                 network_agents=None, agent_type=None, states=None,
-                 default_state=None, interval=1, num_trials=1,
-                 max_time=100, load_module=None, seed=None,
-                 dir_path=None, environment_agents=None,
-                 environment_params=None, environment_class=None,
-                 **kwargs):
+    version: str = "2"
+    name: str = "Unnamed simulation"
+    description: Optional[str] = ""
+    group: str = None
+    model_class: Union[str, type] = "soil.Environment"
+    model_params: dict = field(default_factory=dict)
+    seed: str = field(default_factory=lambda: current_time())
+    dir_path: str = field(default_factory=lambda: os.getcwd())
+    max_time: float = float("inf")
+    max_steps: int = -1
+    interval: int = 1
+    num_trials: int = 3
+    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.seed = str(seed) or str(time.time())
-        self.load_module = load_module
-        self.network_params = network_params
-        self.name = name or 'Unnamed_' + time.strftime("%Y-%m-%d_%H.%M.%S")
-        self.group = group or None
-        self.num_trials = num_trials
-        self.max_time = max_time
-        self.default_state = default_state or {}
-        self.dir_path = dir_path or os.getcwd()
-        self.interval = interval
+    @classmethod
+    def from_dict(cls, env, **kwargs):
 
-        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:
-            topology = serialization.load_network(network_params,
-                                                  dir_path=self.dir_path)
-        elif isinstance(topology, basestring) or isinstance(topology, dict):
-            topology = json_graph.node_link_graph(topology)
-        self.topology = nx.Graph(topology)
+        d = {k: v for k, v in env.items() if k not in ignored}
+        if ignored:
+            d.setdefault("extra", {}).update(ignored)
+        if ignored:
+            print(f'Warning: Ignoring these parameters (added to "extra"): { ignored }')
+        d.update(kwargs)
 
-
-        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)
+        return cls(**d)
 
     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'''
-        return list(self._run_simulation_gen(*args, **kwargs))
+        """Run the simulation and return the list of resulting environments"""
+        logger.info(
+            dedent(
+                """
+        Simulation:
+        ---
+        """
+            )
+            + self.to_yaml()
+        )
+        return list(self.run_gen(*args, **kwargs))
 
-    def _run_sync_or_async(self, parallel=False, *args, **kwargs):
-        if parallel:
-            p = Pool()
-            func = partial(self.run_trial_exceptions,
-                           *args,
-                           **kwargs)
-            for i in p.imap_unordered(func, range(self.num_trials)):
-                if isinstance(i, Exception):
-                    logger.error('Trial failed:\n\t%s', i.message)
-                    continue
-                yield i
-        else:
-            for i in range(self.num_trials):
-                yield self.run_trial(i,
-                                     *args,
-                                     **kwargs)
+    def run_gen(
+        self,
+        parallel=False,
+        dry_run=None,
+        exporters=None,
+        outdir=None,
+        exporter_params={},
+        log_level=None,
+        **kwargs,
+    ):
+        """Run the simulation and yield the resulting environments."""
+        if log_level:
+            logger.setLevel(log_level)
+        outdir = outdir or self.outdir
+        logger.info("Using exporters: %s", exporters or [])
+        logger.info("Output directory: %s", outdir)
+        if dry_run is None:
+            dry_run = self.dry_run
+        if exporters is None:
+            exporters = self.exporters
+        if not exporter_params:
+            exporter_params = self.exporter_params
 
-    def _run_simulation_gen(self, *args, parallel=False, dry_run=False,
-                            exporters=['default', ], outdir=None, exporter_params={}, **kwargs):
-        logger.info('Using exporters: %s', exporters or [])
-        logger.info('Output directory: %s', outdir)
-        exporters = exporters_for_sim(self,
-                                      exporters,
-                                      dry_run=dry_run,
-                                      outdir=outdir,
-                                      **exporter_params)
+        exporters = serialization.deserialize_all(
+            exporters,
+            simulation=self,
+            known_modules=[
+                "soil.exporters",
+            ],
+            dry_run=dry_run,
+            outdir=outdir,
+            **exporter_params,
+        )
 
-        with utils.timer('simulation {}'.format(self.name)):
+        with utils.timer("simulation {}".format(self.name)):
             for exporter in exporters:
-                exporter.start()
+                exporter.sim_start()
+
+            for env in utils.run_parallel(
+                func=self.run_trial,
+                iterable=range(int(self.num_trials)),
+                parallel=parallel,
+                log_level=log_level,
+                **kwargs,
+            ):
+
+                for exporter in exporters:
+                    exporter.trial_start(env)
 
-            for env in self._run_sync_or_async(*args, parallel=parallel,
-                                               **kwargs):
                 for exporter in exporters:
                     exporter.trial_end(env)
+
                 yield env
 
             for exporter in exporters:
-                exporter.end()
+                exporter.sim_end()
 
-    def get_env(self, trial_id = 0, **kwargs):
-        '''Create an environment for a trial of the simulation'''
-        opts = self.environment_params.copy()
-        env_name = '{}_trial_{}'.format(self.name, trial_id)
-        opts.update({
-            'name': env_name,
-            'topology': self.topology.copy(),
-            'seed': self.seed+env_name,
-            'initial_time': 0,
-            'interval': self.interval,
-            'network_agents': self.network_agents,
-            'states': self.states,
-            'default_state': self.default_state,
-            'environment_agents': self.environment_agents,
-        })
-        opts.update(kwargs)
-        env = self.environment_class(**opts)
-        return env
+    def get_env(self, trial_id=0, model_params=None, **kwargs):
+        """Create an environment for a trial of the simulation"""
 
-    def run_trial(self, trial_id=0, until=None, **opts):
-        """Run a single trial of the simulation
+        def deserialize_reporters(reporters):
+            for (k, v) in reporters.items():
+                if isinstance(v, str) and v.startswith("py:"):
+                    reporters[k] = serialization.deserialize(v.split(":", 1)[1])
+            return reporters
 
-        Parameters
-        ----------
-        trial_id : int
+        params = self.model_params.copy()
+        if model_params:
+            params.update(model_params)
+        params.update(kwargs)
+
+        agent_reporters = deserialize_reporters(params.pop("agent_reporters", {}))
+        model_reporters = deserialize_reporters(params.pop("model_reporters", {}))
+
+        env = serialization.deserialize(self.model_class)
+        return env(
+            id=f"{self.name}_trial_{trial_id}",
+            seed=f"{self.seed}_trial_{trial_id}",
+            dir_path=self.dir_path,
+            agent_reporters=agent_reporters,
+            model_reporters=model_reporters,
+            **params,
+        )
+
+    def run_trial(
+        self, trial_id=None, until=None, log_file=False, log_level=logging.INFO, **opts
+    ):
         """
+        Run a single trial of the simulation
+
+        """
+        if log_level:
+            logger.setLevel(log_level)
+        model = self.get_env(trial_id, **opts)
+        trial_id = trial_id if trial_id is not None else current_time()
+        with utils.timer("Simulation {} trial {}".format(self.name, trial_id)):
+            return self.run_model(
+                model=model, trial_id=trial_id, until=until, log_level=log_level
+            )
+
+    def run_model(self, model, until=None, **opts):
         # Set-up trial environment and graph
-        until = until or self.max_time
-        env = self.get_env(trial_id = trial_id, **opts)
+        until = float(until or self.max_time or "inf")
+
         # Set up agents on nodes
-        with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)):
-            env.run(until)
-        return env
-    def run_trial_exceptions(self, *args, **kwargs):
-        '''
-        A wrapper for run_trial that catches exceptions and returns them.
-        It is meant for async simulations
-        '''
-        try:
-            return self.run_trial(*args, **kwargs)
-        except Exception as ex:
-            if ex.__cause__ is not None:
-                ex = ex.__cause__
-            ex.message = ''.join(traceback.format_exception(type(ex), ex, ex.__traceback__)[:])
-            return ex
+        def is_done():
+            return not model.running
+
+        if until and hasattr(model.schedule, "time"):
+            prev = is_done
+
+            def is_done():
+                return prev() or model.schedule.time >= until
+
+        if self.max_steps and self.max_steps > 0 and hasattr(model.schedule, "steps"):
+            prev_steps = is_done
+
+            def is_done():
+                return prev_steps() or model.schedule.steps >= self.max_steps
+
+        newline = "\n"
+        logger.info(
+            dedent(
+                f"""
+Model stats:
+  Agents (total: { model.schedule.get_agent_count() }):
+      - { (newline + '      - ').join(str(a) for a in model.schedule.agents) }
+
+  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):
-        if not f and not outdir:
-            raise ValueError('specify a file or an output directory')
-
-        if not f:
-            f = os.path.join(outdir, '{}.dumped.yml'.format(self.name))
-
-        with utils.open_or_reuse(f, 'w') as f:
-            f.write(self.to_yaml())
-
-    def dump_pickle(self, f=None, outdir=None):
-        if not outdir and not f:
-            raise ValueError('specify a file or an output directory')
-
-        if not f:
-            f = os.path.join(outdir,
-                             '{}.simulation.pickle'.format(self.name))
-        with utils.open_or_reuse(f, 'wb') as f:
-            pickle.dump(self, f)
-
-    def __getstate__(self):
-        state={}
-        for k, v in self.__dict__.items():
-            if k[0] != '_':
-                state[k] = v
-                state['topology'] = json_graph.node_link_data(self.topology)
-                state['network_agents'] = agents.serialize_distribution(self.network_agents,
-                                                                        known_modules = [])
-                state['environment_agents'] = agents.serialize_distribution(self.environment_agents,
-                                                                            known_modules = [])
-                state['environment_class'] = 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
-        return state
-
-
-def all_from_config(config):
-    configs = list(serialization.load_config(config))
-    for config, _ in configs:
-        sim = Simulation(**config)
-        yield sim
+def iter_from_config(*cfgs, **kwargs):
+    for config in cfgs:
+        configs = list(serialization.load_config(config))
+        for config, path in configs:
+            d = dict(config)
+            if "dir_path" not in d:
+                d["dir_path"] = os.path.dirname(path)
+            yield Simulation.from_dict(d, **kwargs)
 
 
 def from_config(conf_or_path):
-    config = list(serialization.load_config(conf_or_path))
-    if len(config) > 1:
-        raise AttributeError('Provide only one configuration')
-    config = config[0][0]
-    sim = Simulation(**config)
-    return sim
+    lst = list(iter_from_config(conf_or_path))
+    if len(lst) > 1:
+        raise AttributeError("Provide only one configuration")
+    return lst[0]
 
 
 def run_from_config(*configs, **kwargs):
-    for config_def in configs:
-        # logger.info("Found {} config(s)".format(len(ls)))
-        for config, path in serialization.load_config(config_def):
-            name = config.get('name', 'unnamed')
-            logger.info("Using config(s): {name}".format(name=name))
-
-            dir_path = config.pop('dir_path', os.path.dirname(path))
-            sim = Simulation(dir_path=dir_path,
-                             **config)
-            sim.run_simulation(**kwargs)
+    for sim in iter_from_config(*configs):
+        logger.info(f"Using config(s): {sim.name}")
+        sim.run_simulation(**kwargs)

soil/time.py

@@ -0,0 +1,201 @@
+from mesa.time import BaseScheduler
+from queue import Empty
+from heapq import heappush, heappop, heapify
+import math
+
+from inspect import getsource
+from numbers import Number
+
+from .utils import logger
+from mesa import Agent as MesaAgent
+
+
+INFINITY = float("inf")
+
+
+class DeadAgent(Exception):
+    pass
+
+
+class When:
+    def __init__(self, time):
+        if isinstance(time, When):
+            return time
+        self._time = time
+
+    def next(self, time):
+        return self._time
+
+    def abs(self, time):
+        return self
+
+    def __repr__(self):
+        return str(f"When({self._time})")
+
+    def __lt__(self, other):
+        if isinstance(other, Number):
+            return self._time < other
+        return self._time < other.next(self._time)
+
+    def __gt__(self, other):
+        if isinstance(other, Number):
+            return self._time > other
+        return self._time > other.next(self._time)
+
+    def ready(self, agent):
+        return self._time <= agent.model.schedule.time
+
+
+class Cond(When):
+    def __init__(self, func, delta=1):
+        self._func = func
+        self._delta = delta
+        self._checked = False
+
+    def next(self, time):
+        if self._checked:
+            return time + self._delta
+        return time
+
+    def abs(self, time):
+        return self
+
+    def ready(self, agent):
+        self._checked = True
+        return self._func(agent)
+
+    def __eq__(self, other):
+        return False
+
+    def __lt__(self, other):
+        return True
+
+    def __gt__(self, other):
+        return False
+
+    def __repr__(self):
+        return str(f'Cond("{getsource(self._func)}")')
+
+
+NEVER = When(INFINITY)
+
+
+class Delta(When):
+    def __init__(self, delta):
+        self._delta = delta
+
+    def __eq__(self, other):
+        if isinstance(other, Delta):
+            return self._delta == other._delta
+        return False
+
+    def abs(self, time):
+        return When(self._delta + time)
+
+    def next(self, time):
+        return time + self._delta
+
+    def __repr__(self):
+        return str(f"Delta({self._delta})")
+
+
+class TimedActivation(BaseScheduler):
+    """A scheduler which activates each agent when the agent requests.
+    In each activation, each agent will update its 'next_time'.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._next = {}
+        self._queue = []
+        self.next_time = 0
+        self.logger = logger.getChild(f"time_{ self.model }")
+
+    def add(self, agent: MesaAgent, when=None):
+        if when is None:
+            when = When(self.time)
+        elif not isinstance(when, When):
+            when = When(when)
+        if agent.unique_id in self._agents:
+            del self._agents[agent.unique_id]
+            if agent.unique_id in self._next:
+                self._queue.remove((self._next[agent.unique_id], agent))
+                heapify(self._queue)
+
+        self._next[agent.unique_id] = when
+        heappush(self._queue, (when, agent))
+        super().add(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.
+        """
+
+        self.logger.debug(f"Simulation step {self.time}")
+        if not self.model.running:
+            return
+
+        when = NEVER
+
+        to_process = []
+        skipped = []
+        next_time = INFINITY
+
+        ix = 0
+
+        while self._queue:
+            (when, agent) = self._queue[0]
+            if when > self.time:
+                break
+            heappop(self._queue)
+            if when.ready(agent):
+                to_process.append(agent)
+                self._next.pop(agent.unique_id, None)
+                continue
+
+            next_time = min(next_time, when.next(self.time))
+            self._next[agent.unique_id] = next_time
+            skipped.append((when, agent))
+
+        if self._queue:
+            next_time = min(next_time, self._queue[0][0].next(self.time))
+
+        self._queue = [*skipped, *self._queue]
+
+        for agent in to_process:
+            self.logger.debug(f"Stepping agent {agent}")
+
+            try:
+                returned = ((agent.step() or Delta(1))).abs(self.time)
+            except DeadAgent:
+                if agent.unique_id in self._next:
+                    del self._next[agent.unique_id]
+                agent.alive = False
+                continue
+
+            if not getattr(agent, "alive", True):
+                self.remove(agent)
+                continue
+
+            value = returned.next(self.time)
+
+            if value < self.time:
+                raise Exception(
+                    f"Cannot schedule an agent for a time in the past ({when} < {self.time})"
+                )
+            if value < INFINITY:
+                next_time = min(value, next_time)
+
+                self._next[agent.unique_id] = returned
+                heappush(self._queue, (returned, agent))
+            else:
+                assert not self._next[agent.unique_id]
+
+        self.steps += 1
+        self.logger.debug(f"Updating time step: {self.time} -> {next_time}")
+        self.time = next_time
+
+        if not self._queue or next_time == INFINITY:
+            self.model.running = False
+            return self.time

soil/utils.py

@@ -1,50 +1,154 @@
 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")
 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):
-    start = time.time()
-    function('{}Starting {} at {}.'.format(pre, name,
-                                           time.strftime("%X", time.gmtime(start))))
+def timer(name="task", pre="", function=logger.info, to_object=None):
+    start = current_time()
+    function("{}Starting {} at {}.".format(pre, name, strftime("%X", gmtime(start))))
     yield start
-    end = time.time()
-    function('{}Finished {} at {} in {} seconds'.format(pre, name,
-                                                        time.strftime("%X", time.gmtime(end)),
-                                                        str(end-start)))
+    end = current_time()
+    function(
+        "{}Finished {} at {} in {} seconds".format(
+            pre, name, strftime("%X", gmtime(end)), str(end - start)
+        )
+    )
     if to_object:
         to_object.start = start
         to_object.end = end
 
 
-def safe_open(path, mode='r', backup=True, **kwargs):
+def try_backup(path, remove=False):
+    if not os.path.exists(path):
+        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)
-        stamp = time.strftime('%Y-%m-%d_%H.%M', time.localtime(creation))
+    creation = os.path.getctime(path)
+    stamp = strftime("%Y-%m-%d_%H.%M.%S", localtime(creation))
 
-        backup_dir = os.path.join(outdir, stamp)
-        if not os.path.exists(backup_dir):
-            os.makedirs(backup_dir)
-        newpath = os.path.join(backup_dir, os.path.basename(path))
-        if os.path.exists(newpath):
-            newpath = '{}@{}'.format(newpath, time.time())
+    backup_dir = os.path.join(outdir, "backup")
+    if not os.path.exists(backup_dir):
+        os.makedirs(backup_dir)
+    newpath = os.path.join(backup_dir, "{}@{}".format(os.path.basename(path), stamp))
+    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)
-    except (AttributeError, TypeError):
-        return f
+        with safe_open(f, *args, **kwargs) as f:
+            yield 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=""):
+    if not isinstance(d, dict):
+        # print('END:', prefix, d)
+        yield prefix, d
+        return
+    if prefix:
+        prefix = prefix + "."
+    for k, v in d.items():
+        # print(k, v)
+        res = list(_flatten_dict(v, prefix="{}{}".format(prefix, k)))
+        # print('RES:', res)
+        yield from res
+
+
+def unflatten_dict(d):
+    out = {}
+    for k, v in d.items():
+        target = out
+        if not isinstance(k, str):
+            target[k] = v
+            continue
+        tokens = k.split(".")
+        if len(tokens) < 2:
+            target[k] = v
+            continue
+        for token in tokens[:-1]:
+            if token not in target:
+                target[token] = {}
+            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 {}.'
-                      'Be careful!.').format(__name__))
-        return '0.0'
+        logger.error(
+            ("Running an unknown version of {}." "Be careful!.").format(__name__)
+        )
+        return "0.0"
 
 
 __version__ = read_version()

soil/visualization.py

@@ -0,0 +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,
-                    name=self.application.name)
+        self.render(
+            "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',
-                    'error': error})
+                self.write_message({"type": "error", "error": error})
                 return
 
             self.config = self.config[0]
-            self.send_log('INFO.' + self.simulation_name,
-                          'Using config: {name}'.format(name=self.config['name']))
+            self.send_log(
+                "INFO." + self.simulation_name,
+                "Using config: {name}".format(name=self.config["name"]),
+            )
 
-            if 'visualization_params' in self.config:
-                self.write_message({'type': 'visualization_params',
-                    'data': self.config['visualization_params']})
-            self.name = self.config['name']
+            if "visualization_params" in self.config:
+                self.write_message(
+                    {
+                        "type": "visualization_params",
+                        "data": self.config["visualization_params"],
+                    }
+                )
+            self.name = self.config["name"]
             self.run_simulation()
 
             settings = []
-            for key in self.config['environment_params']: 
-                if type(self.config['environment_params'][key]) == float or type(self.config['environment_params'][key]) == int:
-                    if self.config['environment_params'][key] <= 1:
-                        setting_type = 'number'
+            for key in self.config["environment_params"]:
+                if (
+                    type(self.config["environment_params"][key]) == float
+                    or type(self.config["environment_params"][key]) == int
+                ):
+                    if self.config["environment_params"][key] <= 1:
+                        setting_type = "number"
                     else:
-                        setting_type = 'great_number'
-                elif type(self.config['environment_params'][key]) == bool:
-                    setting_type = 'boolean'
+                        setting_type = "great_number"
+                elif type(self.config["environment_params"][key]) == bool:
+                    setting_type = "boolean"
                 else:
-                    setting_type = 'undefined'
+                    setting_type = "undefined"
 
-                settings.append({
-                    'label': key,
-                    'type': setting_type,
-                    'value': self.config['environment_params'][key]
-                })
+                settings.append(
+                    {
+                        "label": key,
+                        "type": setting_type,
+                        "value": self.config["environment_params"][key],
+                    }
+                )
 
-            self.write_message({'type': 'settings',
-                'data': settings})
+            self.write_message({"type": "settings", "data": settings})
 
-        elif msg['type'] == 'get_trial':
+        elif msg["type"] == "get_trial":
             if self.application.verbose:
-                logger.info('Trial {} requested!'.format(msg['data']))
-            self.send_log('INFO.' + __name__, 'Trial {} requested!'.format(msg['data']))
-            self.write_message({'type': 'get_trial',
-                                'data': self.get_trial(int(msg['data']))})
+                logger.info("Trial {} requested!".format(msg["data"]))
+            self.send_log("INFO." + __name__, "Trial {} requested!".format(msg["data"]))
+            self.write_message(
+                {"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']))
-            self.send_log('INFO.' + self.simulation_name, 'Running new simulation for {name}'.format(name=self.config['name']))
-            self.config['environment_params'] = msg['data']
+                logger.info(
+                    "Running new simulation for {name}".format(name=self.config["name"])
+                )
+            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':
-            G = self.trials[ int(msg['data']) ].history_to_graph()
+        elif msg["type"] == "download_gexf":
+            G = self.trials[int(msg["data"])].history_to_graph()
             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'])
-            writer = nx.readwrite.gexf.GEXFWriter(version='1.2draft')
+                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"]
+            writer = nx.readwrite.gexf.GEXFWriter(version="1.2draft")
             writer.add_graph(G)
-            self.write_message({'type': 'download_gexf',
-                'filename': self.config['name'] + '_trial_' + str(msg['data']),
-                'data': tostring(writer.xml).decode(writer.encoding) })
+            self.write_message(
+                {
+                    "type": "download_gexf",
+                    "filename": self.config["name"] + "_trial_" + str(msg["data"]),
+                    "data": tostring(writer.xml).decode(writer.encoding),
+                }
+            )
 
-        elif msg['type'] == 'download_json':
-            G = self.trials[ int(msg['data']) ].history_to_graph()
+        elif msg["type"] == "download_json":
+            G = self.trials[int(msg["data"])].history_to_graph()
             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'])
-            self.write_message({'type': 'download_json',
-                'filename': self.config['name'] + '_trial_' + str(msg['data']),
-                'data': nx.node_link_data(G) })
+                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"]
+            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()):
-                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])
+            if (
+                not self.log_capture_string.closed
+                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',
-                            'logger': logger,
-                            'logging': logging})
+        self.write_message({"type": "log", "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!')
-        if 'visualization_params' in self.config:
-            del self.config['visualization_params']
+        logger.info("Running simulation!")
+        if "visualization_params" in self.config:
+            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['dump'] = self.application.dump
+                config["outdir"] = os.path.join(self.application.outdir, config["name"])
+                config["dump"] = self.application.dump
                 self.trials = yield self.nonblocking(config)
 
-                self.write_message({'type': 'trials',
-                    'data': list(trial.name for trial in self.trials) })
+                self.write_message(
+                    {
+                        "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',
-                                    'error': error})
-                self.send_log('ERROR.' + self.simulation_name, error)
+                self.write_message({"type": "error", "error": error})
+                self.send_log("ERROR." + self.simulation_name, error)
 
     def get_trial(self, trial):
-        logger.info('Available trials: %s ' % len(self.trials))
-        logger.info('Ask for : %s' % trial)
+        logger.info("Available trials: %s " % len(self.trials))
+        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)
-    socket_handler = (r'/ws', SocketHandler)
-    static_handler = (r'/(.*)', tornado.web.StaticFileHandler,
-                      {'path': os.path.join(ROOT, 'static')})
-    local_handler = (r'/local/(.*)', tornado.web.StaticFileHandler,
-                     {'path': ''})
+    page_handler = (r"/", PageHandler)
+    socket_handler = (r"/ws", SocketHandler)
+    static_handler = (
+        r"/(.*)",
+        tornado.web.StaticFileHandler,
+        {"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,
-                'template_path': ROOT + '/templates'}
+    settings = {"debug": True, "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)
-        print('Interface starting at {url}'.format(url=url))
+        url = "http://127.0.0.1:{PORT}".format(PORT=self.port)
+        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('--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')
+    parser.add_argument(
+        "--name", "-n", nargs=1, default="SOIL", help="name of the simulation"
+    )
+    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('--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')
+    parser.add_argument(
+        "--name", "-n", nargs=1, default="SOIL", help="name of the simulation"
+    )
+    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 +1,4 @@
-pytest
+pytest
+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 +1,47 @@
+from unittest import TestCase
+import pytest
+
+from soil import agents, environment
+from soil import time as stime
+
+
+class Dead(agents.FSM):
+    @agents.default_state
+    @agents.state
+    def only(self):
+        return self.die()
+
+
+class TestMain(TestCase):
+    def test_die_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().abs(0)
+        print(ret, "next")
+        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.
+        '''
+        class Gen(agents.BaseAgent):
+            def step(self):
+                a = 0
+                for i in range(5):
+                    yield a
+                    a += 1
+        e = environment.Environment()
+        g = Gen(model=e, unique_id=e.next_id())
+
+        for i in range(5):
+            t = g.step()
+            assert t == i

tests/test_analysis.py

@@ -1,89 +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['count'] += 1
-        return self.odd
-
-    @agents.state
-    def odd(self):
-        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', 'id')
-        assert res['SEED']['seedanalysis_trial_0'].iloc[0] == 1
-        assert res['SEED']['seedanalysis_trial_0'].iloc[-1] == 1
-        assert res['id']['odd'].iloc[0] == 2
-        assert res['id']['even'].iloc[0] == 0
-        assert res['id']['odd'].iloc[-1] == 1
-        assert res['id']['even'].iloc[-1] == 1
-
-    def test_value(self):
-        env = self.env
-        df = analysis.read_sql(env._history._db)
-        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[0] == 1
-
-        res_total = analysis.get_value(df)
-
-        res_total['SEED'].iloc[0] == 'seedanalysis_trial_0'

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 +2,52 @@ 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):
-            iterations = s.max_time * s.num_trials
-            if iterations > 1000:
-                s.max_time = 100
+        for s in simulation.iter_from_config(cfg):
+            iterations = s.max_steps * s.num_trials
+            if iterations < 0 or iterations > 1000:
+                s.max_steps = 100
                 s.num_trials = 1
-            if config.get('skip_test', False) and not FORCE_TESTS:
-                self.skipTest('Example ignored.')
+            assert isinstance(cfg, config.Config)
+            if getattr(cfg, "skip_test", False) and not FORCE_TESTS:
+                self.skipTest("Example ignored.")
             envs = s.run_simulation(dry_run=True)
             assert envs
             for env in envs:
                 assert env
                 try:
-                    n = config['network_params']['n']
+                    n = cfg.model_params["network_params"]["n"]
                     assert len(list(env.network_agents)) == n
-                    assert env.now > 2  # 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(
-            join(EXAMPLES, '*', '*.yml'),
-            join(EXAMPLES, '*.yml'),
+    for cfg, path in serialization.load_files(
+        join(EXAMPLES, "**", "*.yml"),
     ):
-        p = make_example_test(path=path, config=config)
+        p = make_example_test(path=path, cfg=config.Config.from_raw(cfg))
         fname = os.path.basename(path)
-        p.__name__ = 'test_example_file_%s' % fname
-        p.__doc__ = '%s should be a valid configuration' % fname
+        p.__name__ = "test_example_file_%s" % fname
+        p.__doc__ = "%s should be a valid configuration" % fname
         setattr(TestExamples, p.__name__, p)
         del p
 

tests/test_exporters.py

@@ -2,12 +2,12 @@ import os
 import io
 import tempfile
 import shutil
-from time import time
+import sqlite3
 
 from unittest import TestCase
 from soil import exporters
-from soil.utils import safe_open
 from soil import simulation
+from soil import agents
 
 
 class Dummy(exporters.Exporter):
@@ -15,95 +15,97 @@ class Dummy(exporters.Exporter):
     trials = 0
     ended = False
     total_time = 0
+    called_start = 0
+    called_trial = 0
+    called_end = 0
 
-    def start(self):
+    def sim_start(self):
+        self.__class__.called_start += 1
         self.__class__.started = True
 
     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):
+    def sim_end(self):
         self.__class__.ended = True
+        self.__class__.called_end += 1
 
 
 class Exporters(TestCase):
     def test_basic(self):
+        # We need to add at least one agent to make sure the scheduler
+        # ticks every step
+        num_trials = 5
+        max_time = 2
         config = {
-            'name': 'exporter_sim',
-            'network_params': {},
-            'agent_type': 'CounterModel',
-            'max_time': 2,
-            'num_trials': 5,
-            'environment_params': {}
+            "name": "exporter_sim",
+            "model_params": {"agents": [{"agent_class": agents.BaseAgent}]},
+            "max_time": max_time,
+            "num_trials": num_trials,
         }
         s = simulation.from_config(config)
-        s.run_simulation(exporters=[Dummy], dry_run=True)
+
+        for env in s.run_simulation(exporters=[Dummy], dry_run=True):
+            assert len(env.agents) == 1
+
         assert Dummy.started
         assert Dummy.ended
-        assert Dummy.trials == 5
-        assert Dummy.total_time == 2*5
-
-    def test_distribution(self):
-        '''The distribution exporter should write the number of agents in each state'''
-        config = {
-            'name': 'exporter_sim',
-            'network_params': {
-                'generator': 'complete_graph',
-                'n': 4
-            },
-            'agent_type': 'CounterModel',
-            'max_time': 2,
-            'num_trials': 5,
-            'environment_params': {}
-        }
-        output = io.StringIO()
-        s = simulation.from_config(config)
-        s.run_simulation(exporters=[exporters.distribution], dry_run=True, exporter_params={'copy_to': output})
-        result = output.getvalue()
-        assert 'count' in result
-        assert 'SEED,Noneexporter_sim_trial_3,1,,1,1,1,1' in result
+        assert Dummy.called_start == 1
+        assert Dummy.called_end == 1
+        assert Dummy.called_trial == num_trials
+        assert Dummy.trials == num_trials
+        assert Dummy.total_time == max_time * num_trials
 
     def test_writing(self):
-        '''Try to write CSV, GEXF, sqlite and YAML (without dry_run)'''
+        """Try to write CSV, sqlite and YAML (without dry_run)"""
         n_trials = 5
         config = {
-            'name': 'exporter_sim',
-            'network_params': {
-                'generator': 'complete_graph',
-                'n': 4
-            },
-            'agent_type': 'CounterModel',
-            'max_time': 2,
-            'num_trials': n_trials,
-            'environment_params': {}
+            "name": "exporter_sim",
+            "network_params": {"generator": "complete_graph", "n": 4},
+            "agent_class": "CounterModel",
+            "max_time": 2,
+            "num_trials": n_trials,
+            "dry_run": False,
+            "environment_params": {},
         }
         output = io.StringIO()
         s = simulation.from_config(config)
         tmpdir = tempfile.mkdtemp()
-        envs = s.run_simulation(exporters=[
-            exporters.default,
-            exporters.csv,
-            exporters.gexf,
-            exporters.distribution,
-        ],
-                                outdir=tmpdir,
-                                exporter_params={'copy_to': output})
+        envs = s.run_simulation(
+            exporters=[
+                exporters.default,
+                exporters.csv,
+            ],
+            model_params={
+                "agent_reporters": {"times": "times"},
+                "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)
-        with open(os.path.join(simdir, '{}.dumped.yml'.format(s.name))) as f:
+        simdir = os.path.join(tmpdir, s.group or "", s.name)
+        with open(os.path.join(simdir, "{}.dumped.yml".format(s.name))) as f:
             result = f.read()
             assert result
 
         try:
             for e in envs:
-                with open(os.path.join(simdir, '{}.gexf'.format(e.name))) as f:
-                    result = f.read()
-                    assert result
+                db = sqlite3.connect(os.path.join(simdir, f"{s.name}.sqlite"))
+                cur = db.cursor()
+                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_history.py

@@ -1,156 +0,0 @@
-from unittest import TestCase
-
-import os
-import shutil
-from glob import glob
-
-from soil import history
-
-
-ROOT = os.path.abspath(os.path.dirname(__file__))
-DBROOT = os.path.join(ROOT, 'testdb')
-
-
-class TestHistory(TestCase):
-
-    def setUp(self):
-        if not os.path.exists(DBROOT):
-            os.makedirs(DBROOT)
-
-    def tearDown(self):
-        if os.path.exists(DBROOT):
-            shutil.rmtree(DBROOT)
-
-    def test_history(self):
-        """
-        """
-        tuples = (
-            ('a_0', 0, 'id', 'h'),
-            ('a_0', 1, 'id', 'e'),
-            ('a_0', 2, 'id', 'l'),
-            ('a_0', 3, 'id', 'l'),
-            ('a_0', 4, 'id', 'o'),
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 3, 'prob', 2),
-            ('env', 5, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        h = history.History()
-        h.save_tuples(tuples)
-        # assert h['env', 0, 'prob'] == 0
-        for i in range(1, 7):
-            assert h['env', i, 'prob'] == ((i-1)//2)+1
-
-
-        for i, k in zip(range(5), 'hello'):
-            assert h['a_0', i, 'id'] == k
-        for record, value in zip(h['a_0', None, 'id'], 'hello'):
-            t_step, val = record
-            assert val == value
-
-        for i, k in zip(range(5), 'value'):
-            assert h['a_1', i, 'id'] == k
-        for i in range(5, 8):
-            assert h['a_1', i, 'id'] == 'e'
-        for i in range(7):
-            assert h['a_2', i, 'finished'] == False
-        assert h['a_2', 7, 'finished']
-
-    def test_history_gen(self):
-        """
-        """
-        tuples = (
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 2, 'prob', 2),
-            ('env', 3, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        h = history.History()
-        h.save_tuples(tuples)
-        for t_step, key, value in h['env', None, None]:
-            assert t_step == value
-            assert key == 'prob'
-
-        records = list(h[None, 7, None])
-        assert len(records) == 3
-        for i in records:
-            agent_id, key, value = i
-            if agent_id == 'a_1':
-                assert key == 'id'
-                assert value == 'e'
-            elif agent_id == 'a_2':
-                assert key == 'finished'
-                assert value
-            else:
-                assert key == 'prob'
-                assert value == 3
-
-        records = h['a_1', 7, None]
-        assert records['id'] == 'e'
-
-    def test_history_file(self):
-        """
-        History should be saved to a file
-        """
-        tuples = (
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 2, 'prob', 2),
-            ('env', 3, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        db_path = os.path.join(DBROOT, 'test')
-        h = history.History(db_path=db_path)
-        h.save_tuples(tuples)
-        h.flush_cache()
-        assert os.path.exists(db_path)
-
-        # Recover the data
-        recovered = history.History(db_path=db_path)
-        assert recovered['a_1', 0, 'id'] == 'v'
-        assert recovered['a_1', 4, 'id'] == 'e'
-
-        # Using backup=True should create a backup copy, and initialize an empty history
-        newhistory = history.History(db_path=db_path, backup=True)
-        backuppaths = glob(db_path + '.backup*.sqlite')
-        assert len(backuppaths) == 1
-        backuppath = backuppaths[0]
-        assert newhistory.db_path == h.db_path
-        assert os.path.exists(backuppath)
-        assert len(newhistory[None, None, None]) == 0
-
-    def test_history_tuples(self):
-        """
-        The data recovered should be equal to the one recorded.
-        """
-        tuples = (
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 2, 'prob', 2),
-            ('env', 3, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        h = history.History()
-        h.save_tuples(tuples)
-        recovered = list(h.to_tuples())
-        assert recovered
-        for i in recovered:
-            assert i in tuples

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,
-                  history, utils)
-
+from soil import simulation, Environment, agents, network, serialization, utils, config
+from soil.time import Delta
 
 ROOT = os.path.abspath(os.path.dirname(__file__))
-EXAMPLES = join(ROOT, '..', 'examples')
+EXAMPLES = join(ROOT, "..", "examples")
 
 
-class CustomAgent(agents.FSM):
+class CustomAgent(agents.FSM, agents.NetworkAgent):
     @agents.default_state
     @agents.state
     def normal(self):
-        self.state['neighbors'] = self.count_agents(state_id='normal',
-                                                    limit_neighbors=True)
+        self.neighbors = self.count_agents(state_id="normal", 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': {
-                'path': join(ROOT, 'test.gexf')
-            },
-            'agent_type': 'BaseAgent',
-            'environment_params': {
+            "model_params": {
+                "network_params": {"path": join(ROOT, "test.gexf")},
+                "agent_class": "BaseAgent",
             }
         }
         s = simulation.from_config(config)
         s.run_simulation(dry_run=True)
 
+    def test_network_agent(self):
+        """
+        The initial states should be applied to the agent and the
+        agent should be able to update its state."""
+        config = {
+            "name": "CounterAgent",
+            "num_trials": 1,
+            "max_time": 2,
+            "model_params": {
+                "network_params": {
+                    "generator": nx.complete_graph,
+                    "n": 2,
+                },
+                "agent_class": "CounterModel",
+                "states": {
+                    0: {"times": 10},
+                    1: {"times": 20},
+                },
+            },
+        }
+        s = simulation.from_config(config)
+
     def test_counter_agent(self):
         """
         The initial states should be applied to the agent and the
         agent should be able to update its state."""
         config = {
-            'name': 'CounterAgent',
-            'network_params': {
-                'path': join(ROOT, 'test.gexf')
+            "version": "2",
+            "name": "CounterAgent",
+            "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]
-        assert env.get_agent(0)['times', 0] == 11
-        assert env.get_agent(0)['times', 1] == 12
-        assert env.get_agent(1)['times', 0] == 21
-        assert env.get_agent(1)['times', 1] == 22
+        env = s.get_env()
+        assert isinstance(env.agents[0], agents.CounterModel)
+        assert env.agents[0].G == env.G
+        assert env.agents[0]["times"] == 10
+        assert env.agents[0]["times"] == 10
+        env.step()
+        assert env.agents[0]["times"] == 11
+        assert env.agents[1]["times"] == 21
 
-    def test_counter_agent_history(self):
-        """
-        The evolution of the state should be recorded in the logging agent
-        """
+    def test_init_and_count_agents(self):
+        """Agents should be properly initialized and counting should filter them properly"""
+        # TODO: separate this test into two or more test cases
         config = {
-            'name': 'CounterAgent',
-            'network_params': {
-                'path': join(ROOT, 'test.gexf')
+            "max_time": 10,
+            "model_params": {
+                "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': {'id': 0}
-
-            }],
-            'max_time': 10,
-            'environment_params': {
-            }
         }
         s = simulation.from_config(config)
         env = s.run_simulation(dry_run=True)[0]
-        for agent in env.network_agents:
-            last = 0
-            assert len(agent[None, None]) == 10
-            for step, total in sorted(agent['total', None]):
-                assert total == last + 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(0).state['neighbors'] == 1
-        assert env.get_agent(0).state['neighbors'] == 1
-        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.agents[0].weight == 1
+        assert env.count_agents() == 2
+        assert env.count_agents(weight=1) == 1
+        assert env.count_agents(weight=3) == 1
+        assert env.count_agents(agent_class=CustomAgent) == 2
 
     def test_torvalds_example(self):
         """A complete example from a documentation should work."""
-        config = serialization.load_file(join(EXAMPLES, 'torvalds.yml'))[0]
-        config['network_params']['path'] = join(EXAMPLES,
-                                                config['network_params']['path'])
+        config = serialization.load_file(join(EXAMPLES, "torvalds.yml"))[0]
+        config["model_params"]["network_params"]["path"] = join(
+            EXAMPLES, config["model_params"]["network_params"]["path"]
+        )
         s = simulation.from_config(config)
         env = s.run_simulation(dry_run=True)[0]
         for a in env.network_agents:
-            skill_level = a.state['skill_level']
-            if a.id == 'Torvalds':
-                assert skill_level == 'God'
-                assert a.state['total'] == 3
-                assert a.state['neighbors'] == 2
-            elif a.id == 'balkian':
-                assert skill_level == 'developer'
-                assert a.state['total'] == 3
-                assert a.state['neighbors'] == 1
+            skill_level = a.state["skill_level"]
+            if a.id == "Torvalds":
+                assert skill_level == "God"
+                assert a.state["total"] == 3
+                assert a.state["neighbors"] == 2
+            elif a.id == "balkian":
+                assert skill_level == "developer"
+                assert a.state["total"] == 3
+                assert a.state["neighbors"] == 1
             else:
-                assert skill_level == 'beginner'
-                assert a.state['total'] == 3
-                assert a.state['neighbors'] == 1
-
-    def test_yaml(self):
-        """
-        The YAML version of a newly created simulation
-        should be equivalent to the configuration file used
-        """
-        with utils.timer('loading'):
-            config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
-            s = simulation.from_config(config)
-        with utils.timer('serializing'):
-            serial = s.to_yaml()
-        with utils.timer('recovering'):
-            recovered = yaml.load(serial, Loader=yaml.SafeLoader)
-        with utils.timer('deleting'):
-            del recovered['topology']
-        assert config == recovered
-
-    def test_configuration_changes(self):
-        """
-        The configuration should not change after running
-         the simulation.
-        """
-        config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
-        s = simulation.from_config(config)
-        for i in range(5):
-            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._now = 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
+                assert skill_level == "beginner"
+                assert a.state["total"] == 3
+                assert a.state["neighbors"] == 1
 
     def test_serialize_class(self):
-        ser, name = serialization.serialize(agents.BaseAgent)
-        assert name == 'soil.agents.BaseAgent'
+        ser, name = serialization.serialize(agents.BaseAgent, known_modules=[])
+        assert name == "soil.agents.BaseAgent"
+        assert ser == agents.BaseAgent
+
+        ser, name = serialization.serialize(
+            agents.BaseAgent,
+            known_modules=[
+                "soil",
+            ],
+        )
+        assert name == "BaseAgent"
         assert ser == agents.BaseAgent
 
         ser, name = serialization.serialize(CustomAgent)
-        assert name == 'test_main.CustomAgent'
+        assert name == "test_main.CustomAgent"
         assert ser == CustomAgent
         pickle.dumps(ser)
 
@@ -262,85 +158,96 @@ class TestMain(TestCase):
             des = serialization.deserialize(name, ser)
             assert i == des
 
-    def test_serialize_agent_type(self):
-        '''A class from soil.agents should be serialized without the module part'''
+    def test_serialize_agent_class(self):
+        """A class from soil.agents should be serialized without the module part"""
         ser = agents.serialize_type(CustomAgent)
-        assert ser == 'test_main.CustomAgent'
+        assert ser == "test_main.CustomAgent"
         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
-            },
+            {"agent_class": "CounterModel", "weight": 1},
+            {"agent_class": "test_main.CustomAgent", "weight": 2},
         ]
-        converted = agents.deserialize_distribution(agent_distro)
-        assert converted[0]['agent_type'] == agents.CounterModel
-        assert converted[1]['agent_type'] == CustomAgent
+        converted = agents.deserialize_definition(agent_distro)
+        assert converted[0]["agent_class"] == agents.CounterModel
+        assert converted[1]["agent_class"] == CustomAgent
         pickle.dumps(converted)
 
     def test_serialize_agent_distribution(self):
         agent_distro = [
-            {
-                'agent_type': agents.CounterModel,
-                'weight': 1
-            },
-            {
-                'agent_type': CustomAgent,
-                'weight': 2
-            },
+            {"agent_class": agents.CounterModel, "weight": 1},
+            {"agent_class": CustomAgent, "weight": 2},
         ]
-        converted = agents.serialize_distribution(agent_distro)
-        assert converted[0]['agent_type'] == 'CounterModel'
-        assert converted[1]['agent_type'] == 'test_main.CustomAgent'
+        converted = agents.serialize_definition(agent_distro)
+        assert converted[0]["agent_class"] == "CounterModel"
+        assert converted[1]["agent_class"] == "test_main.CustomAgent"
         pickle.dumps(converted)
 
-    def test_pickle_agent_environment(self):
-        env = Environment(name='Test')
-        a = agents.BaseAgent(environment=env, agent_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_history(self):
-        '''Test storing in and retrieving from history (sqlite)'''
-        h = history.History()
-        h.save_record(agent_id=0, t_step=0, key="test", value="hello")
-        assert h[0, 0, "test"] == "hello"
-
-    def test_subgraph(self):
-        '''An agent should be able to subgraph the global topology'''
-        G = nx.Graph()
-        G.add_node(3)
-        G.add_edge(1, 2)
-        distro = agents.calculate_distribution(agent_type=agents.NetworkAgent)
-        env = Environment(name='Test', topology=G, network_agents=distro)
-        lst = list(env.network_agents)
-
-        a2 = env.get_agent(2)
-        a3 = env.get_agent(3)
-        assert len(a2.subgraph(limit_neighbors=True)) == 2
-        assert len(a3.subgraph(limit_neighbors=True)) == 1
-        assert len(a3.subgraph(limit_neighbors=True, center=False)) == 0
-        assert len(a3.subgraph(agent_type=agents.NetworkAgent)) == 3
-
     def test_templates(self):
-        '''Loading a template should result in several configs'''
-        configs = serialization.load_file(join(EXAMPLES, 'template.yml'))
+        """Loading a template should result in several configs"""
+        configs = serialization.load_file(join(EXAMPLES, "template.yml"))
         assert len(configs) > 0
 
+    def test_until(self):
+        config = {
+            "name": "until_sim",
+            "model_params": {
+                "network_params": {},
+                "agents": {
+                    "fixed": [
+                        {
+                            "agent_class": agents.BaseAgent,
+                        }
+                    ]
+                },
+            },
+            "max_time": 2,
+            "num_trials": 50,
+        }
+        s = simulation.from_config(config)
+        runs = list(s.run_simulation(dry_run=True))
+        over = list(x.now for x in runs if x.now > 2)
+        assert len(runs) == config["num_trials"]
+        assert len(over) == 0
 
+    def test_fsm(self):
+        """Basic state change"""
+
+        class ToggleAgent(agents.FSM):
+            @agents.default_state
+            @agents.state
+            def ping(self):
+                return self.pong
+
+            @agents.state
+            def pong(self):
+                return self.ping
+
+        a = ToggleAgent(unique_id=1, model=Environment())
+        assert a.state_id == a.ping.id
+        a.step()
+        assert a.state_id == a.pong.id
+        a.step()
+        assert a.state_id == a.ping.id
+
+    def test_fsm_when(self):
+        """Basic state change"""
+
+        class ToggleAgent(agents.FSM):
+            @agents.default_state
+            @agents.state
+            def ping(self):
+                return self.pong, 2
+
+            @agents.state
+            def pong(self):
+                return self.ping
+
+        a = ToggleAgent(unique_id=1, model=Environment())
+        when = a.step()
+        assert when == 2
+        when = a.step()
+        assert when == Delta(a.interval)

tests/test_mesa.py

@@ -0,0 +1,71 @@
+"""
+Mesa-SOIL integration tests
+
+We have to test that:
+- Mesa agents can be used in SOIL
+- Simplified soil agents can be used in mesa simulations
+- Mesa and soil agents can interact in a simulation
+
+- 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."""
+
+    def __init__(self, unique_id, model):
+        super().__init__(unique_id, model)
+        self.wealth = 1
+
+    def step(self):
+        self.move()
+        if self.wealth > 0:
+            self.give_money()
+
+    def give_money(self):
+        cellmates = self.model.grid.get_cell_list_contents([self.pos])
+        if len(cellmates) > 1:
+            other = self.random.choice(cellmates)
+            other.wealth += 1
+            self.wealth -= 1
+
+    def move(self):
+        possible_steps = self.model.grid.get_neighborhood(
+            self.pos, 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)
+        self.schedule = RandomActivation(self)
+
+        # Create agents
+        for i in range(self.num_agents):
+            a = MoneyAgent(i, self)
+            self.schedule.add(a)
+
+            # Add the agent to a random grid cell
+            x = self.random.randrange(self.grid.width)
+            y = self.random.randrange(self.grid.height)
+            self.grid.place_agent(a, (x, y))
+
+    def step(self):
+        """Advance the model by one step."""
+        self.schedule.step()
+
+
+# model = MoneyModel(10)
+# for i in range(10):
+#     model.step()
+
+# agent_wealth = [a.wealth for a in model.schedule.agents]

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].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].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_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 = []
+        done = 0
+
+        class CondAgent(agents.BaseAgent):
+
+            def step(self):
+                nonlocal done
+                times_started.append(self.now)
+                while True:
+                    yield time.Cond(lambda agent: agent.model.schedule.time >= 10)
+                    times_awakened.append(self.now)
+                    if self.now >= 10:
+                        break
+                done += 1
+
+        env = environment.Environment(agents=[{'agent_class': CondAgent}])
+
+
+        while env.schedule.time < 11:
+            env.step()
+            times.append(env.now)
+        assert env.schedule.time == 11
+        assert times_started == [0]
+        assert times_awakened == [10]
+        assert done == 1
+        # The first time will produce the Cond.
+        # Since there are no other agents, time will not advance, but the number
+        # of steps will.
+        assert env.schedule.steps == 12
+        assert len(times) == 12
+
+        while env.schedule.time < 12:
+            env.step()
+            times.append(env.now)
+
+        assert env.schedule.time == 12
+        assert times_started == [0, 11]
+        assert times_awakened == [10, 11]
+        assert done == 2
+        # Once more to yield the cond, another one to continue
+        assert env.schedule.steps == 14
+        assert len(times) == 14