Fix pre-release version of v1.0.0rc1

The examples weren't being properly tested in the last commit. When we fixed
that a lot of bugs in the new implementation of environment and agent were
found, which accounts for most of these changes.

The main difference is the mechanism to load simulations from a configuration
file. For that to work, we had to rework our module loading code in
`serialization` and add a `source_file` attribute to configurations (and
simulations, for that matter).

.gitlab-ci.yml

@@ -20,7 +20,7 @@ docker:
 test:
   tags:
     - docker
-  image: python:3.7
+  image: python:3.8
   stage: test
   script:
     - pip install -r requirements.txt -r test-requirements.txt
@@ -31,7 +31,7 @@ push_pypi:
     - tags
   tags:
     - docker
-  image: python:3.7
+  image: python:3.8
   stage: publish
   script:
     - echo $CI_COMMIT_TAG > soil/VERSION
@@ -44,7 +44,7 @@ check_pypi:
     - tags
   tags:
     - docker
-  image: python:3.7
+  image: python:3.8
   stage: check_published
   script:
     - pip install soil==$CI_COMMIT_TAG

CHANGELOG.md

@@ -3,7 +3,36 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [UNRELEASED]
+## [1.0 UNRELEASED]
+
+Version 1.0 introduced multiple changes, especially on the `Simulation` class and anything related to how configuration is handled.
+For an explanation of the general changes in version 1.0, please refer to the file `docs/notes_v1.0.rst`.
+
+### Added
+* 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).
+* Environments now have a class method to make them easier to use without a simulation`.run`. Notice that this is different from `run_model`, which is an instance method.
+* Ability to run simulations using mesa models
+* The `soil.exporters` module to export the results of datacollectors (`model.datacollector`) into files at the end of trials/simulations
+* Agents can now have generators as a step function or a state. They work similar to normal functions, with one caveat in the case of `FSM`: only `time` values (or None) 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.
+* Simulations can now specify a `matrix` with possible values for every simulation parameter. The final parameters will be calculated based on the `parameters` used and a cartesian product (i.e., all possible combinations) of each parameter.
+* 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>`
+### Changed
+* Configuration schema (`Simulation`) is very simplified. All simulations should be checked
+* Model / environment variables are expected (but not enforced) to be a single value. This is done to more closely align with mesa
+* `Exporter.iteration_end` now takes two parameters: `env` (same as before) and `params` (specific parameters for this environment). We considered including a `parameters` attribute in the environment, but this would not be compatible with mesa.
+* `num_trials` renamed to `iterations`
+* General renaming of `trial` to `iteration`, to work better with `mesa`
+* `model_parameters` renamed to `parameters` in simulation
+* Simulation results for every iteration of a simulation with the same name are stored in a single `sqlite` database
+
+### 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'

README.md

@@ -1,10 +1,66 @@
 # [SOIL](https://github.com/gsi-upm/soil)
 
+
 Soil is an extensible and user-friendly Agent-based Social Simulator for Social Networks.
 Learn how to run your own simulations with our [documentation](http://soilsim.readthedocs.io).
 
 Follow our [tutorial](examples/tutorial/soil_tutorial.ipynb) to develop your own agent models.
 
+> **Warning**
+> Soil 1.0 introduced many fundamental changes. Check the [documention on how to update your simulations to work with newer versions](docs/notes_v1.0.rst)
+
+## Features
+
+* Integration with (social) networks (through `networkx`)
+* Convenience functions and methods to easily assign agents to your model (and optionally to its network):
+  * Following a given distribution (e.g., 2 agents of type `Foo`, 10% of the network should be agents of type `Bar`)
+  * Based on the topology of the network
+* **Several types of abstractions for agents**:
+  * Finite state machine, where methods can be turned into a state
+  * Network agents, which have convenience methods to access the model's topology
+  * Generator-based agents, whose state is paused though a `yield` and resumed on the next step
+* **Reporting and data collection**:
+  * Soil models include data collection and record some data by default (# of agents, state of each agent, etc.)
+  * All data collected are exported by default to a SQLite database and a description file
+  * Options to export to other formats, such as CSV, or defining your own exporters
+  * A summary of the data collected is shown in the command line, for easy inspection
+* **An event-based scheduler**
+  * Agents can be explicit about when their next time/step should be, and not all agents run in every step. This avoids unnecessary computation.
+  * Time intervals between each step are flexible.
+  * There are primitives to specify when the next execution of an agent should be (or conditions)
+* **Actor-inspired** message-passing
+* A simulation runner (`soil.Simulation`) that can:
+  * Run models in parallel
+  * Save results to different formats
+* Simulation configuration files 
+* A command line interface (`soil`), to quickly run simulations with different parameters
+* An integrated debugger (`soil --debug`) with custom functions to print agent states and break at specific states
+
+
+## Mesa compatibility
+
+SOIL has been redesigned to integrate well with [Mesa](https://github.com/projectmesa/mesa).
+For instance, it should be possible to run a `mesa.Model` models using a `soil.Simulation` and the `soil` CLI, or to integrate the `soil.TimedActivation` scheduler on a `mesa.Model`.
+
+Note that some combinations of `mesa` and `soil` components, while technically possible, are much less useful or might yield surprising results.
+For instance, you may add any `soil.agent` agent on a regular `mesa.Model` with a vanilla scheduler from `mesa.time`.
+But in that case the agents will not get any of the advanced event-based scheduling, and most agent behaviors that depend on that may not work. 
+
+
+## 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.
+
+
+
 ## Citation 
 
 
@@ -31,24 +87,6 @@ If you use Soil in your research, don't forget to cite this paper:
 
 ```
 
-## Mesa compatibility
-
-Soil is in the process of becoming fully compatible with MESA.
-As of this writing, 
-
-This is a non-exhaustive list of tasks to achieve compatibility:
-
-* Environments.agents and mesa.Agent.agents are not the same. env is a property, and it only takes into account network and environment agents. Might rename environment_agents to other_agents or sth like that
-- [ ] Integrate `soil.Simulation` with mesa's runners:
-  - [ ] `soil.Simulation` could mimic/become a `mesa.batchrunner`
-- [ ] Integrate `soil.Environment` with `mesa.Model`:
-  - [x] `Soil.Environment` inherits from `mesa.Model`
-  - [x] `Soil.Environment` includes a Mesa-like Scheduler (see the `soil.time` module.
-- [ ] Integrate `soil.Agent` with `mesa.Agent`:
-  - [x] Rename agent.id to unique_id?
-  - [x] mesa agents can be used in soil simulations (see `examples/mesa`)
-- [ ] Document the new APIs and usage
-
 @Copyright GSI - Universidad Politécnica de Madrid 2017-2021
 
 [![SOIL](logo_gsi.png)](https://www.gsi.upm.es)

docs/configuration.rst

@@ -1,241 +0,0 @@
-Configuring a simulation
-------------------------
-
-There are two ways to configure a simulation: programmatically and with a configuration file.
-In both cases, the parameters used are the same.
-The advantage of a configuration file is that it is a clean declarative description, and it makes it easier to reproduce.
-
-Simulation configuration files can be formatted in ``json`` or ``yaml`` and they define all the parameters of a simulation.
-Here's an example (``example.yml``).
-
-.. literalinclude:: example.yml
-   :language: yaml
-
-
-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.
-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``).
-
-Now run the simulation with the command line tool:
-
-.. code:: bash
-
-   soil example.yml
-
-Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
-Three types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a ``sqlite`` file with the content of the state of every network node and the environment parameters at every step of the simulation.
-
-
-.. code::
-
-    soil_output
-    └── MyExampleSimulation
-        ├── MyExampleSimulation.dumped.yml
-        ├── MyExampleSimulation.simulation.pickle
-        ├── MyExampleSimulation_trial_0.db.sqlite
-        ├── MyExampleSimulation_trial_1.db.sqlite
-        └── MyExampleSimulation_trial_2.db.sqlite
-
-
-You may also ask soil to export the states in a ``csv`` file, and the network in gephi format (``gexf``).
-
-Network
-=======
-
-The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx.
-
-Loading a network
-#################
-
-To load an existing network, specify its path in the configuration:
-
-.. code:: yaml
-
-   ---
-   network_params:
-      path: /tmp/mynetwork.gexf
-
-Soil will try to guess what networkx method to use to read the file based on its extension.
-However, we only test using ``gexf`` files.
-
-For simple networks, you may also include them in the configuration itself using , using the ``topology`` parameter like so:
-
-.. code:: yaml
-
-   ---
-   topology:
-       nodes:
-          - id: First
-          - id: Second
-       links:
-          - source: First
-            target: Second
-
-
-Generating a random network
-###########################
-
-To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm <https://networkx.github.io/documentation/development/reference/generators.html>`_ and other parameters.
-For example, the following configuration is equivalent to :code:`nx.complete_graph(n=100)`:
-
-.. code:: yaml
-
-    network_params:
-        generator: complete_graph
-        n: 100
-
-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:
-
-.. code:: yaml
-
-    environment_params:
-        daily_probability_of_earthquake: 0.001
-        number_of_earthquakes: 0
-
-All agents have access to the environment 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.
-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.
-
-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.
-
-The simplest way is to specify a single type of agent.
-Hence, every node in the network will be associated to an agent of that type.
-
-.. code:: yaml
-
-   agent_type: 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).
-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
-            weight: 1
-          - agent_type: CounterModel
-            weight: 5
-
-The third option is to specify the type of agent on the node itself, e.g.:
-
-
-.. code:: yaml
-
-   topology:
-       nodes:
-           - id: first
-   agent_type: BaseAgent
-   states:
-       first:
-         agent_type: SISaModel
-          
-
-This would also work with a randomly generated network:
-
-
-.. code:: yaml
-
-   network:
-       generator: complete
-       n: 5
-   agent_type: BaseAgent
-   states:
-       - agent_type: SISaModel
-
-              
-
-In addition to agent type, you may add a custom initial state to the distribution.
-This is very useful to add the same agent type with different states.
-e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state:
-
-.. code:: yaml
-
-    network_agents:
-        - agent_type: SISaModel
-            weight: 9
-            state:
-            id: neutral
-        - agent_type: SISaModel
-            weight: 1
-            state:
-            id: discontent
-
-Lastly, the configuration may include initial state for one or more nodes.
-For instance, to add a state for the two nodes in this configuration:
-
-.. code:: yaml
-
-   agent_type: SISaModel
-   network:
-      generator: complete_graph
-      n: 2
-   states:
-     - id: content
-     - id: discontent
-
-
-Or to add state only to specific nodes (by ``id``).
-For example, to apply special skills to Linux Torvalds in a simulation:
-
-.. literalinclude:: ../examples/torvalds.yml
-   :language: yaml
-
-
-Environment Agents
-##################
-In addition to network agents, more agents can be added to the simulation.
-These agents are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes.
-
-
-.. code::
-
-   environment_agents:
-       - agent_type: MyAgent
-         state:
-           mood: happy
-       - agent_type: 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

@@ -3,33 +3,38 @@ name: MyExampleSimulation
 max_time: 50
 num_trials: 3
 interval: 2
-network_params:
-    generator: barabasi_albert_graph
-    n: 100
-    m: 2
-network_agents:
-    - agent_type: SISaModel
-      weight: 1
+model_params:
+  topology:
+    params:
+      generator: barabasi_albert_graph
+      n: 100
+      m: 2
+  agents:
+    distribution:
+    - agent_class: SISaModel
+      topology: True
+      ratio: 0.1
       state:
-        id: content
-    - agent_type: SISaModel
-      weight: 1
+        state_id: content
+    - agent_class: SISaModel
+      topology: True
+      ratio: .1
       state:
-        id: discontent
-    - agent_type: SISaModel
-      weight: 8
+        state_id: discontent
+    - agent_class: SISaModel
+      topology: True
+      ratio: 0.8
       state:
-        id: neutral
-environment_params:
-    prob_infect: 0.075
-    neutral_discontent_spon_prob: 0.1
-    neutral_discontent_infected_prob: 0.3
-    neutral_content_spon_prob: 0.3
-    neutral_content_infected_prob: 0.4
-    discontent_neutral: 0.5
-    discontent_content: 0.5
-    variance_d_c: 0.2
-    content_discontent: 0.2
-    variance_c_d: 0.2
-    content_neutral: 0.2
-    standard_variance: 1
+        state_id: neutral
+  prob_infect: 0.075
+  neutral_discontent_spon_prob: 0.1
+  neutral_discontent_infected_prob: 0.3
+  neutral_content_spon_prob: 0.3
+  neutral_content_infected_prob: 0.4
+  discontent_neutral: 0.5
+  discontent_content: 0.5
+  variance_d_c: 0.2
+  content_discontent: 0.2
+  variance_c_d: 0.2
+  content_neutral: 0.2
+  standard_variance: 1

docs/index.rst

@@ -1,12 +1,20 @@
-.. Soil documentation master file, created by
-   sphinx-quickstart on Tue Apr 25 12:48:56 2017.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
 Welcome to Soil's documentation!
 ================================
 
-Soil is an Agent-based Social Simulator in Python focused on Social Networks.
+Soil is an opinionated Agent-based Social Simulator in Python focused on Social Networks.
+
+.. image:: soil.png
+  :width: 80%
+  :align: center
+
+Soil can be installed through pip (see more details in the :doc:`installation` page):
+
+.. code:: bash
+
+    pip install soil
+
+
+To get started developing your own simulations and agent behaviors, check out our :doc:`Tutorial <soil_tutorial>` and the `examples on GitHub <https://github.com/gsi-upm/soil/tree/master/examples>.
 
 If you use Soil in your research, do not forget to cite this paper:
 
@@ -38,8 +46,6 @@ If you use Soil in your research, do not forget to cite this paper:
    :caption: Learn more about soil:
 
    installation
-   quickstart
-   configuration
    Tutorial <soil_tutorial>
 
 ..

docs/installation.rst

@@ -1,7 +1,10 @@
 Installation
 ------------
 
-The easiest way to install Soil is through pip, with Python >= 3.4:
+Through pip
+===========
+
+The easiest way to install Soil is through pip, with Python >= 3.8:
 
 .. code:: bash
 
@@ -14,6 +17,10 @@ Now test that it worked by running the command line tool
 
    soil --help
 
+   #or
+
+   python -m soil --help
+
 Or, if you're using using soil programmatically:
 
 .. code:: python
@@ -21,4 +28,38 @@ Or, if you're using using soil programmatically:
    import soil
    print(soil.__version__)
 
-The latest version can be installed through `GitLab <https://lab.gsi.upm.es/soil/soil.git>`_ or `GitHub <https://github.com/gsi-upm/soil>`_.
+
+
+Web UI
+======
+
+Soil also includes a web server that allows you to upload your simulations, change parameters, and visualize the results, including a timeline of the network.
+To make it work, you have to install soil like this:
+
+.. code::
+
+  pip install soil[web]
+
+Once installed, the soil web UI can be run in two ways:
+
+.. code::
+
+  soil-web
+
+  # OR
+
+  python -m soil.web
+
+
+Development
+===========
+
+The latest version can be downloaded from `GitHub <https://github.com/gsi-upm/soil>`_ and installed manually:
+
+.. code:: bash
+
+   git clone https://github.com/gsi-upm/soil
+   cd soil
+   python -m venv .venv
+   source .venv/bin/activate
+   pip install --editable .

docs/make.bat

@@ -12,7 +12,7 @@ set BUILDDIR=_build
 set SPHINXPROJ=Soil
 
 if "%1" == "" goto help
-
+eE
 %SPHINXBUILD% >NUL 2>NUL
 if errorlevel 9009 (
 	echo.

docs/mesa.rst

@@ -0,0 +1,22 @@
+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

docs/notes_v1.0.rst

@@ -0,0 +1,35 @@
+What are the main changes in version 1.0?
+#########################################
+
+Version 1.0 is a major rewrite of the Soil system, focused on simplifying the API, aligning it with Mesa, and making it easier to use.
+Unfortunately, this comes at the cost of backwards compatibility.
+
+We drew several lessons from the previous version of Soil, and tried to address them in this version.
+Mainly:
+
+- The split between simulation configuration and simulation code was overly complicated for most use cases. As a result, most users ended up reusing configuration.
+- Storing **all** the simulation data in a database is costly and unnecessary for most use cases. For most use cases, only a handful of variables need to be stored. This fits nicely with Mesa's data collection system.
+- The API was too complex, and it was difficult to understand how to use it.
+- Most parts of the API were not aligned with Mesa, which made it difficult to use Mesa's features or to integrate Soil modules with Mesa code, especially for newcomers.
+- Many parts of the API were tightly coupled, which made it difficult to find bugs, test the system and add new features.
+
+The 0.30 rewrite should provide a middle ground between Soil's opinionated approach and Mesa's flexibility.
+The new Soil is less configuration-centric.
+It aims to provide more modular and convenient functions, most of which can be used in vanilla Mesa.
+
+How are agents assigned to nodes in the network
+###############################################
+
+The constructor of the `NetworkAgent` class has two arguments: `node_id` and `topology`.
+If `topology` is not provided, it will default to `self.model.topology`.
+This assignment might err if the model does not have a `topology` attribute, but most Soil environments derive from `NetworkEnvironment`, so they include a topology by default.
+If `node_id` is not provided, a random node will be selected from the topology, until a node with no agent is found.
+Then, the `node_id` of that node is assigned to the agent.
+If no node with no agent is found, a new node is automatically added to the topology.
+
+
+Can Soil environments include more than one network / topology?
+###############################################################
+
+Yes, but each network has to be included manually.
+Somewhere between 0.20 and 0.30 we included the ability to include multiple networks, but it was deemed too complex and was removed.

docs/quickstart.rst

@@ -1,93 +0,0 @@
-Quickstart
-----------
-
-This section shows how to run your first simulation with Soil.
-For installation instructions, see :doc:`installation`.
-
-There are mainly two parts in a simulation: agent classes and simulation configuration.
-An agent class defines how the agent will behave throughout the simulation.
-The configuration includes things such as number of agents to use and their type, network topology to use, etc.
-
-
-.. image:: soil.png
-  :width: 80%
-  :align: center
-
-
-Soil includes several agent classes in the ``soil.agents`` module, and we will use them in this quickstart.
-If you are interested in developing your own agents classes, see :doc:`soil_tutorial`.
-
-Configuration
-=============
-To get you started, we will use this configuration (:download:`download the file <quickstart.yml>` directly):
-
-.. literalinclude:: quickstart.yml
-   :language: yaml
-
-The agent type used, SISa, is a very simple model.
-It only has three states (neutral, content and discontent),
-Its parameters are the probabilities to change from one state to another, either spontaneously or because of contagion from neighboring agents.
-
-Running the simulation
-======================
-
-To see the simulation in action, simply point soil to the configuration, and tell it to store the graph and the history of agent states and environment parameters at every point.
-
-.. code::
-
-    ❯ soil --graph --csv quickstart.yml                                                          [13:35:29]
-    INFO:soil:Using config(s): quickstart
-    INFO:soil:Dumping results to soil_output/quickstart : ['csv', 'gexf']
-    INFO:soil:Starting simulation quickstart at 13:35:30.
-    INFO:soil:Starting Simulation quickstart trial 0 at 13:35:30.
-    INFO:soil:Finished Simulation quickstart trial 0 at 13:35:49 in 19.43677067756653 seconds
-    INFO:soil:Starting Dumping simulation quickstart trial 0 at 13:35:49.
-    INFO:soil:Finished Dumping simulation quickstart trial 0 at 13:35:51 in 1.7733407020568848 seconds
-    INFO:soil:Dumping results to soil_output/quickstart
-    INFO:soil:Finished simulation quickstart at 13:35:51 in 21.29862952232361 seconds
-
-
-The ``CSV`` file should look like this:
-
-.. code::
-
-   agent_id,t_step,key,value
-   env,0,neutral_discontent_spon_prob,0.05
-   env,0,neutral_discontent_infected_prob,0.1
-   env,0,neutral_content_spon_prob,0.2
-   env,0,neutral_content_infected_prob,0.4
-   env,0,discontent_neutral,0.2
-   env,0,discontent_content,0.05
-   env,0,content_discontent,0.05
-   env,0,variance_d_c,0.05
-   env,0,variance_c_d,0.1
-
-Results and visualization
-=========================
-
-The environment variables are marked as ``agent_id`` env.
-Th exported values are only stored when they change.
-To find out how to get every key and value at every point in the simulation, check out the :doc:`soil_tutorial`.
-
-The dynamic graph is exported as a .gexf file which could be visualized with
-`Gephi <https://gephi.org/users/download/>`__.
-Now it is your turn to experiment with the simulation.
-Change some of the parameters, such as the number of agents, the probability of becoming content, or the type of network, and see how the results change.
-
-
-Soil also includes a web server that allows you to upload your simulations, change parameters, and visualize the results, including a timeline of the network.
-To make it work, you have to install soil like this:
-
-.. code::
-
-  pip install soil[web]
-
-Once installed, the soil web UI can be run in two ways:
-
-.. code::
-
-  soil-web
-
-  # OR
-
-  python -m soil.web

docs/quickstart.yml

@@ -1,30 +0,0 @@
----
-name: quickstart
-num_trials: 1
-max_time: 1000
-network_agents:
-  - agent_type: SISaModel
-    state:
-      id: neutral
-    weight: 1
-  - agent_type: SISaModel
-    state:
-      id: content
-    weight: 2
-network_params:
-  n: 100
-  k: 5
-  p: 0.2
-  generator: newman_watts_strogatz_graph
-environment_params:
-    neutral_discontent_spon_prob: 0.05
-    neutral_discontent_infected_prob: 0.1
-    neutral_content_spon_prob: 0.2
-    neutral_content_infected_prob: 0.4
-    discontent_neutral: 0.2
-    discontent_content: 0.05
-    content_discontent: 0.05
-    variance_d_c: 0.05
-    variance_c_d: 0.1
-    content_neutral: 0.1
-    standard_variance: 0.1

docs/requirements.txt

@@ -1 +1 @@
-ipython==7.31.1
+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

examples/complete.yml

@@ -1,27 +0,0 @@
----
-name: simple
-group: tests
-dir_path: "/tmp/"
-num_trials: 3
-max_time: 100
-interval: 1
-seed: "CompleteSeed!"
-network_params:
-  generator: complete_graph
-  n: 10
-network_agents:
-  - agent_type: CounterModel
-    weight: 1
-    state:
-      state_id: 0
-  - agent_type: AggregatedCounter
-    weight: 0.2
-environment_agents: []
-environment_class: Environment
-environment_params:
-  am_i_complete: true
-default_state:
-  incidents: 0
-states:
-  - name: 'The first node'
-  - name: 'The second node'

examples/custom_generator/custom_generator.yml

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

examples/custom_generator/generator_sim.py

@@ -0,0 +1,39 @@
+from networkx import Graph
+import random
+import networkx as nx
+from soil import Simulation, Environment, CounterModel, parameters
+
+
+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 = random.choice(nodes)
+        nodes.remove(n_in)  # Avoid loops
+        n_out = random.choice(nodes)
+        G.add_edge(n_in, n_out)
+    return G
+
+
+class GeneratorEnv(Environment):
+    """Using a custom generator for the network"""
+
+    generator: parameters.function = staticmethod(mygenerator)
+
+    def init(self):
+        self.create_network(generator=self.generator, n=10, n_edges=5)
+        self.add_agents(CounterModel)
+
+
+sim = Simulation(model=GeneratorEnv, max_steps=10, interval=1)
+
+if __name__ == '__main__':
+    sim.run(dump=False)

examples/custom_generator/mymodule.py

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

examples/custom_timeouts/custom_timeouts.py

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

examples/custom_timeouts/custom_timeouts_sim.py

@@ -0,0 +1,41 @@
+from soil.agents import FSM, state, default_state
+from soil.time import Delta
+
+
+class Fibonacci(FSM):
+    """Agent that only executes in t_steps that are Fibonacci numbers"""
+    prev = 1
+
+    @default_state
+    @state
+    def counting(self):
+        self.log("Stopping at {}".format(self.now))
+        prev, self["prev"] = self["prev"], max([self.now, self["prev"]])
+        return None, Delta(prev)
+
+
+class Odds(FSM):
+    """Agent that only executes in odd t_steps"""
+
+    @default_state
+    @state
+    def odds(self):
+        self.log("Stopping at {}".format(self.now))
+        return None, Delta(1 + self.now % 2)
+
+
+from soil import Environment, Simulation
+from networkx import complete_graph
+
+
+class TimeoutsEnv(Environment):
+    def init(self):
+        self.create_network(generator=complete_graph, n=2)
+        self.add_agent(agent_class=Fibonacci, node_id=0)
+        self.add_agent(agent_class=Odds, node_id=1)
+
+
+sim = Simulation(model=TimeoutsEnv, max_steps=10, interval=1)
+
+if __name__ == "__main__":
+    sim.run(dump=False)

examples/events_and_messages/README.md

@@ -0,0 +1,9 @@
+This example can be run like with command-line options, like this:
+
+```bash
+python cars.py --level DEBUG  -e summary --csv
+#or
+soil cars.py -e summary
+```
+
+This will set the `CSV` (save the agent and model data to a CSV) and `summary` (print the a summary of the data to stdout) exporters, and set the log level to DEBUG.

examples/events_and_messages/cars_sim.py

@@ -0,0 +1,231 @@
+"""
+This is an example of a simplified city, where there are Passengers and Drivers that can take those passengers
+from their location to their desired location.
+
+An example scenario could play like the following:
+
+- Drivers start in the `wandering` state, where they wander around the city until they have been assigned a journey
+- Passenger(1) tells every driver that it wants to request a Journey.
+- Each driver receives the request.
+  If Driver(2) is interested in providing the Journey, it asks Passenger(1) to confirm that it accepts Driver(2)'s request
+- When Passenger(1) accepts the request, two things happen:
+    - Passenger(1) changes its state to `driving_home`
+    - Driver(2) starts moving towards the origin of the Journey
+- Once Driver(2) reaches the origin, it starts moving itself and Passenger(1) to the destination of the Journey
+- When Driver(2) reaches the destination (carrying Passenger(1) along):
+    - Driver(2) starts wondering again
+    - Passenger(1) dies, and is removed from the simulation
+- If there are no more passengers available in the simulation, Drivers die
+"""
+from __future__ import annotations
+from typing import Optional
+from soil import *
+from soil import events
+from mesa.space import MultiGrid
+
+
+# More complex scenarios may use more than one type of message  between objects.
+# A common pattern is to use `enum.Enum` to represent state changes in a request.
+@dataclass
+class Journey:
+    """
+    This represents a request for a journey. Passengers and drivers exchange this object.
+
+    A journey may have a driver assigned or not. If the driver has not been assigned, this
+    object is considered a "request for a journey".
+    """
+
+    origin: (int, int)
+    destination: (int, int)
+    tip: float
+
+    passenger: Passenger
+    driver: Optional[Driver] = None
+
+
+class City(EventedEnvironment):
+    """
+    An environment with a grid where drivers and passengers will be placed.
+
+    The number of drivers and riders is configurable through its parameters:
+
+    :param str n_cars: The total number of drivers to add
+    :param str n_passengers: The number of passengers in the simulation
+    :param list agents: Specific agents to use in the simulation. It overrides the `n_passengers`
+    and `n_cars` params.
+    :param int height: Height of the internal grid
+    :param int width: Width of the internal grid
+    """
+    n_cars = 1
+    n_passengers = 10
+    height = 100
+    width = 100
+
+    def init(self):
+        self.grid = MultiGrid(width=self.width, height=self.height, torus=False)
+        if not self.agents:
+            self.add_agents(Driver, k=self.n_cars)
+            self.add_agents(Passenger, k=self.n_passengers)
+
+        for agent in self.agents:
+            self.grid.place_agent(agent, (0, 0))
+            self.grid.move_to_empty(agent)
+        
+        self.total_earnings = 0
+        self.add_model_reporter("total_earnings")
+
+    @report
+    @property
+    def number_passengers(self):
+        return self.count_agents(agent_class=Passenger)
+
+
+class Driver(Evented, FSM):
+    pos = None
+    journey = None
+    earnings = 0
+
+    def on_receive(self, msg, sender):
+        """This is not a state. It will run (and block) every time check_messages is invoked"""
+        if self.journey is None and isinstance(msg, Journey) and msg.driver is None:
+            msg.driver = self
+            self.journey = msg
+
+    def check_passengers(self):
+        """If there are no more passengers, stop forever"""
+        c = self.count_agents(agent_class=Passenger)
+        self.debug(f"Passengers left {c}")
+        if not c:
+            self.die("No more passengers")
+
+    @default_state
+    @state
+    def wandering(self):
+        """Move around the city until a journey is accepted"""
+        target = None
+        self.check_passengers()
+        self.journey = None
+        while self.journey is None:  # No potential journeys detected (see on_receive)
+            if target is None or not self.move_towards(target):
+                target = self.random.choice(
+                    self.model.grid.get_neighborhood(self.pos, moore=False)
+                )
+
+            self.check_passengers()
+            # This will call on_receive behind the scenes, and the agent's status will be updated
+            self.check_messages()
+            yield Delta(30)  # Wait at least 30 seconds before checking again
+
+        try:
+            # Re-send the journey to the passenger, to confirm that we have been selected
+            self.journey = yield self.journey.passenger.ask(self.journey, timeout=60)
+        except events.TimedOut:
+            # No journey has been accepted. Try again
+            self.journey = None
+            return
+
+        return self.driving
+
+    @state
+    def driving(self):
+        """The journey has been accepted. Pick them up and take them to their destination"""
+        self.info(f"Driving towards Passenger {self.journey.passenger.unique_id}")
+        while self.move_towards(self.journey.origin):
+            yield
+        self.info(f"Driving {self.journey.passenger.unique_id} from {self.journey.origin} to {self.journey.destination}")
+        while self.move_towards(self.journey.destination, with_passenger=True):
+            yield
+        self.info("Arrived at destination")
+        self.earnings += self.journey.tip
+        self.model.total_earnings += self.journey.tip
+        self.check_passengers()
+        return self.wandering
+
+    def move_towards(self, target, with_passenger=False):
+        """Move one cell at a time towards a target"""
+        self.debug(f"Moving { self.pos } -> { target }")
+        if target[0] == self.pos[0] and target[1] == self.pos[1]:
+            return False
+
+        next_pos = [self.pos[0], self.pos[1]]
+        for idx in [0, 1]:
+            if self.pos[idx] < target[idx]:
+                next_pos[idx] += 1
+                break
+            if self.pos[idx] > target[idx]:
+                next_pos[idx] -= 1
+                break
+        self.model.grid.move_agent(self, tuple(next_pos))
+        if with_passenger:
+            self.journey.passenger.pos = (
+                self.pos
+            )  # This could be communicated through messages
+        return True
+
+
+class Passenger(Evented, FSM):
+    pos = None
+
+    def on_receive(self, msg, sender):
+        """This is not a state. It will be run synchronously every time `check_messages` is run"""
+
+        if isinstance(msg, Journey):
+            self.journey = msg
+            return msg
+
+    @default_state
+    @state
+    def asking(self):
+        destination = (
+            self.random.randint(0, self.model.grid.height-1),
+            self.random.randint(0, self.model.grid.width-1),
+        )
+        self.journey = None
+        journey = Journey(
+            origin=self.pos,
+            destination=destination,
+            tip=self.random.randint(10, 100),
+            passenger=self,
+        )
+
+        timeout = 60
+        expiration = self.now + timeout
+        self.info(f"Asking for journey at: { self.pos }")
+        self.model.broadcast(journey, ttl=timeout, sender=self, agent_class=Driver)
+        while not self.journey:
+            self.debug(f"Waiting for responses at: { self.pos }")
+            try:
+                # This will call check_messages behind the scenes, and the agent's status will be updated
+                # If you want to avoid that, you can call it with: check=False
+                yield self.received(expiration=expiration)
+            except events.TimedOut:
+                self.info(f"Still no response. Waiting at: { self.pos }")
+                self.model.broadcast(
+                    journey, ttl=timeout, sender=self, agent_class=Driver
+                )
+                expiration = self.now + timeout
+        self.info(f"Got a response! Waiting for driver")
+        return self.driving_home
+
+    @state
+    def driving_home(self):
+        while (
+            self.pos[0] != self.journey.destination[0]
+            or self.pos[1] != self.journey.destination[1]
+        ):
+            try:
+                yield self.received(timeout=60)
+            except events.TimedOut:
+                pass
+
+        self.die("Got home safe!")
+
+
+simulation = Simulation(name="RideHailing",
+                        model=City,
+                        seed="carsSeed",
+                        max_time=1000,
+                        parameters=dict(n_passengers=2))
+
+if __name__ == "__main__":
+    easy(simulation)

examples/mesa/mesa.yml

@@ -1,21 +0,0 @@
----
-name: mesa_sim
-group: tests
-dir_path: "/tmp"
-num_trials: 3
-max_time: 100
-interval: 1
-seed: '1'
-network_params:
-  generator: social_wealth.graph_generator
-  n: 5
-network_agents:
-  - agent_type: social_wealth.SocialMoneyAgent
-    weight: 1
-environment_class: social_wealth.MoneyEnv
-environment_params:
-  num_mesa_agents: 5
-  mesa_agent_type: social_wealth.MoneyAgent
-  N: 10
-  width: 50
-  height: 50

examples/mesa/mesa_sim.py

@@ -0,0 +1,7 @@
+from soil import Simulation
+from social_wealth import MoneyEnv, graph_generator
+
+sim = Simulation(name="mesa_sim", dump=False, max_steps=10, interval=2, model=MoneyEnv, parameters=dict(generator=graph_generator, N=10, width=50, height=50))
+
+if __name__ == "__main__":
+    sim.run()

examples/mesa/server.py

@@ -1,7 +1,8 @@
 from mesa.visualization.ModularVisualization import ModularServer
-from soil.visualization import UserSettableParameter
+from mesa.visualization.UserParam import Slider, Choice
 from mesa.visualization.modules import ChartModule, NetworkModule, CanvasGrid
 from social_wealth import MoneyEnv, graph_generator, SocialMoneyAgent
+import networkx as nx
 
 
 class MyNetwork(NetworkModule):
@@ -13,15 +14,18 @@ def network_portrayal(env):
     # The model ensures there is 0 or 1 agent per node
 
     portrayal = dict()
+    wealths = {
+        node_id: data["agent"].wealth for (node_id, data) in env.G.nodes(data=True)
+    }
     portrayal["nodes"] = [
         {
-            "id": agent_id,
-            "size": env.get_agent(agent_id).wealth,
-            # "color": "#CC0000" if not agents or agents[0].wealth == 0 else "#007959",
-            "color": "#CC0000",
-            "label": f"{agent_id}: {env.get_agent(agent_id).wealth}",
+            "id": node_id,
+            "size": 2 * (wealth + 1),
+            "color": "#CC0000" if wealth == 0 else "#007959",
+            # "color": "#CC0000",
+            "label": f"{node_id}: {wealth}",
         }
-        for (agent_id) in env.G.nodes
+        for (node_id, wealth) in wealths.items()
     ]
 
     portrayal["edges"] = [
@@ -29,7 +33,6 @@ def network_portrayal(env):
         for edge_id, (source, target) in enumerate(env.G.edges)
     ]
 
-
     return portrayal
 
 
@@ -40,7 +43,7 @@ def gridPortrayal(agent):
     :param agent:  the agent in the simulation
     :return: the portrayal dictionary
     """
-    color = max(10, min(agent.wealth*10, 100))
+    color = max(10, min(agent.wealth * 10, 100))
     return {
         "Shape": "rect",
         "w": 1,
@@ -51,18 +54,17 @@ def gridPortrayal(agent):
         "Text": agent.unique_id,
         "x": agent.pos[0],
         "y": agent.pos[1],
-        "Color": f"rgba(31, 10, 255, 0.{color})"
+        "Color": f"rgba(31, 10, 255, 0.{color})",
     }
 
 
-grid = MyNetwork(network_portrayal, 500, 500, library="sigma")
+grid = MyNetwork(network_portrayal, 500, 500)
 chart = ChartModule(
     [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
 )
 
-model_params = {
-    "N": UserSettableParameter(
-        "slider",
+parameters = {
+    "N": Slider(
         "N",
         5,
         1,
@@ -70,36 +72,40 @@ model_params = {
         1,
         description="Choose how many agents to include in the model",
     ),
-    "network_agents": [{"agent_type": SocialMoneyAgent}],
-    "height": UserSettableParameter(
-        "slider",
+    "height": Slider(
         "height",
         5,
         5,
         10,
         1,
         description="Grid height",
-        ),
-    "width": UserSettableParameter(
-        "slider",
+    ),
+    "width": Slider(
         "width",
         5,
         5,
         10,
         1,
         description="Grid width",
-        ),
-    "network_params": {
-        'generator': graph_generator
-    },
+    ),
+    "agent_class": Choice(
+        "Agent class",
+        value="MoneyAgent",
+        choices=["MoneyAgent", "SocialMoneyAgent"],
+    ),
+    "generator": graph_generator,
 }
 
-canvas_element = CanvasGrid(gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500)
+
+canvas_element = CanvasGrid(
+    gridPortrayal, parameters["width"].value, parameters["height"].value, 500, 500
+)
 
 
 server = ModularServer(
-    MoneyEnv, [grid, chart, canvas_element], "Money Model", model_params
+    MoneyEnv, [grid, chart, canvas_element], "Money Model", parameters
 )
 server.port = 8521
 
-server.launch(open_browser=False)
+if __name__ == '__main__':
+    server.launch(open_browser=False)

examples/mesa/social_wealth.py

@@ -1,23 +1,26 @@
-'''
+"""
 This is an example that adds soil agents and environment in a normal
 mesa workflow.
-'''
+"""
 from mesa import Agent as MesaAgent
 from mesa.space import MultiGrid
+
 # from mesa.time import RandomActivation
 from mesa.datacollection import DataCollector
 from mesa.batchrunner import BatchRunner
 
 import networkx as nx
 
-from soil import NetworkAgent, Environment
+from soil import NetworkAgent, Environment, serialization
+
 
 def compute_gini(model):
     agent_wealths = [agent.wealth for agent in model.agents]
     x = sorted(agent_wealths)
     N = len(list(model.agents))
-    B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
-    return (1 + (1/N) - 2*B)
+    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
+    return 1 + (1 / N) - 2 * B
+
 
 class MoneyAgent(MesaAgent):
     """
@@ -25,15 +28,14 @@ class MoneyAgent(MesaAgent):
     It will only share wealth with neighbors based on grid proximity
     """
 
-    def __init__(self, unique_id, model):
+    def __init__(self, unique_id, model, wealth=1, **kwargs):
         super().__init__(unique_id=unique_id, model=model)
-        self.wealth = 1
+        self.wealth = wealth
 
     def move(self):
         possible_steps = self.model.grid.get_neighborhood(
-            self.pos,
-            moore=True,
-            include_center=False)
+            self.pos, moore=True, include_center=False
+        )
         new_position = self.random.choice(possible_steps)
         self.model.grid.move_agent(self, new_position)
 
@@ -45,21 +47,21 @@ class MoneyAgent(MesaAgent):
             self.wealth -= 1
 
     def step(self):
-        self.info("Crying wolf", self.pos)
+        print("Crying wolf", self.pos)
         self.move()
         if self.wealth > 0:
             self.give_money()
 
 
-class SocialMoneyAgent(NetworkAgent, MoneyAgent):
+class SocialMoneyAgent(MoneyAgent, NetworkAgent):
     wealth = 1
 
     def give_money(self):
         cellmates = set(self.model.grid.get_cell_list_contents([self.pos]))
-        friends = set(self.get_neighboring_agents())
+        friends = set(self.get_neighbors())
         self.info("Trying to give money")
-        self.debug("Cellmates: ", cellmates)
-        self.debug("Friends: ", friends)
+        self.info("Cellmates: ", cellmates)
+        self.info("Friends: ", friends)
 
         nearby_friends = list(cellmates & friends)
 
@@ -69,14 +71,35 @@ class SocialMoneyAgent(NetworkAgent, MoneyAgent):
             self.wealth -= 1
 
 
+def graph_generator(n=5):
+    G = nx.Graph()
+    for ix in range(n):
+        G.add_edge(0, ix)
+    return G
+
+
 class MoneyEnv(Environment):
     """A model with some number of agents."""
-    def __init__(self, N, width, height, *args, network_params, **kwargs):
 
-        network_params['n'] = N
-        super().__init__(*args, network_params=network_params, **kwargs)
+    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)
@@ -84,37 +107,31 @@ class MoneyEnv(Environment):
             self.grid.place_agent(agent, (x, y))
 
         self.datacollector = DataCollector(
-            model_reporters={"Gini": compute_gini},
-            agent_reporters={"Wealth": "wealth"})
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
+        )
 
 
-def graph_generator(n=5):
-    G = nx.Graph()
-    for ix in range(n):
-        G.add_edge(0, ix)
-    return G
+if __name__ == "__main__":
 
-if __name__ == '__main__':
-
-
-    G = graph_generator()
-    fixed_params = {"topology": G,
-                    "width": 10,
-                    "network_agents": [{"agent_type": SocialMoneyAgent,
-                                       'weight': 1}],
-                    "height": 10}
+    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 = 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

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

@@ -1,138 +0,0 @@
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_time: 300
-name: Sim_all_dumb
-network_agents:
-- agent_type: DumbViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_type: DumbViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-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
-name: Sim_half_herd
-network_agents:
-- agent_type: DumbViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_type: DumbViewer
-  state:
-    has_tv: true
-  weight: 1
-- agent_type: HerdViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-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
-name: Sim_all_herd
-network_agents:
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-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
-name: Sim_wise_herd
-network_agents:
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_type: WiseViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-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
-name: Sim_all_wise
-network_agents:
-- agent_type: WiseViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_type: WiseViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50

examples/newsspread/newsspread.py

@@ -1,86 +0,0 @@
-from soil.agents import FSM, state, default_state, prob
-import logging
-
-
-class DumbViewer(FSM):
-    '''
-    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,
-    }
-
-    @default_state
-    @state
-    def neutral(self):
-        if self['has_tv']:
-            if prob(self.env['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']):
-                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.
-    '''
-
-    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)
-
-
-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,
-    }
-
-    @state
-    def cured(self):
-        prob_cure = self.env['prob_neighbor_cure']
-        for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
-            if prob(prob_cure):
-                try:
-                    neighbor.cure()
-                except AttributeError:
-                    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.cured
-        return self.set_state(super().infected)

examples/newsspread/newsspread_sim.py

@@ -0,0 +1,134 @@
+from soil.agents import FSM, NetworkAgent, state, default_state, prob
+from soil.parameters import *
+import logging
+
+from soil.environment import Environment
+
+
+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.
+    """
+
+    has_been_infected: bool = False
+    has_tv: bool = False
+
+    @default_state
+    @state
+    def neutral(self):
+        if self.has_tv:
+            if self.prob(self.get("prob_tv_spread")):
+                return self.infected
+        if self.has_been_infected:
+            return self.infected
+
+    @state
+    def infected(self):
+        for neighbor in self.get_neighbors(state_id=self.neutral.id):
+            if self.prob(self.get("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.has_been_infected = True
+
+
+class HerdViewer(DumbViewer):
+    """
+    A viewer whose probability of infection depends on the state of its neighbors.
+    """
+
+    def infect(self):
+        """Notice again that this is NOT a state. See DumbViewer.infect for reference"""
+        infected = self.count_neighbors(state_id=self.infected.id)
+        total = self.count_neighbors()
+        prob_infect = self.get("prob_neighbor_spread") * infected / total
+        self.debug("prob_infect", prob_infect)
+        if self.prob(prob_infect):
+            self.has_been_infected = True
+
+
+class WiseViewer(HerdViewer):
+    """
+    A viewer that can change its mind.
+    """
+
+    @state
+    def cured(self):
+        prob_cure = self.get("prob_neighbor_cure")
+        for neighbor in self.get_neighbors(state_id=self.infected.id):
+            if self.prob(prob_cure):
+                try:
+                    neighbor.cure()
+                except AttributeError:
+                    self.debug("Viewer {} cannot be cured".format(neighbor.id))
+
+    def cure(self):
+        self.has_been_cured = True
+
+    @state
+    def infected(self):
+        if self.has_been_cured:
+            return self.cured
+        cured = max(self.count_neighbors(self.cured.id), 1.0)
+        infected = max(self.count_neighbors(self.infected.id), 1.0)
+        prob_cure = self.get("prob_neighbor_cure") * (cured / infected)
+        if self.prob(prob_cure):
+            return self.cured
+
+
+class NewsSpread(Environment):
+    ratio_dumb: probability = 1,
+    ratio_herd: probability = 0,
+    ratio_wise: probability = 0,
+    prob_tv_spread: probability = 0.1,
+    prob_neighbor_spread: probability = 0.1,
+    prob_neighbor_cure: probability = 0.05,
+
+    def init(self):
+        self.populate_network([DumbViewer, HerdViewer, WiseViewer],
+                              [self.ratio_dumb, self.ratio_herd, self.ratio_wise])
+
+
+from itertools import product
+from soil import Simulation
+
+
+# We want to investigate the effect of different agent distributions on the spread of news.
+# To do that, we will run different simulations, with a varying ratio of DumbViewers, HerdViewers, and WiseViewers
+# Because the effect of these agents might also depend on the network structure, we will run our simulations on two different networks:
+# one with a small-world structure and one with a connected structure.
+
+counter = 0
+for [r1, r2] in product([0, 0.5, 1.0], repeat=2):
+    for (generator, netparams) in {
+        "barabasi_albert_graph": {"m": 5},
+        "erdos_renyi_graph": {"p": 0.1},
+    }.items():
+        print(r1, r2, 1-r1-r2, generator)
+        # Create new simulation
+        netparams["n"] = 500
+        Simulation(
+            name='newspread_sim',
+            model=NewsSpread,
+            parameters=dict(
+                ratio_dumb=r1,
+                ratio_herd=r2,
+                ratio_wise=1-r1-r2,
+                network_generator=generator,
+                network_params=netparams,
+                prob_neighbor_spread=0,
+            ),
+            iterations=5,
+            max_steps=300,
+            dump=False,
+        ).run()
+        counter += 1
+        # Run all the necessary instances
+ 
+print(f"A total of {counter} simulations were run.")

examples/programmatic/programmatic.py

@@ -1,40 +0,0 @@
-'''
-Example of a fully programmatic simulation, without definition files.
-'''
-from soil import Simulation, agents
-from networkx import Graph
-import logging
-
-
-def mygenerator():
-    # Add only a node
-    G = Graph()
-    G.add_node(1)
-    return G
-
-
-class MyAgent(agents.FSM):
-
-    @agents.default_state
-    @agents.state
-    def neutral(self):
-        self.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)
-
-
-# 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()
-
-# Uncomment this to output the simulation to a YAML file
-# s.dump_yaml('simulation.yaml')

examples/programmatic/programmatic_sim.py

@@ -0,0 +1,53 @@
+"""
+Example of a fully programmatic simulation, without definition files.
+"""
+from soil import Simulation, Environment, agents
+from networkx import Graph
+import logging
+
+
+def mygenerator():
+    # Add only a node
+    G = Graph()
+    G.add_node(1)
+    G.add_node(2)
+    return G
+
+
+class MyAgent(agents.NetworkAgent, agents.FSM):
+    times_run = 0
+    @agents.default_state
+    @agents.state
+    def neutral(self):
+        self.debug("I am running")
+        if self.prob(0.2):
+            self.times_run += 1
+            self.info("This runs 2/10 times on average")
+
+
+class ProgrammaticEnv(Environment):
+
+    def init(self):
+        self.create_network(generator=mygenerator)
+        assert len(self.G)
+        self.populate_network(agent_class=MyAgent)
+        self.add_agent_reporter('times_run')
+
+
+simulation = Simulation(
+    name="Programmatic",
+    model=ProgrammaticEnv,
+    seed='Program',
+    iterations=1,
+    max_time=100,
+    dump=False,
+)
+
+if __name__ == "__main__":
+    # 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 = simulation.run()
+
+    for agent in envs[0].agents:
+        print(agent.times_run)

examples/pubcrawl/pubcrawl.py

@@ -1,175 +0,0 @@
-from soil.agents import FSM, state, default_state
-from soil import Environment
-from random import random, shuffle
-from itertools import islice
-import logging
-
-
-class CityPubs(Environment):
-    '''Environment with Pubs'''
-    level = logging.INFO
-
-    def __init__(self, *args, number_of_pubs=3, pub_capacity=10, **kwargs):
-        super(CityPubs, self).__init__(*args, **kwargs)
-        pubs = {}
-        for i in range(number_of_pubs):
-            newpub = {
-                'name': 'The awesome pub #{}'.format(i),
-                'open': True,
-                'capacity': pub_capacity,
-                'occupancy': 0,
-            }
-            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'''
-        try:
-            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'])):
-            return False
-        pub['occupancy'] += len(nodes)
-        for node in nodes:
-            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']
-
-    def exit(self, pub_id, *node_ids):
-        '''Agents will notify the pub they want to leave'''
-        try:
-            pub = self['pubs'][pub_id]
-        except KeyError:
-            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
-
-
-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.
-    '''
-    level = logging.DEBUG
-
-    defaults = {
-        'pub': None,
-        'drunk': False,
-        'pints': 0,
-        'max_pints': 3,
-    }
-
-    @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))
-        if not available_friends:
-            self.info('Life sucks and I\'m alone!')
-            return self.at_home
-        befriended = self.try_friends(available_friends)
-        if befriended:
-            return self.looking_for_pub
-
-    @state
-    def looking_for_pub(self):
-        '''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')
-        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))
-                return self.sober_in_pub
-
-    @state
-    def sober_in_pub(self):
-        '''Drink up.'''
-        self.drink()
-        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
-
-    @state
-    def at_home(self):
-        '''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)))
-    
-    def drink(self):
-        self['pints'] += 1
-        self.debug('Cheers to that')
-    
-    def kick_out(self):
-        self.set_state(self.at_home)
-
-    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))
-            return True
-        return False
-
-    def try_friends(self, others):
-        ''' Look for random agents around me and try to befriend them'''
-        befriended = False
-        k = int(10*self['openness'])
-        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))
-                befriended = True
-            else:
-                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.'''
-    level = logging.INFO
-
-    @default_state
-    @state
-    def patrol(self):
-        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))
-            drunk.kick_out()
-        else:
-            self.info('No trash to take out. Too bad.')
-
-
-if __name__ == '__main__':
-    from soil import simulation
-    simulation.run_from_config('pubcrawl.yml',
-                               dry_run=True,
-                               dump=None,
-                               parallel=False)

examples/pubcrawl/pubcrawl.yml

@@ -1,26 +0,0 @@
----
-name: pubcrawl
-num_trials: 3
-max_time: 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
-    description: Extroverted patron
-    state:
-      openness: 1.0
-    weight: 9
-  - agent_type: pubcrawl.Patron
-    description: Introverted patron
-    state:
-      openness: 0.1
-    weight: 1
-environment_agents:
-  - agent_type: pubcrawl.Police
-environment_class: pubcrawl.CityPubs
-environment_params:
-  altercations: 0
-  number_of_pubs: 3

examples/pubcrawl/pubcrawl_sim.py

@@ -0,0 +1,195 @@
+from soil.agents import FSM, NetworkAgent, state, default_state
+from soil import Environment, Simulation, parameters
+from itertools import islice
+import networkx as nx
+import logging
+
+
+class CityPubs(Environment):
+    """Environment with Pubs"""
+
+    level = logging.INFO
+    number_of_pubs: parameters.Integer = 3
+    ratio_extroverted: parameters.probability = 0.1
+    pub_capacity: parameters.Integer = 10
+    
+    def init(self):
+        self.pubs = {}
+        for i in range(self.number_of_pubs):
+            newpub = {
+                "name": "The awesome pub #{}".format(i),
+                "open": True,
+                "capacity": self.pub_capacity,
+                "occupancy": 0,
+            }
+            self.pubs[newpub["name"]] = newpub
+        self.add_agent(agent_class=Police)
+        self.populate_network([Patron.w(openness=0.1), Patron.w(openness=1)],
+                              [self.ratio_extroverted, 1-self.ratio_extroverted])
+        assert all(["agent" in node and isinstance(node["agent"], Patron) for (_, node) in self.G.nodes(data=True)])
+
+    def enter(self, pub_id, *nodes):
+        """Agents will try to enter. The pub checks if it is possible"""
+        try:
+            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"])):
+            return False
+        pub["occupancy"] += len(nodes)
+        for node in nodes:
+            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"]
+
+    def exit(self, pub_id, *node_ids):
+        """Agents will notify the pub they want to leave"""
+        try:
+            pub = self["pubs"][pub_id]
+        except KeyError:
+            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
+
+
+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
+
+    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)
+        )
+        if not available_friends:
+            self.info("Life sucks and I'm alone!")
+            return self.at_home
+        befriended = self.try_friends(available_friends)
+        if befriended:
+            return self.looking_for_pub
+
+    @state
+    def looking_for_pub(self):
+        """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")
+        group = list(self.get_neighbors())
+        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."""
+        self.drink()
+        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
+        if self.kicked_out:
+            return self.at_home
+        pass  # out drun
+
+    @state
+    def at_home(self):
+        """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)))
+
+    def drink(self):
+        self["pints"] += 1
+        self.debug("Cheers to that")
+
+    def kick_out(self):
+        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"] > 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"""
+        befriended = False
+        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.unique_id))
+                befriended = True
+            else:
+                self.debug("{} does not want to be friends".format(friend.unique_id))
+        return befriended
+
+
+class Police(FSM):
+    """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))
+        for drunk in drunksters:
+            self.info("Kicking out the trash: {}".format(drunk.unique_id))
+            drunk.kick_out()
+        else:
+            self.info("No trash to take out. Too bad.")
+
+
+sim = Simulation(
+    model=CityPubs,
+    name="pubcrawl",
+    iterations=3,
+    max_steps=10,
+    dump=False,
+    parameters=dict(
+        network_generator=nx.empty_graph,
+        network_params={"n": 30},
+        model=CityPubs,
+        altercations=0,
+        number_of_pubs=3,
+    )
+)
+
+
+if __name__ == "__main__":
+    sim.run(parallel=False)

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/rabbit_agents.py

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

examples/rabbits/rabbit_improved_sim.py

@@ -0,0 +1,153 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment, Simulation
+from soil.time import Delta
+from enum import Enum
+from collections import Counter
+import logging
+import math
+
+from rabbits_basic_sim import RabbitEnv
+
+
+class RabbitsImprovedEnv(RabbitEnv):
+    def init(self):
+        """Initialize the environment with the new versions of the agents"""
+        a1 = self.add_node(Male)
+        a2 = self.add_node(Female)
+        a1.add_edge(a2)
+        self.add_agent(RandomAccident)
+
+
+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))
+
+
+sim = Simulation(model=RabbitsImprovedEnv, max_time=100, seed="MySeed", iterations=1)
+
+if __name__ == "__main__":
+    sim.run()

examples/rabbits/rabbits.yml

@@ -1,21 +0,0 @@
----
-load_module: rabbit_agents
-name: rabbits_example
-max_time: 100
-interval: 1
-seed: MySeed
-agent_type: rabbit_agents.RabbitModel
-environment_agents:
-    - agent_type: rabbit_agents.RandomAccident
-environment_params:
-  prob_death: 0.001
-default_state:
-  mating_prob: 0.1
-topology:
-  nodes:
-    - id: 1
-      agent_type: rabbit_agents.Male
-    - id: 0
-      agent_type: rabbit_agents.Female
-  directed: true
-  links: []

examples/rabbits/rabbits_basic_sim.py

@@ -0,0 +1,161 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment, Simulation, report, parameters as params
+from collections import Counter
+import logging
+import math
+
+
+class RabbitEnv(Environment):
+    prob_death: params.probability = 1e-100
+
+    def init(self):
+        a1 = self.add_node(Male)
+        a2 = self.add_node(Female)
+        a1.add_edge(a2)
+        self.add_agent(RandomAccident)
+
+    @report
+    @property
+    def num_rabbits(self):
+        return self.count_agents(agent_class=Rabbit)
+
+    @report
+    @property
+    def num_males(self):
+        return self.count_agents(agent_class=Male)
+
+    @report
+    @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.prob_death * math.floor(
+            math.log10(max(1, rabbits_alive))
+        )
+        self.debug("Killing some rabbits with prob={}!".format(prob_death))
+        for i in self.get_agents(agent_class=Rabbit):
+            if i.state_id == i.dead.id:
+                continue
+            if self.prob(prob_death):
+                self.info("I killed a rabbit: {}".format(i.id))
+                rabbits_alive -= 1
+                i.die()
+        self.debug("Rabbits alive: {}".format(rabbits_alive))
+
+
+
+sim = Simulation(model=RabbitEnv, max_time=100, seed="MySeed", iterations=1)
+
+if __name__ == "__main__":
+    sim.run()

examples/random_delays/random_delays.py

@@ -1,45 +0,0 @@
-'''
-Example of setting a 
-Example of a fully programmatic simulation, without definition files.
-'''
-from soil import Simulation, agents
-from soil.time import Delta
-from random import expovariate
-import logging
-
-
-
-class MyAgent(agents.FSM):
-    '''
-    An agent that first does a ping
-    '''
-
-    defaults = {'pong_counts': 2}
-
-    @agents.default_state
-    @agents.state
-    def ping(self):
-        self.info('Ping')
-        return self.pong, Delta(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(expovariate(1/16))
-
-
-s = Simulation(name='Programmatic',
-               network_agents=[{'agent_type': MyAgent, 'id': 0}],
-               topology={'nodes': [{'id': 0}], 'links': []},
-               num_trials=1,
-               max_time=100,
-               agent_type=MyAgent,
-               dry_run=True)
-
-
-logging.basicConfig(level=logging.INFO)
-envs = s.run()

examples/random_delays/random_delays_sim.py

@@ -0,0 +1,47 @@
+"""
+Example of setting a 
+Example of a fully programmatic simulation, without definition files.
+"""
+from soil import Simulation, agents, Environment
+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))
+
+
+class RandomEnv(Environment):
+
+    def init(self):
+        self.add_agent(agent_class=MyAgent)
+
+
+s = Simulation(
+    name="Programmatic",
+    model=RandomEnv,
+    iterations=1,
+    max_time=100,
+    dump=False,
+)
+
+
+envs = s.run()

examples/template.yml

@@ -1,30 +0,0 @@
----
-sampler:
-  method: "SALib.sample.morris.sample"
-  N: 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:
-        state_id: 0
-    - agent_type: AggregatedCounter
-      weight: "{{ 1 - x1 }}"
-  environment_params:
-    name: "{{ x3 }}"
-  skip_test: true
-vars:
-  bounds:
-    x1: [0, 1]
-    x2: [1, 2]
-  fixed:
-    x3: ["a", "b", "c"]

examples/terrorism/TerroristNetworkModel.py

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

examples/terrorism/TerroristNetworkModel.yml

@@ -1,63 +0,0 @@
-name: TerroristNetworkModel_sim
-load_module: TerroristNetworkModel
-max_time: 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
-
-environment_params:
-  # TerroristSpreadModel
-  information_spread_intensity: 0.7
-  terrorist_additional_influence: 0.035
-  max_vulnerability: 0.7
-  prob_interaction: 0.5
-
-  # TrainingAreaModel and HavenModel
-  training_influence: 0.20
-  haven_influence: 0.20
-
-  # TerroristNetworkModel
-  vision_range: 0.30
-  sphere_influence: 2
-  weight_social_distance: 0.035
-  weight_link_distance: 0.035
-
-visualization_params:
-  # Icons downloaded from https://www.iconfinder.com/
-  shape_property: agent
-  shapes:
-    TrainingAreaModel: target
-    HavenModel: home
-    TerroristNetworkModel: person
-  colors:
-    - attr_id: civilian
-      color: '#40de40'
-    - attr_id: terrorist
-      color: red
-    - attr_id: leader
-      color: '#c16a6a'
-  background_image: 'map_4800x2860.jpg'
-  background_opacity: '0.9'
-  background_filter_color: 'blue'
-skip_test: true  # This simulation takes too long for automated tests.

examples/terrorism/TerroristNetworkModel_sim.py

@@ -0,0 +1,341 @@
+import networkx as nx
+from soil.agents import Geo, NetworkAgent, FSM, custom, state, default_state
+from soil import Environment, Simulation
+from soil.parameters import *
+from soil.utils import int_seed
+
+
+class TerroristEnvironment(Environment):
+    n: Integer = 100
+    radius: Float = 0.2
+
+    information_spread_intensity: probability = 0.7
+    terrorist_additional_influence: probability = 0.03
+    terrorist_additional_influence: probability = 0.035
+    max_vulnerability: probability = 0.7
+    prob_interaction: probability = 0.5
+
+    # TrainingAreaModel and HavenModel
+    training_influence: probability = 0.20
+    haven_influence: probability = 0.20
+
+    # TerroristNetworkModel
+    vision_range: Float = 0.30
+    sphere_influence: Integer = 2
+    weight_social_distance: Float = 0.035
+    weight_link_distance: Float = 0.035
+
+    ratio_civil: probability = 0.8
+    ratio_leader: probability = 0.1
+    ratio_training: probability = 0.05
+    ratio_haven: probability = 0.05
+
+    def init(self):
+        self.create_network(generator=self.generator, n=self.n, radius=self.radius)
+        self.populate_network([
+            TerroristNetworkModel.w(state_id='civilian'),
+            TerroristNetworkModel.w(state_id='leader'),
+            TrainingAreaModel,
+            HavenModel
+        ], [self.ratio_civil, self.ratio_leader, self.ratio_training, self.ratio_haven])
+
+    def generator(self, *args, **kwargs):
+        return nx.random_geometric_graph(*args, **kwargs, seed=int_seed(self._seed))
+
+class TerroristSpreadModel(FSM, Geo):
+    """
+    Settings:
+        information_spread_intensity
+
+        terrorist_additional_influence
+
+        min_vulnerability (optional else zero)
+
+        max_vulnerability
+    """
+
+    information_spread_intensity = 0.1
+    terrorist_additional_influence = 0.1
+    min_vulnerability = 0
+    max_vulnerability = 1
+
+    def init(self):
+        if self.state_id == self.civilian.id:  # Civilian
+            self.mean_belief = self.model.random.uniform(0.00, 0.5)
+        elif self.state_id == self.terrorist.id:  # Terrorist
+            self.mean_belief = self.random.uniform(0.8, 1.00)
+        elif self.state_id == self.leader.id:  # Leader
+            self.mean_belief = 1.00
+        else:
+            raise Exception("Invalid state id: {}".format(self["id"]))
+
+        self.vulnerability = self.random.uniform(
+            self.get("min_vulnerability", 0), self.get("max_vulnerability", 1)
+        )
+
+    @default_state
+    @state
+    def civilian(self):
+        neighbours = list(self.get_neighbors(agent_class=TerroristSpreadModel))
+        if len(neighbours) > 0:
+            # Only interact with some of the neighbors
+            interactions = list(
+                n for n in neighbours if self.random.random() <= self.model.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_neighbors(
+            state_id=[self.terrorist.id, self.leader.id]
+        ):
+            if self.betweenness(neighbour) > self.betweenness(self):
+                return self.terrorist
+
+    @state
+    def terrorist(self):
+        neighbours = self.get_agents(
+            state_id=[self.terrorist.id, self.leader.id],
+            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
+            )
+
+        # Check if there are any leaders in the group
+        leaders = list(filter(lambda x: x.state_id == self.leader.id, neighbours))
+        if not leaders:
+            # Check if this is the potential leader
+            # Stop once it's found. Otherwise, set self as leader
+            for neighbour in neighbours:
+                if self.betweenness(self) < self.betweenness(neighbour):
+                    return
+            return self.leader
+
+    def ego_search(self, steps=1, center=False, agent=None, **kwargs):
+        """Get a list of nodes in the ego network of *node* of radius *steps*"""
+        node = agent.node_id if agent else self.node_id
+        G = self.subgraph(**kwargs)
+        return nx.ego_graph(G, node, center=center, radius=steps).nodes()
+
+    def degree(self, agent, force=False):
+        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[agent.node_id]
+
+    def betweenness(self, agent, force=False):
+        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[agent.node_id]
+
+
+class TrainingAreaModel(FSM, Geo):
+    """
+    Settings:
+        training_influence
+
+        min_vulnerability
+
+    Requires TerroristSpreadModel.
+    """
+
+    training_influence = 0.1
+    min_vulnerability = 0
+
+    def init(self):
+        self.mean_believe = 1
+        self.vulnerability = 0
+
+    @default_state
+    @state
+    def terrorist(self):
+        for neighbour in self.get_neighbors(agent_class=TerroristSpreadModel):
+            if neighbour.vulnerability > self.min_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability ** (
+                    1 - self.training_influence
+                )
+
+
+class HavenModel(FSM, Geo):
+    """
+    Settings:
+        haven_influence
+
+        min_vulnerability
+
+        max_vulnerability
+
+    Requires TerroristSpreadModel.
+    """
+
+    min_vulnerability = 0
+    haven_influence = 0.1
+    max_vulnerability = 0.5
+
+    def init(self):
+        self.mean_believe = 0
+        self.vulnerability = 0
+
+    def get_occupants(self, **kwargs):
+        return self.get_neighbors(agent_class=TerroristSpreadModel,
+                                  **kwargs)
+
+    @default_state
+    @state
+    def civilian(self):
+        civilians = self.get_occupants(state_id=self.civilian.id)
+        if not civilians:
+            return self.terrorist
+
+        for neighbour in self.get_occupants():
+            if neighbour.vulnerability > self.min_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability * (
+                    1 - self.haven_influence
+                )
+        return self.civilian
+
+    @state
+    def terrorist(self):
+        for neighbour in self.get_occupants():
+            if neighbour.vulnerability < self.max_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability ** (
+                    1 - self.haven_influence
+                )
+        return self.terrorist
+
+
+class TerroristNetworkModel(TerroristSpreadModel):
+    """
+    Settings:
+        sphere_influence
+
+        vision_range
+
+        weight_social_distance
+
+        weight_link_distance
+    """
+
+    sphere_influence: float = 1
+    vision_range: float = 1
+    weight_social_distance: float = 0.5
+    weight_link_distance: float = 0.2
+
+    @state
+    def terrorist(self):
+        self.update_relationships()
+        return super().terrorist()
+
+    @state
+    def leader(self):
+        self.update_relationships()
+        return super().leader()
+
+    def update_relationships(self):
+        if self.count_neighbors(state_id=self.civilian.id) == 0:
+            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.unique_id
+                for agent in self.get_neighbors(agent_class=TerroristNetworkModel)
+            )
+            search = (close_ups | step_neighbours) - neighbours
+            for agent in self.get_agents(search):
+                social_distance = 1 / self.shortest_path_length(agent.unique_id)
+                spatial_proximity = 1 - self.get_distance(agent.unique_id)
+                prob_new_interaction = (
+                    self.weight_social_distance * social_distance
+                    + self.weight_link_distance * spatial_proximity
+                )
+                if (
+                    agent.state_id == "civilian"
+                    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.G, "pos")[self.unique_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.G, self.unique_id, target)
+        except nx.NetworkXNoPath:
+            return float("inf")
+
+
+sim = Simulation(
+    model=TerroristEnvironment,
+    iterations=1,
+    name="TerroristNetworkModel_sim",
+    max_steps=150,
+    seed="default2",
+    skip_test=False,
+    dump=False,
+)
+
+# TODO: integrate visualization
+# visualization_params:
+#   # Icons downloaded from https://www.iconfinder.com/
+#   shape_property: agent
+#   shapes:
+#     TrainingAreaModel: target
+#     HavenModel: home
+#     TerroristNetworkModel: person
+#   colors:
+#     - attr_id: civilian
+#       color: '#40de40'
+#     - attr_id: terrorist
+#       color: red
+#     - attr_id: leader
+#       color: '#c16a6a'
+#   background_image: 'map_4800x2860.jpg'
+#   background_opacity: '0.9'
+#   background_filter_color: 'blue'

examples/torvalds.yml

@@ -1,14 +0,0 @@
----
-name: torvalds_example
-max_time: 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'

examples/torvalds_sim.py

@@ -0,0 +1,25 @@
+from soil import Environment, Simulation, CounterModel, report
+
+
+# Get directory path for current file
+import os, sys, inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+
+class TorvaldsEnv(Environment):
+
+  def init(self):
+    self.create_network(path=os.path.join(currentdir, 'torvalds.edgelist'))
+    self.populate_network(CounterModel, skill_level='beginner')
+    self.agent(node_id="Torvalds").skill_level = 'God'
+    self.agent(node_id="balkian").skill_level = 'developer'
+    self.add_agent_reporter("times")
+
+  @report
+  def god_developers(self):
+    return self.count_agents(skill_level='God')
+  
+
+sim = Simulation(name='torvalds_example',
+                 max_steps=10,
+                 interval=2,
+                 model=TorvaldsEnv)

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

@@ -2,8 +2,12 @@ networkx>=2.5
 numpy
 matplotlib
 pyyaml>=5.1
-pandas>=0.23
+pandas>=1
 SALib>=1.3
 Jinja2
-Mesa>=0.8
-tsih>=0.1.5
+Mesa>=1.2
+pydantic>=1.9
+sqlalchemy>=1.4
+typing-extensions>=4.4
+annotated-types>=0.4
+tqdm>=4.64

setup.cfg

@@ -1,3 +1,7 @@
+[metadata]
+long_description = file: README.md
+long_description_content_type = text/markdown
+
 [aliases]
 test=pytest
 [tool:pytest]

setup.py

@@ -44,14 +44,20 @@ setup(
         'Operating System :: MacOS :: MacOS X',
         'Operating System :: Microsoft :: Windows',
         'Operating System :: POSIX',
-        'Programming Language :: Python :: 3'],
+        "Programming Language :: Python :: 3 :: Only",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        ],
     install_requires=install_reqs,
     extras_require=extras_require,
     tests_require=test_reqs,
     setup_requires=['pytest-runner', ],
+    pytest_plugins = ['pytest_profiling'],
     include_package_data=True,
+    python_requires=">=3.8",
     entry_points={
         'console_scripts':
-        ['soil = soil.__init__:main',
+        ['soil = soil.__main__:main',
         'soil-web = soil.web.__init__:main']
     })

soil/VERSION

@@ -1 +1 @@
-0.20.5
+1.0.0rc1

soil/__init__.py

@@ -1,8 +1,12 @@
+from __future__ import annotations
+
 import importlib
+from importlib.resources import path
 import sys
 import os
-import pdb
 import logging
+import traceback
+from contextlib import contextmanager
 
 from .version import __version__
 
@@ -11,89 +15,273 @@ try:
 except NameError:
     basestring = str
 
+from pathlib import Path
+from .analysis import *
 from .agents import *
 from . import agents
 from .simulation import *
-from .environment import Environment
+from .environment import Environment, EventedEnvironment
+from .datacollection import SoilCollector
 from . import serialization
-from . import analysis
 from .utils import logger
 from .time import *
+from .decorators import *
+
+
+def main(
+    cfg="simulation.yml",
+    exporters=None,
+    num_processes=1,
+    output="soil_output",
+    *,
+    debug=False,
+    pdb=False,
+    **kwargs,
+):
+
+    sim = None
+    if isinstance(cfg, Simulation):
+        sim = cfg
 
-def main():
     import argparse
     from . import simulation
 
-    logger.info('Running SOIL version: {}'.format(__version__))
+    logger.info("Running SOIL version: {}".format(__version__))
 
-    parser = argparse.ArgumentParser(description='Run a SOIL simulation')
-    parser.add_argument('file', type=str,
-                        nargs="?",
-                        default='simulation.yml',
-                        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.')
-    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('--level', type=str,
-                        help='Logging level')
-    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 if sim is None else "",
+        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 run the simulation",
+    )
+    parser.add_argument(
+        "--no-dump",
+        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 iteration'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.",
+    )
+    parser.add_argument(
+        "--num-processes",
+        default=num_processes,
+        help="Number of processes to use for parallel execution. Defaults to 1.",
+    )
+
+    parser.add_argument(
+        "-e",
+        "--exporter",
+        action="append",
+        default=[],
+        help="Export environment and/or simulations using this exporter",
+    )
+    parser.add_argument(
+        "--max_time",
+        default="-1",
+        help="Set maximum time for the simulation to run. ",
+    )
+
+    parser.add_argument(
+        "--max_steps",
+        default="-1",
+        help="Set maximum number of steps for the simulation to run.",
+    )
+
+    parser.add_argument(
+        "--iterations",
+        default="",
+        help="Set maximum number of iterations (runs) for the simulation.",
+    )
+
+    parser.add_argument(
+        "--seed",
+        default=None,
+        help="Manually set a seed for the simulation.",
+    )
+
+    parser.add_argument(
+        "--only-convert",
+        "--convert",
+        action="store_true",
+        help="Do not run the simulation, only convert the configuration file(s) and output them.",
+    )
+
+    parser.add_argument(
+        "--set",
+        metavar="KEY=VALUE",
+        action="append",
+        help="Set a number of parameters that will be passed to the simulation."
+        "(do not put spaces before or after the = sign). "
+        "If a value contains spaces, you should define "
+        "it with double quotes: "
+        'foo="this is a sentence". Note that '
+        "values are always treated as strings.",
+    )
 
     args = parser.parse_args()
-    logging.basicConfig(level=getattr(logging, (args.level or 'INFO').upper()))
+    level = getattr(logging, (args.level or "INFO").upper())
+    logger.setLevel(level)
 
     if args.version:
         return
 
+    exporters = exporters or [
+        "default",
+    ]
+    for exp in args.exporter:
+        if exp not in exporters:
+            exporters.append(exp)
+    if args.csv:
+        exporters.append("csv")
+    if args.graph:
+        exporters.append("gexf")
+
     if os.getcwd() not in sys.path:
         sys.path.append(os.getcwd())
     if args.module:
         importlib.import_module(args.module)
+    if output is None:
+        output = args.output
 
-    logger.info('Loading config file: {}'.format(args.file))
+    debug = debug or args.debug
 
-    if args.pdb:
+    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
+        opts = dict(
+                    dry_run=args.dry_run,
+                    dump=not args.no_dump,
+                    debug=debug,
+                    exporters=exporters,
+                    num_processes=args.num_processes,
+                    level=level,
+                    outdir=output,
+                    exporter_params=exp_params,
+                    **kwargs)
+        if args.seed is not None:
+            opts["seed"] = args.seed
+        if args.iterations:
+            opts["iterations"] =int(args.iterations)
 
-        if not os.path.exists(args.file):
-            logger.error('Please, input a valid file')
-            return
-        simulation.run_from_config(args.file,
-                                   dry_run=args.dry_run,
-                                   exporters=exporters,
-                                   parallel=(not args.synchronous),
-                                   outdir=args.output,
-                                   exporter_params=exp_params)
-    except Exception:
+        if sim:
+            logger.info("Loading simulation instance")
+            for (k, v) in opts.items():
+                setattr(sim, k, v)
+            sims = [sim]
+        else:
+            logger.info("Loading config file: {}".format(args.file))
+            if not os.path.exists(args.file):
+                logger.error("Please, input a valid file")
+                return
+
+            assert opts["debug"] == debug
+            sims = list(
+                simulation.iter_from_file(
+                    args.file,
+                    **opts,
+                )
+            )
+
+        for sim in sims:
+            assert sim.debug == debug
+
+            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.parameters
+                    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
+            max_time = float(args.max_time) if args.max_time != "-1" else None
+            max_steps = float(args.max_steps) if args.max_steps != "-1" else None
+            res.append(sim.run(max_time=max_time, max_steps=max_steps))
+
+    except Exception as ex:
         if args.pdb:
-            pdb.post_mortem()
+            from .debugging import post_mortem
+
+            print(traceback.format_exc())
+            post_mortem()
         else:
             raise
+    if debug:
+        from .debugging import set_trace
+
+        os.environ["SOIL_DEBUG"] = "true"
+        set_trace()
+    return res
 
 
-if __name__ == '__main__':
+@contextmanager
+def easy(cfg, pdb=False, debug=False, **kwargs):
+    try:
+        return main(cfg, debug=debug, pdb=pdb, **kwargs)[0]
+    except Exception as e:
+        if os.environ.get("SOIL_POSTMORTEM"):
+            from .debugging import post_mortem
+
+            print(traceback.format_exc())
+            post_mortem()
+        raise
+
+
+if __name__ == "__main__":
     main()

soil/__main__.py

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

soil/agents/BassModel.py

@@ -1,4 +1,3 @@
-import random
 from . import FSM, state, default_state
 
 
@@ -8,6 +7,7 @@ class BassModel(FSM):
         innovation_prob
         imitation_prob
     """
+
     sentimentCorrelation = 0
 
     def step(self):
@@ -16,16 +16,16 @@ class BassModel(FSM):
     @default_state
     @state
     def innovation(self):
-        if random.random() < self.innovation_prob:
+        if self.prob(self.innovation_prob):
             self.sentimentCorrelation = 1
             return self.aware
         else:
-            aware_neighbors = self.get_neighboring_agents(state_id=self.aware.id)
+            aware_neighbors = self.get_neighbors(state_id=self.aware.id)
             num_neighbors_aware = len(aware_neighbors)
-            if random.random() < (self['imitation_prob']*num_neighbors_aware):
+            if self.prob((self.imitation_prob * num_neighbors_aware)):
                 self.sentimentCorrelation = 1
                 return self.aware
 
     @state
     def aware(self):
-        self.die()
+        self.die()

soil/agents/BigMarketModel.py

@@ -1,95 +0,0 @@
-import random
-from . import FSM, state, default_state
-
-
-class BigMarketModel(FSM):
-    """
-    Settings:
-        Names:
-            enterprises [Array]
-            
-            tweet_probability_enterprises [Array]
-        Users:
-            tweet_probability_users
-            
-            tweet_relevant_probability
-            
-            tweet_probability_about [Array]
-            
-            sentiment_about [Array]
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.enterprises = self.env.environment_params['enterprises']
-        self.type = ""
-
-        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]
-        else:  # normal users
-            self.type = "User"
-            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
-
-    @state
-    def enterprise(self):
-
-        if 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:
-                    x.sentiment_about[self.id] += 0.1  # Increments for enterprise
-                else:
-                    x.sentiment_about[self.id] -= 0.1  # Decrements for enterprise
-
-                # Establecemos limites
-                if x.sentiment_about[self.id] > 1:
-                    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]
-
-    @state
-    def user(self):
-        if random.random() < self.tweet_probability:  # Tweets
-            if random.random() < self.tweet_relevant_probability:  # Tweets something relevant
-                # Tweet probability per enterprise
-                for i in range(len(self.enterprises)):
-                    random_num = 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)
-                        elif self.sentiment_about[i] == 0:
-                            # NEUTRO
-                            pass
-                        else:
-                            # POSITIVO
-                            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
-        for x in aware_neighbors:
-            if sentiment == "positive":
-                x.sentiment_about[enterprise] +=0.003
-            elif sentiment == "negative":
-                x.sentiment_about[enterprise] -=0.003
-            else:
-                pass
-
-            # Establecemos limites
-            if x.sentiment_about[enterprise] > 1:
-                x.sentiment_about[enterprise] = 1
-            if x.sentiment_about[enterprise] < -1:
-                x.sentiment_about[enterprise] = -1
-
-            x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]

soil/agents/CounterModel.py

@@ -1,19 +1,29 @@
-from . import NetworkAgent
+from . import BaseAgent, NetworkAgent
 
 
+class Ticker(BaseAgent):
+    times = 0
+
+    def step(self):
+        self.times += 1
+
 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_agents()))
-        neighbors = len(list(self.get_neighboring_agents()))
-        self['times'] = self.get('times', 0) + 1
-        self['neighbors'] = neighbors
-        self['total'] = total
+        total = len(list(self.model.schedule._agents))
+        neighbors = len(list(self.get_neighbors()))
+        self["times"] = self.get("times", 0) + 1
+        self["neighbors"] = neighbors
+        self["total"] = total
 
 
 class AggregatedCounter(NetworkAgent):
@@ -22,17 +32,15 @@ class AggregatedCounter(NetworkAgent):
     in each step and adds it to its state.
     """
 
-    defaults = {
-        'times': 0,
-        'neighbors': 0,
-        'total': 0
-    }
+    times = 0
+    neighbors = 0
+    total = 0
 
     def step(self):
         # Outside effects
-        self['times'] += 1
-        neighbors = len(list(self.get_neighboring_agents()))
-        self['neighbors'] += neighbors
-        total = len(list(self.get_agents()))
-        self['total'] += total
-        self.debug('Running for step: {}. Total: {}'.format(self.now, total))
+        self["times"] += 1
+        neighbors = len(list(self.get_neighbors()))
+        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

@@ -1,21 +1,21 @@
 from scipy.spatial import cKDTree as KDTree
 import networkx as nx
-from . import NetworkAgent, as_node
+from . import NetworkAgent
+
 
 class Geo(NetworkAgent):
-    '''In this type of network, nodes have a "pos" attribute.'''
+    """In this type of network, nodes have a "pos" attribute."""
 
-    def geo_search(self, radius, node=None, center=False, **kwargs):
-        '''Get a list of nodes whose coordinates are closer than *radius* to *node*.'''
-        node = as_node(node if node is not None else self)
+    def geo_search(self, radius, center=False, **kwargs):
+        """Get a list of nodes whose coordinates are closer than *radius* to *node*."""
+        node = self.node_id
 
         G = self.subgraph(**kwargs)
 
-        pos = nx.get_node_attributes(G, 'pos')
+        pos = nx.get_node_attributes(G, "pos")
         if not pos:
             return []
         nodes, coords = list(zip(*pos.items()))
         kdtree = KDTree(coords)  # Cannot provide generator.
         indices = kdtree.query_ball_point(pos[node], radius)
-        return [nodes[i] for i in indices if center or (nodes[i] != node)]
-
+        return [nodes[i] for i in indices if center or (nodes[i] != node)]