Merge branch 'mesa'

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).

.dockerignore

@@ -1,5 +1,7 @@
 **/soil_output
 .*
+**/.*
 **/__pycache__
 __pycache__
 *.pyc
+**/backup

.gitignore

@@ -9,3 +9,4 @@ docs/_build*
 build/*
 dist/*
 prof
+backup

.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

@@ -3,21 +3,37 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.30 UNRELEASED]
+## [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
-* Simple debugging capabilities in `soil.debugging`, with a custom `pdb.Debugger` subclass that exposes commands to list agents and their status and set breakpoints on states (for FSM agents). Try it with `soil --debug <simulation file>`
-* Ability to run 
-* Ability to 
-* The `soil.exporters` module to export the results of datacollectors (model.datacollector) into files at the end of trials/simulations
 * A modular set of classes for environments/models. Now the ability to configure the agents through an agent definition and a topology through a network configuration is split into two classes (`soil.agents.BaseEnvironment` for agents, `soil.agents.NetworkEnvironment` to add topology).
-* FSM agents can now have generators as states. They work similar to normal states, with one caveat. Only `time` values can be yielded, not a state. This is because the state will not change, it will be resumed after the yield, at the appropriate time. The return value *can* be a state, or a `(state, time)` tuple, just like in normal states.
+* 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 is very different now. Check `soil.config` for more information. We are also using Pydantic for (de)serialization.
+* Configuration schema (`Simulation`) is very simplified. All simulations should be checked
-* There may be more than one topology/network in the simulation
+* Model / environment variables are expected (but not enforced) to be a single value. This is done to more closely align with mesa
-* Ability
+* `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.
 
+## [0.20.8]
+### Changed
+* Tsih bumped to version 0.1.8
+### Fixed
+* Mentions to `id` in docs. It should be `state_id` now.
+* Fixed bug: environment agents were not being added to the simulation
 
 ## [0.20.7]
 ### Changed

README.md

@@ -1,12 +1,52 @@
 # [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)
 
-# Changes in version 0.3
+## 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.
@@ -18,27 +58,6 @@ If you have an older Soil simulation, you have two options:
 * Update the necessary configuration files and code. You may use the examples in the `examples` folder for reference, as well as the documentation.
 * Keep using a previous `soil` version.
 
-## Mesa compatibility
-
-Soil is in the process of becoming fully compatible with MESA.
-The idea is to provide a set of modular classes and functions that extend the functionality of mesa, whilst staying compatible.
-In the end, it should be possible to add regular mesa agents to a soil simulation, or use a soil agent within a mesa simulation/model.
-
-This is a non-exhaustive list of tasks to achieve compatibility:
-
-- [ ] Integrate `soil.Simulation` with mesa's runners:
-  - [ ] `soil.Simulation` could mimic/become a `mesa.batchrunner`
-- [ ] Integrate `soil.Environment` with `mesa.Model`:
-  - [x] `Soil.Environment` inherits from `mesa.Model`
-  - [x] `Soil.Environment` includes a Mesa-like Scheduler (see the `soil.time` module.
-  - [ ] Allow for `mesa.Model` to be used in a simulation.
-- [ ] Integrate `soil.Agent` with `mesa.Agent`:
-  - [x] Rename agent.id to unique_id?
-  - [x] mesa agents can be used in soil simulations (see `examples/mesa`)
-- [ ] Provide examples
-  - [ ] Using mesa modules in a soil simulation
-  - [ ] Using soil modules in a mesa simulation
-- [ ] Document the new APIs and usage
 
 
 ## Citation 

docs/configuration.rst

@@ -1,262 +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_class``), which is an agent behavior that is included in Soil.
-10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
-All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
-The state of the agents will be updated every 2 seconds (``interval``).
-
-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.
-That means both global parameters, such as the probability of disease outbreak.
-But it also means other data, such as a map, or a network topology that connects multiple agents.
-As a result, it is also typical to add custom functions in an environment that help agents interact with each other and with the state of the simulation.
-
-Last but not least, an environment controls when and how its agents will be executed.
-By default, soil environments incorporate a ``soil.time.TimedActivation`` model for agent execution (more on this on the following section).
-
-Soil environments are very similar, and often interchangeable with, mesa models (``mesa.Model``).
-
-A configuration may specify the initial value of the environment parameters:
-
-.. code:: yaml
-
-    environment_params:
-        daily_probability_of_earthquake: 0.001
-        number_of_earthquakes: 0
-
-All agents have access to the environment (and its parameters).
-
-In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
-For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.
-
-Agents
-======
-
-Agents are a way of modelling behavior.
-Agents can be characterized with two variables: agent type (``agent_class``) and state.
-The agent type is a ``soil.Agent`` class, which contains the code that encapsulates the behavior of the agent.
-The state is a set of variables, which may change during the simulation, and that the code may use to control the behavior.
-All agents provide a ``step`` method either explicitly or implicitly (by inheriting it from a superclass), which controls how the agent will behave in each step of the simulation.
-
-When and how agent steps are executed in a simulation depends entirely on the ``environment``.
-Most environments will internally use a scheduler (``mesa.time.BaseScheduler``), which controls the activation of agents.
-
-In soil, we generally used the ``soil.time.TimedActivation`` scheduler, which allows agents to specify when their next activation will happen, defaulting to a 
-
-When an agent's step is executed (generally, every ``interval`` seconds), the agent has access to its state and the environment.
-Through the environment, it can access the network topology and the state of other agents.
-
-There are two types of agents according to how they are added to the simulation: network agents and environment agent.
-
-Network Agents
-##############
-
-Network agents are attached to a node in the topology.
-The configuration file allows you to specify how agents will be mapped to topology nodes.
-
-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_class: SISaModel
-
-It is also possible to add more than one type of agent to the simulation.
-
-To control the ratio of each type (using the ``weight`` property).
-For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
-
-.. code:: yaml
-
-    network_agents:
-          - agent_class: SISaModel
-            weight: 1
-          - agent_class: 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_class: BaseAgent
-   states:
-       first:
-         agent_class: SISaModel
-          
-
-This would also work with a randomly generated network:
-
-
-.. code:: yaml
-
-   network:
-       generator: complete
-       n: 5
-   agent_class: BaseAgent
-   states:
-       - agent_class: 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_class: SISaModel
-            weight: 9
-            state:
-            id: neutral
-        - agent_class: 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_class: 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_class: MyAgent
-         state:
-           mood: happy
-       - agent_class: DummyAgent
-
-
-You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance.
-They are also useful to add behavior that has little to do with the network and the interactions within that network.
-
-Templating
-==========
-
-Sometimes, it is useful to parameterize a simulation and run it over a range of values in order to compare each run and measure the effect of those parameters in the simulation.
-For instance, you may want to run a simulation with different agent distributions.
-
-This can be done in Soil using **templates**.
-A template is a configuration where some of the values are specified with a variable.
-e.g.,  ``weight: "{{ var1 }}"`` instead of ``weight: 1``.
-There are two types of variables, depending on how their values are decided:
-
-* Fixed. A list of values is provided, and a new simulation is run for each possible value. If more than a variable is given, a new simulation will be run per combination of values.
-* Bounded/Sampled. The bounds of the variable are provided, along with a sampler method, which will be used to compute all the configuration combinations.
-
-When fixed and bounded variables are mixed, Soil generates a new configuration per combination of fixed values and bounded values.
-
-Here is an example with a single fixed variable and two bounded variable:
-
-.. literalinclude:: ../examples/template.yml
-   :language: yaml

docs/example.yml

@@ -3,24 +3,29 @@ name: MyExampleSimulation
 max_time: 50
 num_trials: 3
 interval: 2
-network_params:
+model_params:
+  topology:
+    params:
       generator: barabasi_albert_graph
       n: 100
       m: 2
-network_agents:
+  agents:
+    distribution:
     - agent_class: SISaModel
-      weight: 1
+      topology: True
+      ratio: 0.1
       state:
-        id: content
+        state_id: content
     - agent_class: SISaModel
-      weight: 1
+      topology: True
+      ratio: .1
       state:
-        id: discontent
+        state_id: discontent
     - agent_class: SISaModel
-      weight: 8
+      topology: True
+      ratio: 0.8
       state:
-        id: neutral
+        state_id: neutral
-environment_params:
   prob_infect: 0.075
   neutral_discontent_spon_prob: 0.1
   neutral_discontent_infected_prob: 0.3

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_class: SISaModel
-    state:
-      id: neutral
-    weight: 1
-  - agent_class: 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

examples/complete.yml

@@ -1,54 +0,0 @@
----
-version: '2'
-name: simple
-group: tests
-dir_path: "/tmp/"
-num_trials: 3
-max_steps: 100
-interval: 1
-seed: "CompleteSeed!"
-model_class: Environment
-model_params:
-  am_i_complete: true
-  topology:
-    params:
-      generator: complete_graph
-      n: 12
-  environment:
-  agents:
-    agent_class: CounterModel
-    topology: true
-    state:
-      times: 1
-      # In this group we are not specifying any topology
-    fixed:
-      - name: 'Environment Agent 1'
-        agent_class: BaseAgent
-        group: environment
-        topology: false
-        hidden: true
-        state:
-          times: 10
-      - agent_class: CounterModel
-        id: 0
-        group: fixed_counters
-        state:
-          times: 1
-          total: 0
-      - agent_class: CounterModel
-        group: fixed_counters
-        id: 1
-    distribution:
-      - agent_class: CounterModel
-        weight: 1
-        group: distro_counters
-        state:
-          times: 3
-      - agent_class: AggregatedCounter
-        weight: 0.2
-    override:
-      - filter:
-          agent_class: AggregatedCounter
-        n: 2
-        state:
-          times: 5

examples/custom_generator/custom_generator.yml

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

examples/custom_generator/generator_sim.py

@@ -1,6 +1,7 @@
 from networkx import Graph
 import random
 import networkx as nx
+from soil import Simulation, Environment, CounterModel, parameters
 
 
 def mygenerator(n=5, n_edges=5):
@@ -20,3 +21,19 @@ def mygenerator(n=5, n_edges=5):
         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_timeouts/custom_timeouts_sim.py

@@ -1,17 +1,17 @@
 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
-    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)
+        return None, Delta(prev)
 
 
 class Odds(FSM):
@@ -21,18 +21,21 @@ class Odds(FSM):
     @state
     def odds(self):
         self.log("Stopping at {}".format(self.now))
-        return None, self.env.timeout(1 + self.now % 2)
+        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__":
-    from soil import Simulation
+    sim.run(dump=False)
-
-    s = Simulation(
-        network_agents=[
-            {"ids": [0], "agent_class": Fibonacci},
-            {"ids": [1], "agent_class": Odds},
-        ],
-        network_params={"generator": "complete_graph", "n": 2},
-        max_time=100,
-    )
-    s.run(dry_run=True)

examples/events_and_messages/README.md

@@ -2,6 +2,8 @@ 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

@@ -18,6 +18,7 @@ An example scenario could play like the following:
 - 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
@@ -33,12 +34,13 @@ class Journey:
     A journey may have a driver assigned or not. If the driver has not been assigned, this
     object is considered a "request for a journey".
     """
+
     origin: (int, int)
     destination: (int, int)
     tip: float
 
     passenger: Passenger
-    driver: Driver = None
+    driver: Optional[Driver] = None
 
 
 class City(EventedEnvironment):
@@ -54,28 +56,25 @@ class City(EventedEnvironment):
     :param int height: Height of the internal grid
     :param int width: Width of the internal grid
     """
-    def __init__(self, *args, n_cars=1, n_passengers=10,
+    n_cars = 1
-                 height=100, width=100, agents=None,
+    n_passengers = 10
-                 model_reporters=None,
+    height = 100
-                 **kwargs):
+    width = 100
-        self.grid = MultiGrid(width=width, height=height, torus=False)
+
-        if agents is None:
+    def init(self):
-            agents = []
+        self.grid = MultiGrid(width=self.width, height=self.height, torus=False)
-            for i in range(n_cars):
+        if not self.agents:
-                agents.append({'agent_class': Driver})
+            self.add_agents(Driver, k=self.n_cars)
-            for i in range(n_passengers):
+            self.add_agents(Passenger, k=self.n_passengers)
-                agents.append({'agent_class': Passenger})
+
-        model_reporters = model_reporters or {'earnings': 'total_earnings', 'n_passengers': 'number_passengers'}
-        print('REPORTERS', model_reporters)
-        super().__init__(*args, agents=agents, model_reporters=model_reporters, **kwargs)
         for agent in self.agents:
             self.grid.place_agent(agent, (0, 0))
             self.grid.move_to_empty(agent)
         
-    @property
+        self.total_earnings = 0
-    def total_earnings(self):
+        self.add_model_reporter("total_earnings")
-        return sum(d.earnings for d in self.agents(agent_class=Driver))
 
+    @report
     @property
     def number_passengers(self):
         return self.count_agents(agent_class=Passenger)
@@ -87,31 +86,34 @@ class Driver(Evented, FSM):
     earnings = 0
 
     def on_receive(self, msg, sender):
-        '''This is not a state. It will run (and block) every time check_messages is invoked'''
+        """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'''
+        """If there are no more passengers, stop forever"""
         c = self.count_agents(agent_class=Passenger)
-        self.info(f"Passengers left {c}")
+        self.debug(f"Passengers left {c}")
         if not c:
-            self.die()
+            self.die("No more passengers")
 
     @default_state
     @state
     def wandering(self):
-        '''Move around the city until a journey is accepted'''
+        """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))
+                target = self.random.choice(
+                    self.model.grid.get_neighborhood(self.pos, moore=False)
+                )
 
             self.check_passengers()
-            self.check_messages() # This will call on_receive behind the scenes, and the agent's status will be updated
+            # 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:
@@ -126,18 +128,22 @@ class Driver(Evented, FSM):
 
     @state
     def driving(self):
-        '''The journey has been accepted. Pick them up and take them to their destination'''
+        """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'''
+        """Move one cell at a time towards a target"""
-        self.info(f"Moving { self.pos } -> { target }")
+        self.debug(f"Moving { self.pos } -> { target }")
         if target[0] == self.pos[0] and target[1] == self.pos[1]:
             return False
 
@@ -151,7 +157,9 @@ class Driver(Evented, FSM):
                 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
+            self.journey.passenger.pos = (
+                self.pos
+            )  # This could be communicated through messages
         return True
 
 
@@ -159,47 +167,65 @@ 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'''
+        """This is not a state. It will be run synchronously every time `check_messages` is run"""
 
         if isinstance(msg, Journey):
             self.journey = msg
             return msg
 
-
     @default_state
     @state
     def asking(self):
-        destination = (self.random.randint(0, self.model.grid.height), self.random.randint(0, self.model.grid.width))
+        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,
+        journey = Journey(
+            origin=self.pos,
             destination=destination,
             tip=self.random.randint(10, 100),
-                          passenger=self)
+            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.info(f"Passenger at: { self.pos }. Checking for responses.")
+            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"Passenger at: { self.pos }. Asking for journey.")
+                self.info(f"Still no response. Waiting at: { self.pos }")
-                self.model.broadcast(journey, ttl=timeout, sender=self, agent_class=Driver)
+                self.model.broadcast(
+                    journey, ttl=timeout, sender=self, agent_class=Driver
+                )
                 expiration = self.now + timeout
-            self.check_messages()
+        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]:
+        while (
+            self.pos[0] != self.journey.destination[0]
+            or self.pos[1] != self.journey.destination[1]
+        ):
+            try:
                 yield self.received(timeout=60)
-        self.info("Got home safe!")
+            except events.TimedOut:
-        self.die()
+                pass
+
+        self.die("Got home safe!")
 
 
-simulation = Simulation(name='RideHailing', model_class=City, model_params={'n_passengers': 2})
+simulation = Simulation(name="RideHailing",
+                        model=City,
+                        seed="carsSeed",
+                        max_time=1000,
+                        parameters=dict(n_passengers=2))
 
 if __name__ == "__main__":
-    with easy(simulation) as s:
+    easy(simulation)
-        s.run()

examples/mesa/mesa.yml

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

examples/mesa/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

@@ -1,5 +1,5 @@
 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
@@ -63,9 +63,8 @@ chart = ChartModule(
     [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
 )
 
-model_params = {
+parameters = {
-    "N": UserSettableParameter(
+    "N": Slider(
-        "slider",
         "N",
         5,
         1,
@@ -73,8 +72,7 @@ model_params = {
         1,
         description="Choose how many agents to include in the model",
     ),
-    "height": UserSettableParameter(
+    "height": Slider(
-        "slider",
         "height",
         5,
         5,
@@ -82,8 +80,7 @@ model_params = {
         1,
         description="Grid height",
     ),
-    "width": UserSettableParameter(
+    "width": Slider(
-        "slider",
         "width",
         5,
         5,
@@ -91,8 +88,7 @@ model_params = {
         1,
         description="Grid width",
     ),
-    "agent_class": UserSettableParameter(
+    "agent_class": Choice(
-        "choice",
         "Agent class",
         value="MoneyAgent",
         choices=["MoneyAgent", "SocialMoneyAgent"],
@@ -102,13 +98,14 @@ model_params = {
 
 
 canvas_element = CanvasGrid(
-    gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500
+    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

@@ -28,7 +28,7 @@ class MoneyAgent(MesaAgent):
     It will only share wealth with neighbors based on grid proximity
     """
 
-    def __init__(self, unique_id, model, wealth=1):
+    def __init__(self, unique_id, model, wealth=1, **kwargs):
         super().__init__(unique_id=unique_id, model=model)
         self.wealth = wealth
 
@@ -53,7 +53,7 @@ class MoneyAgent(MesaAgent):
             self.give_money()
 
 
-class SocialMoneyAgent(NetworkAgent, MoneyAgent):
+class SocialMoneyAgent(MoneyAgent, NetworkAgent):
     wealth = 1
 
     def give_money(self):

examples/newsspread/NewsSpread.ipynb

@@ -2,13 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:30.732107Z",
      "start_time": "2017-11-08T17:22:30.059855+01:00"
-    },
+    }
-    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -28,24 +27,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:35.580593Z",
      "start_time": "2017-11-08T17:22:35.542745+01:00"
     }
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
    "source": [
-    "%pylab inline\n",
+    "%matplotlib inline\n",
     "\n",
     "from soil import *"
    ]
@@ -66,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:37.242327Z",
@@ -86,7 +77,7 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_dumb\r\n",
       "network_agents:\r\n",
       "- agent_class: DumbViewer\r\n",
@@ -110,7 +101,7 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_half_herd\r\n",
       "network_agents:\r\n",
       "- agent_class: DumbViewer\r\n",
@@ -142,18 +133,18 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_herd\r\n",
       "network_agents:\r\n",
       "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
       "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
       "network_params:\r\n",
       "  generator: barabasi_albert_graph\r\n",
@@ -169,13 +160,13 @@
       "  prob_tv_spread: 0.01\r\n",
       "  prob_neighbor_cure: 0.1\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_wise_herd\r\n",
       "network_agents:\r\n",
       "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
       "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
@@ -195,13 +186,13 @@
       "  prob_tv_spread: 0.01\r\n",
       "  prob_neighbor_cure: 0.1\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_wise\r\n",
       "network_agents:\r\n",
       "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
       "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
@@ -225,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T18:07:46.781745Z",
@@ -233,7 +224,24 @@
     },
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "No objects to concatenate",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m evodumb \u001b[38;5;241m=\u001b[39m \u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msoil_output/Sim_all_dumb/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_count\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mid\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m;\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/soil/analysis.py:14\u001b[0m, in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m iterable \u001b[38;5;241m=\u001b[39m _read_data(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m group:\n\u001b[0;32m---> 14\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgroup_trials\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterable\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(iterable)\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/soil/analysis.py:201\u001b[0m, in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m    199\u001b[0m trials \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(trials)\n\u001b[1;32m    200\u001b[0m trials \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x, trials))\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrials\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgroupby(level\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39magg(aggfunc)\u001b[38;5;241m.\u001b[39mreorder_levels([\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m] ,axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/core/reshape/concat.py:368\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjs\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconcat\u001b[39m(\n\u001b[1;32m    148\u001b[0m     objs: Iterable[NDFrame] \u001b[38;5;241m|\u001b[39m Mapping[HashableT, NDFrame],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    157\u001b[0m     copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    158\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m    159\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;124;03m    Concatenate pandas objects along a particular axis.\u001b[39;00m\n\u001b[1;32m    161\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;124;03m    1   3   4\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 368\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    381\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/core/reshape/concat.py:425\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    422\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m    424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 425\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    428\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs))\n",
+      "\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
+     ]
+    }
+   ],
    "source": [
     "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', process=analysis.get_count, group=True, keys=['id']);"
    ]
@@ -721,9 +729,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-soil",
    "language": "python",
-   "name": "python3"
+   "name": "venv-soil"
   },
   "language_info": {
    "codemirror_mode": {
@@ -735,7 +743,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.10"
   },
   "toc": {
    "colors": {

examples/newsspread/NewsSpread.yml

@@ -1,133 +0,0 @@
----
-default_state: {}
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_steps: 300
-name: Sim_all_dumb
-network_agents:
-- agent_class: newsspread.DumbViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_class: newsspread.DumbViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_steps: 300
-name: Sim_half_herd
-network_agents:
-- agent_class: newsspread.DumbViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_class: newsspread.DumbViewer
-  state:
-    has_tv: true
-  weight: 1
-- agent_class: newsspread.HerdViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_class: newsspread.HerdViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_steps: 300
-name: Sim_all_herd
-network_agents:
-- agent_class: newsspread.HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_class: newsspread.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: {}
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-  prob_neighbor_cure: 0.1
-interval: 1
-max_steps: 300
-name: Sim_wise_herd
-network_agents:
-- agent_class: newsspread.HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_class: newsspread.WiseViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-  prob_neighbor_cure: 0.1
-interval: 1
-max_steps: 300
-name: Sim_all_wise
-network_agents:
-- agent_class: newsspread.WiseViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_class: newsspread.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

@@ -1,87 +0,0 @@
-from soil.agents import FSM, NetworkAgent, state, default_state, prob
-import logging
-
-
-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.
-    """
-
-    prob_neighbor_spread = 0.5
-    prob_tv_spread = 0.1
-    has_been_infected = False
-
-    @default_state
-    @state
-    def neutral(self):
-        if self["has_tv"]:
-            if self.prob(self.model["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.model["prob_neighbor_spread"]):
-                neighbor.infect()
-
-    def infect(self):
-        """
-        This is not a state. It is a function that other agents can use to try to
-        infect this agent. DumbViewer always gets infected, but other agents like
-        HerdViewer might not become infected right away
-        """
-
-        self.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.model["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.
-    """
-
-    defaults = {
-        "prob_neighbor_spread": 0.5,
-        "prob_neighbor_cure": 0.25,
-        "prob_tv_spread": 0.1,
-    }
-
-    @state
-    def cured(self):
-        prob_cure = self.model["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.model["prob_neighbor_cure"] * (cured / infected)
-        if self.prob(prob_cure):
-            return self.cured

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

@@ -1,41 +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_class=MyAgent,
-    dry_run=True,
-)
-
-
-# By default, logging will only print WARNING logs (and above).
-# You need to choose a lower logging level to get INFO/DEBUG traces
-logging.basicConfig(level=logging.INFO)
-envs = s.run()
-
-# 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.yml

@@ -1,26 +0,0 @@
----
-name: pubcrawl
-num_trials: 3
-max_steps: 10
-dump: false
-network_params:
-  # Generate 100 empty nodes. They will be assigned a network agent
-  generator: empty_graph
-  n: 30
-network_agents:
-  - agent_class: pubcrawl.Patron
-    description: Extroverted patron
-    state:
-      openness: 1.0
-    weight: 9
-  - agent_class: pubcrawl.Patron
-    description: Introverted patron
-    state:
-      openness: 0.1
-    weight: 1
-environment_agents:
-  - agent_class: pubcrawl.Police
-environment_class: pubcrawl.CityPubs
-environment_params:
-  altercations: 0
-  number_of_pubs: 3

examples/pubcrawl/pubcrawl_sim.py

@@ -1,6 +1,7 @@
 from soil.agents import FSM, NetworkAgent, state, default_state
-from soil import Environment
+from soil import Environment, Simulation, parameters
 from itertools import islice
+import networkx as nx
 import logging
 
 
@@ -8,19 +9,24 @@ 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, *args, number_of_pubs=3, pub_capacity=10, **kwargs):
+    def init(self):
-        super(CityPubs, self).__init__(*args, **kwargs)
+        self.pubs = {}
-        pubs = {}
+        for i in range(self.number_of_pubs):
-        for i in range(number_of_pubs):
             newpub = {
                 "name": "The awesome pub #{}".format(i),
                 "open": True,
-                "capacity": pub_capacity,
+                "capacity": self.pub_capacity,
                 "occupancy": 0,
             }
-            pubs[newpub["name"]] = newpub
+            self.pubs[newpub["name"]] = newpub
-        self["pubs"] = pubs
+        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"""
@@ -146,10 +152,10 @@ class Patron(FSM, NetworkAgent):
                 continue
             if friend.befriend(self):
                 self.befriend(friend, force=True)
-                self.debug("Hooray! new friend: {}".format(friend.id))
+                self.debug("Hooray! new friend: {}".format(friend.unique_id))
                 befriended = True
             else:
-                self.debug("{} does not want to be friends".format(friend.id))
+                self.debug("{} does not want to be friends".format(friend.unique_id))
         return befriended
 
 
@@ -163,13 +169,27 @@ class Police(FSM):
     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))
+            self.info("Kicking out the trash: {}".format(drunk.unique_id))
             drunk.kick_out()
         else:
             self.info("No trash to take out. Too bad.")
 
 
-if __name__ == "__main__":
+sim = Simulation(
-    from soil import 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,
+    )
+)
 
-    simulation.run_from_config("pubcrawl.yml", dry_run=True, dump=None, parallel=False)
+
+if __name__ == "__main__":
+    sim.run(parallel=False)

examples/rabbits/basic/rabbits.yml

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

examples/rabbits/improved/rabbits.yml

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

examples/rabbits/rabbit_improved_sim.py

@@ -1,23 +1,20 @@
-from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+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 RabbitEnv(Environment):
-    @property
-    def num_rabbits(self):
-        return self.count_agents(agent_class=Rabbit)
 
-    @property
+class RabbitsImprovedEnv(RabbitEnv):
-    def num_males(self):
+    def init(self):
-        return self.count_agents(agent_class=Male)
+        """Initialize the environment with the new versions of the agents"""
-
+        a1 = self.add_node(Male)
-    @property
+        a2 = self.add_node(Female)
-    def num_females(self):
+        a1.add_edge(a2)
-        return self.count_agents(agent_class=Female)
+        self.add_agent(RandomAccident)
 
 
 class Rabbit(FSM, NetworkAgent):
@@ -150,8 +147,7 @@ class RandomAccident(BaseAgent):
         self.debug("Rabbits alive: {}".format(rabbits_alive))
 
 
-if __name__ == "__main__":
+sim = Simulation(model=RabbitsImprovedEnv, max_time=100, seed="MySeed", iterations=1)
-    from soil import easy
 
-    with easy("rabbits.yml") as sim:
+if __name__ == "__main__":
     sim.run()

examples/rabbits/rabbits_basic_sim.py

@@ -1,18 +1,29 @@
-from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+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)
@@ -129,11 +140,11 @@ class RandomAccident(BaseAgent):
         if not rabbits_alive:
             return self.die()
 
-        prob_death = self.model.get("prob_death", 1e-100) * math.floor(
+        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.iter_agents(agent_class=Rabbit):
+        for i in self.get_agents(agent_class=Rabbit):
             if i.state_id == i.dead.id:
                 continue
             if self.prob(prob_death):
@@ -143,8 +154,8 @@ class RandomAccident(BaseAgent):
         self.debug("Rabbits alive: {}".format(rabbits_alive))
 
 
-if __name__ == "__main__":
-    from soil import easy
 
-    with easy("rabbits.yml") as sim:
+sim = Simulation(model=RabbitEnv, max_time=100, seed="MySeed", iterations=1)
+
+if __name__ == "__main__":
     sim.run()

examples/random_delays/random_delays_sim.py

@@ -2,7 +2,7 @@
 Example of setting a 
 Example of a fully programmatic simulation, without definition files.
 """
-from soil import Simulation, agents
+from soil import Simulation, agents, Environment
 from soil.time import Delta
 
 
@@ -29,14 +29,18 @@ class MyAgent(agents.FSM):
         return None, Delta(self.random.expovariate(1 / 16))
 
 
+class RandomEnv(Environment):
+
+    def init(self):
+        self.add_agent(agent_class=MyAgent)
+
+
 s = Simulation(
     name="Programmatic",
-    network_agents=[{"agent_class": MyAgent, "id": 0}],
+    model=RandomEnv,
-    topology={"nodes": [{"id": 0}], "links": []},
+    iterations=1,
-    num_trials=1,
     max_time=100,
-    agent_class=MyAgent,
+    dump=False,
-    dry_run=True,
 )
 
 

examples/template.yml

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

examples/terrorism/TerroristNetworkModel.yml

@@ -1,62 +0,0 @@
-name: TerroristNetworkModel_sim
-max_steps: 150 
-num_trials: 1
-model_params:
-  network_params:
-    generator: random_geometric_graph
-    radius: 0.2
-    # generator: geographical_threshold_graph
-    # theta: 20
-    n: 100
-  network_agents:
-    - agent_class: TerroristNetworkModel.TerroristNetworkModel
-      weight: 0.8
-      state:
-        id: civilian  # Civilians
-    - agent_class: TerroristNetworkModel.TerroristNetworkModel
-      weight: 0.1
-      state:
-        id: leader    # Leaders
-    - agent_class: TerroristNetworkModel.TrainingAreaModel
-      weight: 0.05
-      state:
-        id: terrorist # Terrorism
-    - agent_class: TerroristNetworkModel.HavenModel
-      weight: 0.05
-      state:
-        id: civilian  # Civilian
-
-  # 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

@@ -1,8 +1,47 @@
 import networkx as nx
-from soil.agents import Geo, NetworkAgent, FSM, state, default_state
+from soil.agents import Geo, NetworkAgent, FSM, custom, state, default_state
-from soil import Environment
+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:
@@ -13,47 +52,35 @@ class TerroristSpreadModel(FSM, Geo):
         min_vulnerability (optional else zero)
 
         max_vulnerability
-
-        prob_interaction
     """
 
-    def __init__(self, model=None, unique_id=0, state=()):
+    information_spread_intensity = 0.1
-        super().__init__(model=model, unique_id=unique_id, state=state)
+    terrorist_additional_influence = 0.1
+    min_vulnerability = 0
+    max_vulnerability = 1
 
-        self.information_spread_intensity = model.environment_params[
+    def init(self):
-            "information_spread_intensity"
+        if self.state_id == self.civilian.id:  # Civilian
-        ]
+            self.mean_belief = self.model.random.uniform(0.00, 0.5)
-        self.terrorist_additional_influence = model.environment_params[
+        elif self.state_id == self.terrorist.id:  # Terrorist
-            "terrorist_additional_influence"
-        ]
-        self.prob_interaction = model.environment_params["prob_interaction"]
-
-        if self["id"] == self.civilian.id:  # Civilian
-            self.mean_belief = self.random.uniform(0.00, 0.5)
-        elif self["id"] == self.terrorist.id:  # Terrorist
             self.mean_belief = self.random.uniform(0.8, 1.00)
-        elif self["id"] == self.leader.id:  # Leader
+        elif self.state_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 = self.random.uniform(
-                model.environment_params["min_vulnerability"],
+            self.get("min_vulnerability", 0), self.get("max_vulnerability", 1)
-                model.environment_params["max_vulnerability"],
-            )
-        else:
-            self.vulnerability = self.random.uniform(
-                0, model.environment_params["max_vulnerability"]
         )
 
+    @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.prob_interaction
+                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(
@@ -99,7 +126,7 @@ class TerroristSpreadModel(FSM, Geo):
             )
 
         # Check if there are any leaders in the group
-        leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
+        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
@@ -108,14 +135,13 @@ class TerroristSpreadModel(FSM, Geo):
                     return
             return self.leader
 
-    def ego_search(self, steps=1, center=False, node=None, **kwargs):
+    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 = as_node(node if node is not None else self)
+        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, node, force=False):
+    def degree(self, agent, force=False):
-        node = as_node(node)
         if (
             force
             or (not hasattr(self.model, "_degree"))
@@ -123,10 +149,9 @@ class TerroristSpreadModel(FSM, Geo):
         ):
             self.model._degree = nx.degree_centrality(self.G)
             self.model._last_step = self.now
-        return self.model._degree[node]
+        return self.model._degree[agent.node_id]
 
-    def betweenness(self, node, force=False):
+    def betweenness(self, agent, force=False):
-        node = as_node(node)
         if (
             force
             or (not hasattr(self.model, "_betweenness"))
@@ -134,7 +159,7 @@ class TerroristSpreadModel(FSM, Geo):
         ):
             self.model._betweenness = nx.betweenness_centrality(self.G)
             self.model._last_step = self.now
-        return self.model._betweenness[node]
+        return self.model._betweenness[agent.node_id]
 
 
 class TrainingAreaModel(FSM, Geo):
@@ -147,13 +172,12 @@ class TrainingAreaModel(FSM, Geo):
     Requires TerroristSpreadModel.
     """
 
-    def __init__(self, model=None, unique_id=0, state=()):
+    training_influence = 0.1
-        super().__init__(model=model, unique_id=unique_id, state=state)
+    min_vulnerability = 0
-        self.training_influence = model.environment_params["training_influence"]
+
-        if "min_vulnerability" in model.environment_params:
+    def init(self):
-            self.min_vulnerability = model.environment_params["min_vulnerability"]
+        self.mean_believe = 1
-        else:
+        self.vulnerability = 0
-            self.min_vulnerability = 0
 
     @default_state
     @state
@@ -177,18 +201,19 @@ class HavenModel(FSM, Geo):
     Requires TerroristSpreadModel.
     """
 
-    def __init__(self, model=None, unique_id=0, state=()):
+    min_vulnerability = 0
-        super().__init__(model=model, unique_id=unique_id, state=state)
+    haven_influence = 0.1
-        self.haven_influence = model.environment_params["haven_influence"]
+    max_vulnerability = 0.5
-        if "min_vulnerability" in model.environment_params:
+
-            self.min_vulnerability = model.environment_params["min_vulnerability"]
+    def init(self):
-        else:
+        self.mean_believe = 0
-            self.min_vulnerability = 0
+        self.vulnerability = 0
-        self.max_vulnerability = model.environment_params["max_vulnerability"]
 
     def get_occupants(self, **kwargs):
-        return self.get_neighbors(agent_class=TerroristSpreadModel, **kwargs)
+        return self.get_neighbors(agent_class=TerroristSpreadModel,
+                                  **kwargs)
 
+    @default_state
     @state
     def civilian(self):
         civilians = self.get_occupants(state_id=self.civilian.id)
@@ -224,13 +249,10 @@ class TerroristNetworkModel(TerroristSpreadModel):
         weight_link_distance
     """
 
-    def __init__(self, model=None, unique_id=0, state=()):
+    sphere_influence: float = 1
-        super().__init__(model=model, unique_id=unique_id, state=state)
+    vision_range: float = 1
-
+    weight_social_distance: float = 0.5
-        self.vision_range = model.environment_params["vision_range"]
+    weight_link_distance: float = 0.2
-        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):
@@ -257,28 +279,26 @@ class TerroristNetworkModel(TerroristSpreadModel):
                 )
             )
             neighbours = set(
-                agent.id
+                agent.unique_id
-                for agent in self.get_neighbors(
+                for agent in self.get_neighbors(agent_class=TerroristNetworkModel)
-                    agent_class=TerroristNetworkModel
-                )
             )
             search = (close_ups | step_neighbours) - neighbours
             for agent in self.get_agents(search):
-                social_distance = 1 / self.shortest_path_length(agent.id)
+                social_distance = 1 / self.shortest_path_length(agent.unique_id)
-                spatial_proximity = 1 - self.get_distance(agent.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["id"] == agent.civilian.id
+                    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.id]
+        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)
@@ -286,6 +306,36 @@ class TerroristNetworkModel(TerroristSpreadModel):
 
     def shortest_path_length(self, target):
         try:
-            return nx.shortest_path_length(self.G, self.id, target)
+            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'