Delete TerroristModel_tipo.png

.dockerignore

@@ -1,5 +0,0 @@
-**/soil_output
-.*
-**/__pycache__
-__pycache__
-*.pyc

.gitignore

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

.gitlab-ci.yml

@@ -1,28 +0,0 @@
-stages:
-  - test
-  - build
-
-build:
-  stage: build
-  image:
-    name: gcr.io/kaniko-project/executor:debug
-    entrypoint: [""]
-  tags:
-    - docker
-  script:
-    - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
-    # The skip-tls-verify flag is there because our registry certificate is self signed
-    - /kaniko/executor --context $CI_PROJECT_DIR --skip-tls-verify --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
-  only:
-    - tags
-
-
-test:
-  except:
-    - tags  # Avoid running tests for tags, because they are already run for the branch
-  tags:
-    - docker
-  image: python:3.7
-  stage: test
-  script:
-    - python setup.py test

CHANGELOG.md

@@ -1,129 +0,0 @@
-# Changelog
-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).
-
-## [0.20.1]
-### Fixed
-* Agents would run another step after dying.
-## [0.20.0]
-### Added
-* Integration with MESA
-* `not_agent_ids` parameter to get sql in history
-### Changed
-* `soil.Environment` now also inherits from `mesa.Model`
-* `soil.Agent` now also inherits from `mesa.Agent`
-* `soil.time` to replace `simpy` events, delays, duration, etc.
-* `agent.id` is not `agent.unique_id` to be compatible with `mesa`. A property `BaseAgent.id` has been added for compatibility.
-* `agent.environment` is now `agent.model`, for the same reason as above. The parameter name in `BaseAgent.__init__` has also been renamed.
-### Removed
-* `simpy` dependency and compatibility. Each agent used to be a simpy generator, but that made debugging and error handling more complex. That has been replaced by a scheduler within the `soil.Environment` class, similar to how `mesa` does it.
-* `soil.history` is now a separate package named `tsih`. The keys namedtuple uses `dict_id` instead of `agent_id`.
-### Added
-* An option to choose whether a database should be used for history 
-## [0.15.2]
-### Fixed
-* Pass the right known_modules and parameters to stats discovery in simulation
-* The configuration file must exist when launching through the CLI. If it doesn't, an error will be logged
-* Minor changes in the documentation of the CLI arguments
-### Changed
-* Stats are now exported by default
-## [0.15.1]
-### Added
-* read-only `History`
-### Fixed
-* Serialization problem with the `Environment` on parallel mode.
-* Analysis functions now work as they should in the tutorial
-## [0.15.0]
-### Added
-* Control logging level in CLI and simulation
-* `Stats` to calculate trial and simulation-wide statistics
-* Simulation statistics are stored in a separate table in history (see `History.get_stats` and `History.save_stats`, as well as `soil.stats`)
-* Aliased `NetworkAgent.G` to `NetworkAgent.topology`.
-### Changed
-* Templates in config files can be given as dictionaries in addition to strings
-* Samplers are used more explicitly
-* Removed nxsim dependency. We had already made a lot of changes, and nxsim has not been updated in 5 years.
-* Exporter methods renamed to `trial` and `end`. Added `start`.
-* `Distribution` exporter now a stats class
-* `global_topology` renamed to `topology`
-* Moved topology-related methods to `NetworkAgent`
-### Fixed
-* Temporary files used for history in dry_run mode are not longer left open 
-
-## [0.14.9]
-### Changed
-* Seed random before environment initialization
-## [0.14.8]
-### Fixed
-* Invalid directory names in Windows gsi-upm/soil#5
-## [0.14.7]
-### Changed
-* Minor change to traceback handling in async simulations
-### Fixed
-* Incomplete example in the docs (example.yml) caused an exception
-## [0.14.6]
-### Fixed
-* Bug with newer versions of networkx (0.24) where the Graph.node attribute has been removed. We have updated our calls, but the code in nxsim is not under our control, so we have pinned the networkx version until that issue is solved.
-### Changed
-* Explicit yaml.SafeLoader to avoid deprecation warnings when using yaml.load. It should not break any existing setups, but we could move to the FullLoader in the future if needed.
-
-## [0.14.4]
-### Fixed
-* Bug in `agent.get_agents()` when `state_id` is passed as a string. The tests have been modified accordingly.
-## [0.14.3]
-### Fixed
-* Incompatibility with py3.3-3.6 due to ModuleNotFoundError and TypeError in DryRunner
-## [0.14.2]
-### Fixed
-* Output path for exporters is now soil_output
-### Changed 
-* CSV output to stdout in dry_run mode
-## [0.14.1]
-### Changed
-* Exporter names in lower case
-* Add default exporter in runs
-## [0.14.0]
-### Added
-* Loading configuration from template definitions in the yaml, in preparation for SALib support.
-The definition of the variables and their possible values (i.e., a problem in SALib terms), as well as a sampler function, can be provided.
-Soil uses this definition and the template to generate a set of configurations.
-* Simulation group names, to link related simulations. For now, they are only used to group all simulations in the same group under the same folder.
-* Exporters unify exporting/dumping results and other files to disk. If `dry_run` is set to `True`, exporters will write to stdout instead of a file (useful for testing/debugging).
-* Distribution exporter, to write statistics about values and value_counts in every simulation. The results are dumped to two CSV files.
-
-### Changed
-* `dir_path` is now the directory for resources (modules, files)
-* Environments and simulations do not export or write anything by default. That task is delegated to Exporters
-
-### Removed
-* The output dir for environments and simulations (see Exporters)
-* DrawingAgent, because it wrote to disk and was not being used. We provide a partial alternative in the form of the GraphDrawing exporter. A complete alternative will be provided once the network at each state can be accessed by exporters.
-
-## Fixed
-* Modules with custom agents/environments failed to load when they were run from outside the directory of the definition file. Modules are now loaded from the directory of the simulation file in addition to the working directory
-* Memory databases (in history) can now be shared between threads.
-* Testing all examples, not just subdirectories
-
-## [0.13.8]
-### Changed
-* Moved TerroristNetworkModel to examples
-### Added
-* `get_agents` and `count_agents` methods now accept lists as inputs. They can be used to retrieve agents from node ids
-* `subgraph` in BaseAgent
-* `agents.select` method, to filter out agents
-* `skip_test` property in yaml definitions, to force skipping some examples
-* `agents.Geo`, with a search function based on postition
-* `BaseAgent.ego_search` to get nodes from the ego network of a node
-* `BaseAgent.degree` and `BaseAgent.betweenness`
-### Fixed
-
-## [0.13.7]
-### Changed
-* History now defaults to not backing up! This makes it more intuitive to load the history for examination, at the expense of rewriting something. That should not happen because History is only created in the Environment, and that has `backup=True`.
-### Added
-* Agent names are assigned based on their agent types
-* Agent logging uses the agent name.
-* FSM agents can now return a timeout in addition to a new state. e.g. `return self.idle, self.env.timeout(2)` will execute the *different_state* in 2 *units of time* (`t_step=now+2`).
-* Example of using timeouts in FSM (custom_timeouts)
-* `network_agents` entries may include an `ids` entry. If set, it should be a list of node ids that should be assigned that agent type. This complements the previous behavior of setting agent type with `weights`.

Dockerfile

@@ -1,12 +0,0 @@
-FROM python:3.7
-
-WORKDIR /usr/src/app
-
-COPY test-requirements.txt requirements.txt /usr/src/app/
-RUN pip install --no-cache-dir -r test-requirements.txt -r requirements.txt
-
-COPY ./ /usr/src/app
-
-RUN pip install '.[web]'
-
-ENTRYPOINT ["python", "-m", "soil"]

MANIFEST.in

@@ -1,7 +0,0 @@
-include requirements.txt
-include test-requirements.txt
-include README.rst
-graft soil
-global-exclude __pycache__
-global-exclude soil_output
-global-exclude *.py[co]

Makefile

@@ -1,7 +0,0 @@
-quick-test:
-	docker-compose exec dev python -m pytest -s -v
-
-test:
-	docker run -t -v $$PWD:/usr/src/app -w /usr/src/app python:3.7 python setup.py test
-
-.PHONY: test

README.md

@@ -1,54 +1,12 @@
-# [SOIL](https://github.com/gsi-upm/soil)
+#[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.
-
-## Citation 
+The purpose of Soil (SOcial network sImuLator) is provding an Agent-based Social Simulator written in Python for Social Networks.
 
 
-If you use Soil in your research, don't forget to cite this paper:
+In order to see quickly how to use Soil, you can follow the following [tutorial](https://github.com/gsi-upm/soil/blob/master/soil_tutorial.ipynb).
 
-```bibtex
-@inbook{soil-gsi-conference-2017,
-    author = "S{\'a}nchez, Jes{\'u}s M. and Iglesias, Carlos A. and S{\'a}nchez-Rada, J. Fernando",
-    booktitle = "Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection",
-    doi = "10.1007/978-3-319-59930-4_19",
-    editor = "Demazeau Y., Davidsson P., Bajo J., Vale Z.",
-    isbn = "978-3-319-59929-8",
-    keywords = "soil;social networks;agent based social simulation;python",
-    month = "June",
-    organization = "PAAMS 2017",
-    pages = "234-245",
-    publisher = "Springer Verlag",
-    series = "LNAI",
-    title = "{S}oil: {A}n {A}gent-{B}ased {S}ocial {S}imulator in {P}ython for {M}odelling and {S}imulation of {S}ocial {N}etworks",
-    url = "https://link.springer.com/chapter/10.1007/978-3-319-59930-4_19",
-    volume = "10349",
-    year = "2017",
-}
 
-```
 
-## Mesa compatibility
 
-Soil is in the process of becoming fully compatible with MESA.
-As of this writing, 
 
-This is a non-exhaustive list of tasks to achieve compatibility:
-
-* Environments.agents and mesa.Agent.agents are not the same. env is a property, and it only takes into account network and environment agents. Might rename environment_agents to other_agents or sth like that
-- [ ] Integrate `soil.Simulation` with mesa's runners:
-  - [ ] `soil.Simulation` could mimic/become a `mesa.batchrunner`
-- [ ] Integrate `soil.Environment` with `mesa.Model`:
-  - [x] `Soil.Environment` inherits from `mesa.Model`
-  - [x] `Soil.Environment` includes a Mesa-like Scheduler (see the `soil.time` module.
-- [ ] Integrate `soil.Agent` with `mesa.Agent`:
-  - [x] Rename agent.id to unique_id?
-  - [x] mesa agents can be used in soil simulations (see `examples/mesa`)
-- [ ] Document the new APIs and usage
-
-@Copyright GSI - Universidad Politécnica de Madrid 2017-2021
-
-[![SOIL](logo_gsi.png)](https://www.gsi.upm.es)
+@Copyright GSI - Universidad Politécnica de Madrid 2017

debug.py

@@ -1,3 +0,0 @@
-import soil
-soil.main()
-import pdb

docker-compose.yml

@@ -1,12 +0,0 @@
-version: '3'
-services:
-  dev:
-    build: .
-    environment:
-      PYTHONDONTWRITEBYTECODE: 1
-    volumes:
-      - .:/usr/src/app
-    tty: true
-    entrypoint: /bin/bash
-    ports:
-      - '8001:8001'

docs/conf.py

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

docs/configuration.rst

@@ -1,241 +0,0 @@
-Configuring a simulation
-------------------------
-
-There are two ways to configure a simulation: programmatically and with a configuration file.
-In both cases, the parameters used are the same.
-The advantage of a configuration file is that it is a clean declarative description, and it makes it easier to reproduce.
-
-Simulation configuration files can be formatted in ``json`` or ``yaml`` and they define all the parameters of a simulation.
-Here's an example (``example.yml``).
-
-.. literalinclude:: example.yml
-   :language: yaml
-
-
-This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
-The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil.
-10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
-All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
-The state of the agents will be updated every 2 seconds (``interval``).
-
-Now run the simulation with the command line tool:
-
-.. code:: bash
-
-   soil example.yml
-
-Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
-Three types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a ``sqlite`` file with the content of the state of every network node and the environment parameters at every step of the simulation.
-
-
-.. code::
-
-    soil_output
-    └── MyExampleSimulation
-        ├── MyExampleSimulation.dumped.yml
-        ├── MyExampleSimulation.simulation.pickle
-        ├── MyExampleSimulation_trial_0.db.sqlite
-        ├── MyExampleSimulation_trial_1.db.sqlite
-        └── MyExampleSimulation_trial_2.db.sqlite
-
-
-You may also ask soil to export the states in a ``csv`` file, and the network in gephi format (``gexf``).
-
-Network
-=======
-
-The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx.
-
-Loading a network
-#################
-
-To load an existing network, specify its path in the configuration:
-
-.. code:: yaml
-
-   ---
-   network_params:
-      path: /tmp/mynetwork.gexf
-
-Soil will try to guess what networkx method to use to read the file based on its extension.
-However, we only test using ``gexf`` files.
-
-For simple networks, you may also include them in the configuration itself using , using the ``topology`` parameter like so:
-
-.. code:: yaml
-
-   ---
-   topology:
-       nodes:
-          - id: First
-          - id: Second
-       links:
-          - source: First
-            target: Second
-
-
-Generating a random network
-###########################
-
-To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm <https://networkx.github.io/documentation/development/reference/generators.html>`_ and other parameters.
-For example, the following configuration is equivalent to :code:`nx.complete_graph(n=100)`:
-
-.. code:: yaml
-
-    network_params:
-        generator: complete_graph
-        n: 100
-
-Environment
-============
-The environment is the place where the shared state of the simulation is stored.
-For instance, the probability of disease outbreak.
-The configuration file may specify the initial value of the environment parameters:
-
-.. code:: yaml
-
-    environment_params:
-        daily_probability_of_earthquake: 0.001
-        number_of_earthquakes: 0
-
-All agents have access to the environment parameters.
-
-In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
-For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.
-
-
-Agents
-======
-Agents are a way of modelling behavior.
-Agents can be characterized with two variables: agent type (``agent_type``) and state.
-Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters.
-Through the environment, it can access the network topology and the state of other agents.
-
-There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
-
-Network Agents
-##############
-Network agents are attached to a node in the topology.
-The configuration file allows you to specify how agents will be mapped to topology nodes.
-
-The simplest way is to specify a single type of agent.
-Hence, every node in the network will be associated to an agent of that type.
-
-.. code:: yaml
-
-   agent_type: SISaModel
-
-It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property).
-For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
-
-.. code:: yaml
-
-    network_agents:
-          - agent_type: SISaModel
-            weight: 1
-          - agent_type: CounterModel
-            weight: 5
-
-The third option is to specify the type of agent on the node itself, e.g.:
-
-
-.. code:: yaml
-
-   topology:
-       nodes:
-           - id: first
-   agent_type: BaseAgent
-   states:
-       first:
-         agent_type: SISaModel
-          
-
-This would also work with a randomly generated network:
-
-
-.. code:: yaml
-
-   network:
-       generator: complete
-       n: 5
-   agent_type: BaseAgent
-   states:
-       - agent_type: SISaModel
-
-              
-
-In addition to agent type, you may add a custom initial state to the distribution.
-This is very useful to add the same agent type with different states.
-e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state:
-
-.. code:: yaml
-
-    network_agents:
-        - agent_type: SISaModel
-            weight: 9
-            state:
-            id: neutral
-        - agent_type: SISaModel
-            weight: 1
-            state:
-            id: discontent
-
-Lastly, the configuration may include initial state for one or more nodes.
-For instance, to add a state for the two nodes in this configuration:
-
-.. code:: yaml
-
-   agent_type: SISaModel
-   network:
-      generator: complete_graph
-      n: 2
-   states:
-     - id: content
-     - id: discontent
-
-
-Or to add state only to specific nodes (by ``id``).
-For example, to apply special skills to Linux Torvalds in a simulation:
-
-.. literalinclude:: ../examples/torvalds.yml
-   :language: yaml
-
-
-Environment Agents
-##################
-In addition to network agents, more agents can be added to the simulation.
-These agents are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes.
-
-
-.. code::
-
-   environment_agents:
-       - agent_type: MyAgent
-         state:
-           mood: happy
-       - agent_type: DummyAgent
-
-
-You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance.
-They are also useful to add behavior that has little to do with the network and the interactions within that network.
-
-Templating
-==========
-
-Sometimes, it is useful to parameterize a simulation and run it over a range of values in order to compare each run and measure the effect of those parameters in the simulation.
-For instance, you may want to run a simulation with different agent distributions.
-
-This can be done in Soil using **templates**.
-A template is a configuration where some of the values are specified with a variable.
-e.g.,  ``weight: "{{ var1 }}"`` instead of ``weight: 1``.
-There are two types of variables, depending on how their values are decided:
-
-* Fixed. A list of values is provided, and a new simulation is run for each possible value. If more than a variable is given, a new simulation will be run per combination of values.
-* Bounded/Sampled. The bounds of the variable are provided, along with a sampler method, which will be used to compute all the configuration combinations.
-
-When fixed and bounded variables are mixed, Soil generates a new configuration per combination of fixed values and bounded values.
-
-Here is an example with a single fixed variable and two bounded variable:
-
-.. literalinclude:: ../examples/template.yml
-   :language: yaml

docs/example.yml

@@ -1,35 +0,0 @@
----
-name: MyExampleSimulation
-max_time: 50
-num_trials: 3
-interval: 2
-network_params:
-    generator: barabasi_albert_graph
-    n: 100
-    m: 2
-network_agents:
-    - agent_type: SISaModel
-      weight: 1
-      state:
-        id: content
-    - agent_type: SISaModel
-      weight: 1
-      state:
-        id: discontent
-    - agent_type: SISaModel
-      weight: 8
-      state:
-        id: neutral
-environment_params:
-    prob_infect: 0.075
-    neutral_discontent_spon_prob: 0.1
-    neutral_discontent_infected_prob: 0.3
-    neutral_content_spon_prob: 0.3
-    neutral_content_infected_prob: 0.4
-    discontent_neutral: 0.5
-    discontent_content: 0.5
-    variance_d_c: 0.2
-    content_discontent: 0.2
-    variance_c_d: 0.2
-    content_neutral: 0.2
-    standard_variance: 1

docs/index.rst

@@ -6,43 +6,16 @@
 Welcome to Soil's documentation!
 ================================
 
-Soil is an Agent-based Social Simulator in Python focused on Social Networks.
-
-If you use Soil in your research, do not forget to cite this paper:
-
-.. code:: bibtex
-
-    @inbook{soil-gsi-conference-2017,
-        author = "S{\'a}nchez, Jes{\'u}s M. and Iglesias, Carlos A. and S{\'a}nchez-Rada, J. Fernando",
-        booktitle = "Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection",
-        doi = "10.1007/978-3-319-59930-4_19",
-        editor = "Demazeau Y., Davidsson P., Bajo J., Vale Z.",
-        isbn = "978-3-319-59929-8",
-        keywords = "soil;social networks;agent based social simulation;python",
-        month = "June",
-        organization = "PAAMS 2017",
-        pages = "234-245",
-        publisher = "Springer Verlag",
-        series = "LNAI",
-        title = "{S}oil: {A}n {A}gent-{B}ased {S}ocial {S}imulator in {P}ython for {M}odelling and {S}imulation of {S}ocial {N}etworks",
-        url = "https://link.springer.com/chapter/10.1007/978-3-319-59930-4_19",
-        volume = "10349",
-        year = "2017",
-    }
-
-
-
+Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks.
 
 .. toctree::
-   :maxdepth: 0
+   :maxdepth: 2
    :caption: Learn more about soil:
 
    installation
-   quickstart
-   configuration
-   Tutorial <soil_tutorial>
+   usage
+   models
 
-..
 
 
 .. Indices and tables

docs/installation.rst

@@ -1,24 +1,7 @@
 Installation
 ------------
-
-The easiest way to install Soil is through pip, with Python >= 3.4:
+The latest version can be installed through GitLab.
 
 .. code:: bash
 
-   pip install soil
-
-
-Now test that it worked by running the command line tool
-
-.. code:: bash
-
-   soil --help
-
-Or, if you're using using soil programmatically:
-
-.. code:: python
-
-   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>`_.
+   git clone https://lab.cluster.gsi.dit.upm.es/soil/soil.git

docs/models.rst

@@ -0,0 +1,112 @@
+Developing new models
+---------------------
+This document describes how to develop a new analysis model.
+
+What is a model?
+================
+
+A model defines the behaviour of the agents with a view to assessing their effects on the system as a whole.
+In practice, a model consists of at least two parts:
+
+* Python module: the actual code that describes the behaviour.
+* Setting up the variables in the Settings JSON file.
+
+This separation allows us to run the simulation with different agents.
+
+Models Code
+===========
+
+All the models are imported to the main file. The initialization look like this:
+
+.. code:: python
+
+    import settings
+
+    networkStatus = {}  # Dict that will contain the status of every agent in the network
+
+    sentimentCorrelationNodeArray = []
+    for x in range(0, settings.network_params["number_of_nodes"]):
+        sentimentCorrelationNodeArray.append({'id': x})
+    # Initialize agent states. Let's assume everyone is normal.
+    init_states = [{'id': 0, } for _ in range(settings.network_params["number_of_nodes"])]
+        # add keys as as necessary, but "id" must always refer to that state category
+
+A new model have to inherit the BaseBehaviour class which is in the same module.
+There are two basics methods:
+
+* __init__
+* step: used to define the behaviour over time.
+
+Variable Initialization
+=======================
+
+The different parameters of the model have to be initialize in the Simulation Settings JSON file which will be
+passed as a parameter to the simulation.
+
+.. code:: json
+
+    {
+        "agent": ["SISaModel","ControlModelM2"],
+
+        "neutral_discontent_spon_prob": 0.04,
+        "neutral_discontent_infected_prob": 0.04,
+        "neutral_content_spon_prob": 0.18,
+        "neutral_content_infected_prob": 0.02,
+
+        "discontent_neutral": 0.13,
+        "discontent_content": 0.07,
+        "variance_d_c": 0.02,
+
+        "content_discontent": 0.009,
+        "variance_c_d": 0.003,
+        "content_neutral": 0.088,
+
+        "standard_variance": 0.055,
+
+
+        "prob_neutral_making_denier": 0.035,
+
+        "prob_infect": 0.075,
+
+        "prob_cured_healing_infected": 0.035,
+        "prob_cured_vaccinate_neutral": 0.035,
+
+        "prob_vaccinated_healing_infected": 0.035,
+        "prob_vaccinated_vaccinate_neutral": 0.035,
+        "prob_generate_anti_rumor": 0.035
+    }
+
+In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
+The simulation returns one result for each model, executing each model separately. For the usage, see :doc:`usage`.
+
+Example Model
+=============
+
+In this section, we will implement a Sentiment Correlation Model.
+
+The class would look like this:
+
+.. code:: python
+
+    from ..BaseBehaviour import *
+    from .. import sentimentCorrelationNodeArray
+
+    class SentimentCorrelationModel(BaseBehaviour):
+
+        def __init__(self, environment=None, agent_id=0, state=()):
+            super().__init__(environment=environment, agent_id=agent_id, state=state)
+            self.outside_effects_prob = environment.environment_params['outside_effects_prob']
+            self.anger_prob = environment.environment_params['anger_prob']
+            self.joy_prob = environment.environment_params['joy_prob']
+            self.sadness_prob = environment.environment_params['sadness_prob']
+            self.disgust_prob = environment.environment_params['disgust_prob']
+            self.time_awareness = []
+            for i in range(4):  # In this model we have 4 sentiments
+                self.time_awareness.append(0)  # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
+            sentimentCorrelationNodeArray[self.id][self.env.now] = 0
+
+        def step(self, now):
+            self.behaviour()  # Method which define the behaviour
+            super().step(now)
+
+The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file.

docs/quickstart.rst

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

docs/quickstart.yml

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

docs/requirements.txt

@@ -1 +0,0 @@
-ipython==7.23

docs/usage.rst

@@ -0,0 +1,99 @@
+Usage
+-----
+
+First of all, you need to install the package. See :doc:`installation` for installation instructions.
+
+Simulation Settings
+===================
+
+Once installed, before running a simulation, you need to configure it.
+
+* In the Settings JSON file you will find the configuration of the network.
+
+  .. code:: python
+
+    {
+        "network_type": 1,
+        "number_of_nodes": 1000,
+        "max_time": 50,
+        "num_trials": 1,
+        "timeout": 2
+    }
+
+* In the Settings JSON file, you will also find the configuration of the models.
+
+Network Types
+=============
+
+There are three types of network implemented, but you could add more.
+
+.. code:: python
+
+    if settings.network_type == 0:
+        G = nx.complete_graph(settings.number_of_nodes)
+    if settings.network_type == 1:
+        G = nx.barabasi_albert_graph(settings.number_of_nodes, 10)
+    if settings.network_type == 2:
+        G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
+    # More types of networks can be added here
+
+Models Settings
+===============
+
+After having configured the simulation, the next step is setting up the variables of the models.
+For this, you will need to modify the Settings JSON file again.
+
+.. code:: json
+
+    {
+        "agent": ["SISaModel","ControlModelM2"],
+
+        "neutral_discontent_spon_prob": 0.04,
+        "neutral_discontent_infected_prob": 0.04,
+        "neutral_content_spon_prob": 0.18,
+        "neutral_content_infected_prob": 0.02,
+
+        "discontent_neutral": 0.13,
+        "discontent_content": 0.07,
+        "variance_d_c": 0.02,
+
+        "content_discontent": 0.009,
+        "variance_c_d": 0.003,
+        "content_neutral": 0.088,
+
+        "standard_variance": 0.055,
+
+
+        "prob_neutral_making_denier": 0.035,
+
+        "prob_infect": 0.075,
+
+        "prob_cured_healing_infected": 0.035,
+        "prob_cured_vaccinate_neutral": 0.035,
+
+        "prob_vaccinated_healing_infected": 0.035,
+        "prob_vaccinated_vaccinate_neutral": 0.035,
+        "prob_generate_anti_rumor": 0.035
+    }
+
+In this file you will define the different models you are going to simulate. You can simulate as many models
+as you want. Each model will be simulated separately.
+
+After setting up the models, you have to initialize the parameters of each one. You will find the parameters needed
+in the documentation of each model.
+
+Parameter validation will fail if a required parameter without a default has not been provided.
+
+Running the Simulation
+======================
+
+After setting all the configuration, you will be able to run the simulation. All you need to do is execute:
+
+.. code:: bash
+
+    python3 soil.py
+
+The simulation will return a dynamic graph .gexf file which could be visualized with
+`Gephi <https://gephi.org/users/download/>`__.
+
+It will also return one .png picture for each model simulated.

examples/complete.yml

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

examples/custom_generator/custom_generator.yml

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

examples/custom_generator/mymodule.py

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

examples/custom_timeouts/custom_timeouts.py

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

examples/mesa/mesa.yml

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

examples/mesa/server.py

@@ -1,105 +0,0 @@
-from mesa.visualization.ModularVisualization import ModularServer
-from soil.visualization import UserSettableParameter
-from mesa.visualization.modules import ChartModule, NetworkModule, CanvasGrid
-from social_wealth import MoneyEnv, graph_generator, SocialMoneyAgent
-
-
-class MyNetwork(NetworkModule):
-    def render(self, model):
-        return self.portrayal_method(model)
-
-
-def network_portrayal(env):
-    # The model ensures there is 0 or 1 agent per node
-
-    portrayal = dict()
-    portrayal["nodes"] = [
-        {
-            "id": agent_id,
-            "size": env.get_agent(agent_id).wealth,
-            # "color": "#CC0000" if not agents or agents[0].wealth == 0 else "#007959",
-            "color": "#CC0000",
-            "label": f"{agent_id}: {env.get_agent(agent_id).wealth}",
-        }
-        for (agent_id) in env.G.nodes
-    ]
-
-    portrayal["edges"] = [
-        {"id": edge_id, "source": source, "target": target, "color": "#000000"}
-        for edge_id, (source, target) in enumerate(env.G.edges)
-    ]
-
-
-    return portrayal
-
-
-def gridPortrayal(agent):
-    """
-    This function is registered with the visualization server to be called
-    each tick to indicate how to draw the agent in its current state.
-    :param agent:  the agent in the simulation
-    :return: the portrayal dictionary
-    """
-    color = max(10, min(agent.wealth*10, 100))
-    return {
-        "Shape": "rect",
-        "w": 1,
-        "h": 1,
-        "Filled": "true",
-        "Layer": 0,
-        "Label": agent.unique_id,
-        "Text": agent.unique_id,
-        "x": agent.pos[0],
-        "y": agent.pos[1],
-        "Color": f"rgba(31, 10, 255, 0.{color})"
-    }
-
-
-grid = MyNetwork(network_portrayal, 500, 500, library="sigma")
-chart = ChartModule(
-    [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
-)
-
-model_params = {
-    "N": UserSettableParameter(
-        "slider",
-        "N",
-        5,
-        1,
-        10,
-        1,
-        description="Choose how many agents to include in the model",
-    ),
-    "network_agents": [{"agent_type": SocialMoneyAgent}],
-    "height": UserSettableParameter(
-        "slider",
-        "height",
-        5,
-        5,
-        10,
-        1,
-        description="Grid height",
-        ),
-    "width": UserSettableParameter(
-        "slider",
-        "width",
-        5,
-        5,
-        10,
-        1,
-        description="Grid width",
-        ),
-    "network_params": {
-        'generator': graph_generator
-    },
-}
-
-canvas_element = CanvasGrid(gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500)
-
-
-server = ModularServer(
-    MoneyEnv, [grid, chart, canvas_element], "Money Model", model_params
-)
-server.port = 8521
-
-server.launch(open_browser=False)

examples/mesa/social_wealth.py

@@ -1,120 +0,0 @@
-'''
-This is an example that adds soil agents and environment in a normal
-mesa workflow.
-'''
-from mesa import Agent as MesaAgent
-from mesa.space import MultiGrid
-# from mesa.time import RandomActivation
-from mesa.datacollection import DataCollector
-from mesa.batchrunner import BatchRunner
-
-import networkx as nx
-
-from soil import NetworkAgent, Environment
-
-def compute_gini(model):
-    agent_wealths = [agent.wealth for agent in model.agents]
-    x = sorted(agent_wealths)
-    N = len(list(model.agents))
-    B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
-    return (1 + (1/N) - 2*B)
-
-class MoneyAgent(MesaAgent):
-    """
-    A MESA agent with fixed initial wealth.
-    It will only share wealth with neighbors based on grid proximity
-    """
-
-    def __init__(self, unique_id, model):
-        super().__init__(unique_id=unique_id, model=model)
-        self.wealth = 1
-
-    def move(self):
-        possible_steps = self.model.grid.get_neighborhood(
-            self.pos,
-            moore=True,
-            include_center=False)
-        new_position = self.random.choice(possible_steps)
-        self.model.grid.move_agent(self, new_position)
-
-    def give_money(self):
-        cellmates = self.model.grid.get_cell_list_contents([self.pos])
-        if len(cellmates) > 1:
-            other = self.random.choice(cellmates)
-            other.wealth += 1
-            self.wealth -= 1
-
-    def step(self):
-        self.info("Crying wolf", self.pos)
-        self.move()
-        if self.wealth > 0:
-            self.give_money()
-
-
-class SocialMoneyAgent(NetworkAgent, MoneyAgent):
-    wealth = 1
-
-    def give_money(self):
-        cellmates = set(self.model.grid.get_cell_list_contents([self.pos]))
-        friends = set(self.get_neighboring_agents())
-        self.info("Trying to give money")
-        self.debug("Cellmates: ", cellmates)
-        self.debug("Friends: ", friends)
-
-        nearby_friends = list(cellmates & friends)
-
-        if len(nearby_friends):
-            other = self.random.choice(nearby_friends)
-            other.wealth += 1
-            self.wealth -= 1
-
-
-class MoneyEnv(Environment):
-    """A model with some number of agents."""
-    def __init__(self, N, width, height, *args, network_params, **kwargs):
-
-        network_params['n'] = N
-        super().__init__(*args, network_params=network_params, **kwargs)
-        self.grid = MultiGrid(width, height, False)
-
-        # Create agents
-        for agent in self.agents:
-            x = self.random.randrange(self.grid.width)
-            y = self.random.randrange(self.grid.height)
-            self.grid.place_agent(agent, (x, y))
-
-        self.datacollector = DataCollector(
-            model_reporters={"Gini": compute_gini},
-            agent_reporters={"Wealth": "wealth"})
-
-
-def graph_generator(n=5):
-    G = nx.Graph()
-    for ix in range(n):
-        G.add_edge(0, ix)
-    return G
-
-if __name__ == '__main__':
-
-
-    G = graph_generator()
-    fixed_params = {"topology": G,
-                    "width": 10,
-                    "network_agents": [{"agent_type": SocialMoneyAgent,
-                                       'weight': 1}],
-                    "height": 10}
-
-    variable_params = {"N": range(10, 100, 10)}
-
-    batch_run = BatchRunner(MoneyEnv,
-                            variable_parameters=variable_params,
-                            fixed_parameters=fixed_params,
-                            iterations=5,
-                            max_steps=100,
-                            model_reporters={"Gini": compute_gini})
-    batch_run.run_all()
-
-    run_data = batch_run.get_model_vars_dataframe()
-    run_data.head()
-    print(run_data.Gini)
-

examples/mesa/wealth.py

@@ -1,83 +0,0 @@
-from mesa import Agent, Model
-from mesa.space import MultiGrid
-from mesa.time import RandomActivation
-from mesa.datacollection import DataCollector
-from mesa.batchrunner import BatchRunner
-
-def compute_gini(model):
-    agent_wealths = [agent.wealth for agent in model.schedule.agents]
-    x = sorted(agent_wealths)
-    N = model.num_agents
-    B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
-    return (1 + (1/N) - 2*B)
-
-class MoneyAgent(Agent):
-    """ An agent with fixed initial wealth."""
-    def __init__(self, unique_id, model):
-        super().__init__(unique_id, model)
-        self.wealth = 1
-
-    def move(self):
-        possible_steps = self.model.grid.get_neighborhood(
-            self.pos,
-            moore=True,
-            include_center=False)
-        new_position = self.random.choice(possible_steps)
-        self.model.grid.move_agent(self, new_position)
-
-    def give_money(self):
-        cellmates = self.model.grid.get_cell_list_contents([self.pos])
-        if len(cellmates) > 1:
-            other = self.random.choice(cellmates)
-            other.wealth += 1
-            self.wealth -= 1
-
-    def step(self):
-        self.move()
-        if self.wealth > 0:
-            self.give_money()
-
-class MoneyModel(Model):
-    """A model with some number of agents."""
-    def __init__(self, N, width, height):
-        self.num_agents = N
-        self.grid = MultiGrid(width, height, True)
-        self.schedule = RandomActivation(self)
-        self.running = True
-
-        # Create agents
-        for i in range(self.num_agents):
-            a = MoneyAgent(i, self)
-            self.schedule.add(a)
-            # Add the agent to a random grid cell
-            x = self.random.randrange(self.grid.width)
-            y = self.random.randrange(self.grid.height)
-            self.grid.place_agent(a, (x, y))
-
-        self.datacollector = DataCollector(
-            model_reporters={"Gini": compute_gini},
-            agent_reporters={"Wealth": "wealth"})
-
-    def step(self):
-        self.datacollector.collect(self)
-        self.schedule.step()
-
-
-if __name__ == '__main__':
-
-    fixed_params = {"width": 10,
-                    "height": 10}
-    variable_params = {"N": range(10, 500, 10)}
-
-    batch_run = BatchRunner(MoneyModel,
-                            variable_params,
-                            fixed_params,
-                            iterations=5,
-                            max_steps=100,
-                            model_reporters={"Gini": compute_gini})
-    batch_run.run_all()
-
-    run_data = batch_run.get_model_vars_dataframe()
-    run_data.head()
-    print(run_data.Gini)
-

examples/newsspread/NewsSpread.yml

@@ -1,138 +0,0 @@
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_time: 300
-name: Sim_all_dumb
-network_agents:
-- agent_type: DumbViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_type: DumbViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_time: 300
-name: Sim_half_herd
-network_agents:
-- agent_type: DumbViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_type: DumbViewer
-  state:
-    has_tv: true
-  weight: 1
-- agent_type: HerdViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_time: 300
-name: Sim_all_herd
-network_agents:
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-  prob_neighbor_cure: 0.1
-interval: 1
-max_time: 300
-name: Sim_wise_herd
-network_agents:
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_type: WiseViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-  prob_neighbor_cure: 0.1
-interval: 1
-max_time: 300
-name: Sim_all_wise
-network_agents:
-- agent_type: WiseViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_type: WiseViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50

examples/newsspread/newsspread.py

@@ -1,79 +0,0 @@
-from soil.agents import FSM, state, default_state, prob
-import logging
-
-
-class DumbViewer(FSM):
-    '''
-    A viewer that gets infected via TV (if it has one) and tries to infect
-    its neighbors once it's infected.
-    '''
-    defaults = {
-        'prob_neighbor_spread': 0.5,
-        'prob_tv_spread': 0.1,
-    }
-
-    @default_state
-    @state
-    def neutral(self):
-        if self['has_tv']:
-            if prob(self.env['prob_tv_spread']):
-                self.set_state(self.infected)
-
-    @state
-    def infected(self):
-        for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
-            if prob(self.env['prob_neighbor_spread']):
-                neighbor.infect()
-
-    def infect(self):
-        self.set_state(self.infected)
-
-
-class HerdViewer(DumbViewer):
-    '''
-    A viewer whose probability of infection depends on the state of its neighbors.
-    '''
-
-    def infect(self):
-        infected = self.count_neighboring_agents(state_id=self.infected.id)
-        total = self.count_neighboring_agents()
-        prob_infect = self.env['prob_neighbor_spread'] * infected/total
-        self.debug('prob_infect', prob_infect)
-        if prob(prob_infect):
-            self.set_state(self.infected.id)
-
-
-class WiseViewer(HerdViewer):
-    '''
-    A viewer that can change its mind.
-    '''
-
-    defaults = {
-        'prob_neighbor_spread': 0.5,
-        'prob_neighbor_cure': 0.25,
-        'prob_tv_spread': 0.1,
-    }
-
-    @state
-    def cured(self):
-        prob_cure = self.env['prob_neighbor_cure']
-        for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
-            if prob(prob_cure):
-                try:
-                    neighbor.cure()
-                except AttributeError:
-                    self.debug('Viewer {} cannot be cured'.format(neighbor.id))
-
-    def cure(self):
-        self.set_state(self.cured.id)
-
-    @state
-    def infected(self):
-        cured = max(self.count_neighboring_agents(self.cured.id),
-                    1.0)
-        infected = max(self.count_neighboring_agents(self.infected.id),
-                       1.0)
-        prob_cure = self.env['prob_neighbor_cure'] * (cured/infected)
-        if prob(prob_cure):
-            return self.cure()
-        return self.set_state(super().infected)

examples/programmatic/.gitignore

@@ -1 +0,0 @@
-Programmatic*

examples/programmatic/programmatic.py

@@ -1,38 +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.info('I am running')
-
-
-s = Simulation(name='Programmatic',
-               network_params={'generator': mygenerator},
-               num_trials=1,
-               max_time=100,
-               agent_type=MyAgent,
-               dry_run=True)
-
-
-logging.basicConfig(level=logging.INFO)
-envs = s.run()
-
-s.dump_yaml()
-
-for env in envs:
-    env.dump_csv()

examples/pubcrawl/README.md

@@ -1,10 +0,0 @@
-Simulation of pubs and drinking pals that go from pub to pub.
-
-Th custom environment includes a list of pubs and methods to allow agents to discover and enter pubs.
-There are two types of agents:
-
-* Patron. A patron will do three things, in this order:
-    * Look for other patrons to drink with
-    * Look for a pub where the agent and other agents in the same group can get in.
-    * While in the pub, patrons only drink, until they get drunk and taken home.
-* Police. There is only one police agent that will take any drunk patrons home (kick them out of the pub).

examples/pubcrawl/pubcrawl.py

@@ -1,175 +0,0 @@
-from soil.agents import FSM, state, default_state
-from soil import Environment
-from random import random, shuffle
-from itertools import islice
-import logging
-
-
-class CityPubs(Environment):
-    '''Environment with Pubs'''
-    level = logging.INFO
-
-    def __init__(self, *args, number_of_pubs=3, pub_capacity=10, **kwargs):
-        super(CityPubs, self).__init__(*args, **kwargs)
-        pubs = {}
-        for i in range(number_of_pubs):
-            newpub = {
-                'name': 'The awesome pub #{}'.format(i),
-                'open': True,
-                'capacity': pub_capacity,
-                'occupancy': 0,
-            }
-            pubs[newpub['name']] = newpub
-        self['pubs'] = pubs
-
-    def enter(self, pub_id, *nodes):
-        '''Agents will try to enter. The pub checks if it is possible'''
-        try:
-            pub = self['pubs'][pub_id]
-        except KeyError:
-            raise ValueError('Pub {} is not available'.format(pub_id))
-        if not pub['open'] or (pub['capacity'] < (len(nodes) + pub['occupancy'])):
-            return False
-        pub['occupancy'] += len(nodes)
-        for node in nodes:
-            node['pub'] = pub_id
-        return True
-
-    def available_pubs(self):
-        for pub in self['pubs'].values():
-            if pub['open'] and (pub['occupancy'] < pub['capacity']):
-                yield pub['name']
-
-    def exit(self, pub_id, *node_ids):
-        '''Agents will notify the pub they want to leave'''
-        try:
-            pub = self['pubs'][pub_id]
-        except KeyError:
-            raise ValueError('Pub {} is not available'.format(pub_id))
-        for node_id in node_ids:
-            node = self.get_agent(node_id)
-            if pub_id == node['pub']:
-                del node['pub']
-                pub['occupancy'] -= 1
-
-
-class Patron(FSM):
-    '''Agent that looks for friends to drink with. It will do three things:
-        1) Look for other patrons to drink with
-        2) Look for a bar where the agent and other agents in the same group can get in.
-        3) While in the bar, patrons only drink, until they get drunk and taken home.
-    '''
-    level = logging.DEBUG
-
-    defaults = {
-        'pub': None,
-        'drunk': False,
-        'pints': 0,
-        'max_pints': 3,
-    }
-
-    @default_state
-    @state
-    def looking_for_friends(self):
-        '''Look for friends to drink with'''
-        self.info('I am looking for friends')
-        available_friends = list(self.get_agents(drunk=False,
-                                                 pub=None,
-                                                 state_id=self.looking_for_friends.id))
-        if not available_friends:
-            self.info('Life sucks and I\'m alone!')
-            return self.at_home
-        befriended = self.try_friends(available_friends)
-        if befriended:
-            return self.looking_for_pub
-
-    @state
-    def looking_for_pub(self):
-        '''Look for a pub that accepts me and my friends'''
-        if self['pub'] != None:
-            return self.sober_in_pub
-        self.debug('I am looking for a pub')
-        group = list(self.get_neighboring_agents())
-        for pub in self.env.available_pubs():
-            self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group)))
-            if self.env.enter(pub, self, *group):
-                self.info('We\'re all {} getting in {}!'.format(len(group), pub))
-                return self.sober_in_pub
-
-    @state
-    def sober_in_pub(self):
-        '''Drink up.'''
-        self.drink()
-        if self['pints'] > self['max_pints']:
-            return self.drunk_in_pub
-
-    @state
-    def drunk_in_pub(self):
-        '''I'm out. Take me home!'''
-        self.info('I\'m so drunk. Take me home!')
-        self['drunk'] = True
-        pass  # out drunk
-
-    @state
-    def at_home(self):
-        '''The end'''
-        others = self.get_agents(state_id=Patron.at_home.id, limit_neighbors=True)
-        self.debug('I\'m home. Just like {} of my friends'.format(len(others)))
-    
-    def drink(self):
-        self['pints'] += 1
-        self.debug('Cheers to that')
-    
-    def kick_out(self):
-        self.set_state(self.at_home)
-
-    def befriend(self, other_agent, force=False):
-        '''
-        Try to become friends with another agent. The chances of
-        success depend on both agents' openness.
-        '''
-        if force or self['openness'] > random():
-            self.env.add_edge(self, other_agent)
-            self.info('Made some friend {}'.format(other_agent))
-            return True
-        return False
-
-    def try_friends(self, others):
-        ''' Look for random agents around me and try to befriend them'''
-        befriended = False
-        k = int(10*self['openness'])
-        shuffle(others)
-        for friend in islice(others, k):  # random.choice >= 3.7
-            if friend == self:
-                continue
-            if friend.befriend(self):
-                self.befriend(friend, force=True)
-                self.debug('Hooray! new friend: {}'.format(friend.id))
-                befriended = True
-            else:
-                self.debug('{} does not want to be friends'.format(friend.id))
-        return befriended
-
-
-class Police(FSM):
-    '''Simple agent to take drunk people out of pubs.'''
-    level = logging.INFO
-
-    @default_state
-    @state
-    def patrol(self):
-        drunksters = list(self.get_agents(drunk=True,
-                                          state_id=Patron.drunk_in_pub.id))
-        for drunk in drunksters:
-            self.info('Kicking out the trash: {}'.format(drunk.id))
-            drunk.kick_out()
-        else:
-            self.info('No trash to take out. Too bad.')
-
-
-if __name__ == '__main__':
-    from soil import simulation
-    simulation.run_from_config('pubcrawl.yml',
-                               dry_run=True,
-                               dump=None,
-                               parallel=False)

examples/pubcrawl/pubcrawl.yml

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

examples/rabbits/rabbit_agents.py

@@ -1,122 +0,0 @@
-from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
-from enum import Enum
-from random import random, choice
-import logging
-import math
-
-
-class Genders(Enum):
-    male = 'male'
-    female = 'female'
-
-
-class RabbitModel(FSM):
-
-    level = logging.INFO
-
-    defaults = {
-        'age': 0,
-        'gender': Genders.male.value,
-        'mating_prob': 0.001,
-        'offspring': 0,
-    }
-
-    sexual_maturity = 3 #4*30
-    life_expectancy = 365 * 3
-    gestation = 33
-    pregnancy = -1
-    max_females = 5
-
-    @default_state
-    @state
-    def newborn(self):
-        self.debug(f'I am a newborn at age {self["age"]}')
-        self['age'] += 1
-
-        if self['age'] >= self.sexual_maturity:
-            self.debug('I am fertile!')
-            return self.fertile
-
-    @state
-    def fertile(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        if self['gender'] == Genders.female.value:
-            return
-
-        # Males try to mate
-        for f in self.get_agents(state_id=self.fertile.id, gender=Genders.female.value, limit_neighbors=False, limit=self.max_females):
-            r = random()
-            if r < self['mating_prob']:
-                self.impregnate(f)
-                break # Take a break
-
-    def impregnate(self, whom):
-        if self['gender'] == Genders.female.value:
-            raise NotImplementedError('Females cannot impregnate')
-        whom['pregnancy'] = 0
-        whom['mate'] = self.id
-        whom.set_state(whom.pregnant)
-        self.debug('{} impregnating: {}. {}'.format(self.id, whom.id, whom.state))
-
-    @state
-    def pregnant(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        self['pregnancy'] += 1
-        self.debug('Pregnancy: {}'.format(self['pregnancy']))
-        if self['pregnancy'] >= self.gestation:
-            number_of_babies = int(8+4*random())
-            self.info('Having {} babies'.format(number_of_babies))
-            for i in range(number_of_babies):
-                state = {}
-                state['gender'] = choice(list(Genders)).value
-                child = self.env.add_node(self.__class__, state)
-                self.env.add_edge(self.id, child.id)
-                self.env.add_edge(self['mate'], child.id)
-                # self.add_edge()
-                self.debug('A BABY IS COMING TO LIFE')
-                self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.topology.number_of_nodes())+1
-                self.debug('Rabbits alive: {}'.format(self.env['rabbits_alive']))
-                self['offspring'] += 1
-                self.env.get_agent(self['mate'])['offspring'] += 1
-            del self['mate']
-            self['pregnancy'] = -1
-            return self.fertile
-
-    @state
-    def dead(self):
-        self.info('Agent {} is dying'.format(self.id))
-        if 'pregnancy' in self and self['pregnancy'] > -1:
-            self.info('A mother has died carrying a baby!!')
-        self.die()
-        return
-
-
-class RandomAccident(NetworkAgent):
-
-    level = logging.DEBUG
-
-    def step(self):
-        rabbits_total = self.topology.number_of_nodes()
-        if 'rabbits_alive' not in self.env:
-            self.env['rabbits_alive'] = 0
-        rabbits_alive = self.env.get('rabbits_alive', rabbits_total)
-        prob_death = self.env.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
-        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
-        for i in self.env.network_agents:
-            if i.state['id'] == i.dead.id:
-                continue
-            r = random()
-            if r < prob_death:
-                self.debug('I killed a rabbit: {}'.format(i.id))
-                rabbits_alive = self.env['rabbits_alive'] = rabbits_alive -1
-                self.log('Rabbits alive: {}'.format(self.env['rabbits_alive']))
-                i.set_state(i.dead)
-        self.log('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
-        if self.count_agents(state_id=RabbitModel.dead.id) == self.topology.number_of_nodes():
-            self.die()

examples/rabbits/rabbits.yml

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

examples/random_delays/random_delays.py

@@ -1,45 +0,0 @@
-'''
-Example of setting a 
-Example of a fully programmatic simulation, without definition files.
-'''
-from soil import Simulation, agents
-from soil.time import Delta
-from random import expovariate
-import logging
-
-
-
-class MyAgent(agents.FSM):
-    '''
-    An agent that first does a ping
-    '''
-
-    defaults = {'pong_counts': 2}
-
-    @agents.default_state
-    @agents.state
-    def ping(self):
-        self.info('Ping')
-        return self.pong, Delta(expovariate(1/16))
-
-    @agents.state
-    def pong(self):
-        self.info('Pong')
-        self.pong_counts -= 1
-        self.info(str(self.pong_counts))
-        if self.pong_counts < 1:
-            return self.die()
-        return None, Delta(expovariate(1/16))
-
-
-s = Simulation(name='Programmatic',
-               network_agents=[{'agent_type': MyAgent, 'id': 0}],
-               topology={'nodes': [{'id': 0}], 'links': []},
-               num_trials=1,
-               max_time=100,
-               agent_type=MyAgent,
-               dry_run=True)
-
-
-logging.basicConfig(level=logging.INFO)
-envs = s.run()

examples/template.yml

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

examples/terrorism/TerroristNetworkModel.py

@@ -1,208 +0,0 @@
-import random
-import networkx as nx
-from soil.agents import Geo, NetworkAgent, FSM, state, default_state
-from soil import Environment
-
-
-class TerroristSpreadModel(FSM, Geo):
-    """
-    Settings:
-        information_spread_intensity
-
-        terrorist_additional_influence
-
-        min_vulnerability (optional else zero)
-
-        max_vulnerability
-
-        prob_interaction
-    """
-
-    def __init__(self, model=None, unique_id=0, state=()):
-        super().__init__(model=model, unique_id=unique_id, state=state)
-
-        self.information_spread_intensity = model.environment_params['information_spread_intensity']
-        self.terrorist_additional_influence = model.environment_params['terrorist_additional_influence']
-        self.prob_interaction = model.environment_params['prob_interaction']
-
-        if self['id'] == self.civilian.id:       # Civilian
-            self.mean_belief = random.uniform(0.00, 0.5)
-        elif self['id'] == self.terrorist.id:     # Terrorist
-            self.mean_belief = random.uniform(0.8, 1.00)
-        elif self['id'] == self.leader.id:     # Leader
-            self.mean_belief = 1.00
-        else:
-            raise Exception('Invalid state id: {}'.format(self['id']))
-
-        if 'min_vulnerability' in model.environment_params:
-            self.vulnerability = random.uniform( model.environment_params['min_vulnerability'], model.environment_params['max_vulnerability'] )
-        else :
-            self.vulnerability = random.uniform( 0, model.environment_params['max_vulnerability'] )
-
-
-    @state
-    def civilian(self):
-        neighbours = list(self.get_neighboring_agents(agent_type=TerroristSpreadModel))
-        if len(neighbours) > 0:
-            # Only interact with some of the neighbors
-            interactions = list(n for n in neighbours if random.random() <= self.prob_interaction)
-            influence = sum( self.degree(i) for i in interactions )
-            mean_belief = sum( i.mean_belief * self.degree(i) / influence for i in interactions )
-            mean_belief = mean_belief * self.information_spread_intensity + self.mean_belief * ( 1 - self.information_spread_intensity )
-            self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
-        
-        if self.mean_belief >= 0.8:
-            return self.terrorist
-
-    @state
-    def leader(self):
-        self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
-        for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
-            if self.betweenness(neighbour) > self.betweenness(self):
-                return self.terrorist
-
-    @state
-    def terrorist(self):
-        neighbours = self.get_agents(state_id=[self.terrorist.id, self.leader.id],
-                                     agent_type=TerroristSpreadModel,
-                                     limit_neighbors=True)
-        if len(neighbours) > 0:
-            influence = sum( self.degree(n) for n in neighbours )
-            mean_belief = sum( n.mean_belief * self.degree(n) / influence for n in neighbours )
-            mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
-            self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
-
-        # Check if there are any leaders in the group
-        leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
-        if not leaders:
-            # Check if this is the potential leader
-            # Stop once it's found. Otherwise, set self as leader
-            for neighbour in neighbours:
-                if self.betweenness(self) < self.betweenness(neighbour):
-                    return
-            return self.leader
-
-
-class TrainingAreaModel(FSM, Geo):
-    """
-    Settings:
-        training_influence
-
-        min_vulnerability
-
-    Requires TerroristSpreadModel.
-    """
-
-    def __init__(self, model=None, unique_id=0, state=()):
-        super().__init__(model=model, unique_id=unique_id, state=state)
-        self.training_influence = model.environment_params['training_influence']
-        if 'min_vulnerability' in model.environment_params:
-            self.min_vulnerability = model.environment_params['min_vulnerability']
-        else: self.min_vulnerability = 0
-
-    @default_state
-    @state
-    def terrorist(self):
-        for neighbour in self.get_neighboring_agents(agent_type=TerroristSpreadModel):
-            if neighbour.vulnerability > self.min_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )        
-
-
-class HavenModel(FSM, Geo):
-    """
-    Settings:
-        haven_influence
-
-        min_vulnerability
-
-        max_vulnerability
-
-    Requires TerroristSpreadModel.
-    """
-
-    def __init__(self, model=None, unique_id=0, state=()):
-        super().__init__(model=model, unique_id=unique_id, state=state)
-        self.haven_influence = model.environment_params['haven_influence']
-        if 'min_vulnerability' in model.environment_params:
-            self.min_vulnerability = model.environment_params['min_vulnerability']
-        else: self.min_vulnerability = 0
-        self.max_vulnerability = model.environment_params['max_vulnerability']
-
-    def get_occupants(self, **kwargs):
-        return self.get_neighboring_agents(agent_type=TerroristSpreadModel, **kwargs)
-
-    @state
-    def civilian(self):
-        civilians = self.get_occupants(state_id=self.civilian.id)
-        if not civilians:
-            return self.terrorist
-
-        for neighbour in self.get_occupants():
-            if neighbour.vulnerability > self.min_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
-        return self.civilian
-
-    @state
-    def terrorist(self):
-        for neighbour in self.get_occupants():
-            if neighbour.vulnerability < self.max_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
-        return self.terrorist
-
-
-class TerroristNetworkModel(TerroristSpreadModel):
-    """
-    Settings:
-        sphere_influence
-
-        vision_range
-
-        weight_social_distance
-
-        weight_link_distance
-    """
-
-    def __init__(self, model=None, unique_id=0, state=()):
-        super().__init__(model=model, unique_id=unique_id, state=state)
-
-        self.vision_range = model.environment_params['vision_range']
-        self.sphere_influence = model.environment_params['sphere_influence']
-        self.weight_social_distance = model.environment_params['weight_social_distance']
-        self.weight_link_distance = model.environment_params['weight_link_distance']
-
-    @state
-    def terrorist(self):
-        self.update_relationships()
-        return super().terrorist()
-
-    @state
-    def leader(self):
-        self.update_relationships()
-        return super().leader()
-
-    def update_relationships(self):
-        if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
-            close_ups = set(self.geo_search(radius=self.vision_range, agent_type=TerroristNetworkModel))
-            step_neighbours = set(self.ego_search(self.sphere_influence, agent_type=TerroristNetworkModel, center=False))
-            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_type=TerroristNetworkModel))
-            search = (close_ups | step_neighbours) - neighbours
-            for agent in self.get_agents(search):
-                social_distance = 1 / self.shortest_path_length(agent.id)
-                spatial_proximity = ( 1 - self.get_distance(agent.id) )
-                prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
-                if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
-                    self.add_edge(agent)
-                    break
-
-    def get_distance(self, target):
-        source_x, source_y = nx.get_node_attributes(self.topology, 'pos')[self.id]
-        target_x, target_y = nx.get_node_attributes(self.topology, 'pos')[target]
-        dx = abs( source_x - target_x )
-        dy = abs( source_y - target_y )
-        return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
-
-    def shortest_path_length(self, target):
-        try:
-            return nx.shortest_path_length(self.topology, self.id, target)
-        except nx.NetworkXNoPath:
-            return float('inf')

examples/terrorism/TerroristNetworkModel.yml

@@ -1,63 +0,0 @@
-name: TerroristNetworkModel_sim
-load_module: TerroristNetworkModel
-max_time: 150 
-num_trials: 1
-network_params:
-  generator: random_geometric_graph
-  radius: 0.2
-  # generator: geographical_threshold_graph
-  # theta: 20
-  n: 100
-network_agents:
-  - agent_type: TerroristNetworkModel
-    weight: 0.8
-    state:
-      id: civilian  # Civilians
-  - agent_type: TerroristNetworkModel
-    weight: 0.1
-    state:
-      id: leader    # Leaders
-  - agent_type: TrainingAreaModel
-    weight: 0.05
-    state:
-      id: terrorist # Terrorism
-  - agent_type: HavenModel
-    weight: 0.05
-    state:
-      id: civilian  # Civilian
-
-environment_params:
-  # TerroristSpreadModel
-  information_spread_intensity: 0.7
-  terrorist_additional_influence: 0.035
-  max_vulnerability: 0.7
-  prob_interaction: 0.5
-
-  # TrainingAreaModel and HavenModel
-  training_influence: 0.20
-  haven_influence: 0.20
-
-  # TerroristNetworkModel
-  vision_range: 0.30
-  sphere_influence: 2
-  weight_social_distance: 0.035
-  weight_link_distance: 0.035
-
-visualization_params:
-  # Icons downloaded from https://www.iconfinder.com/
-  shape_property: agent
-  shapes:
-    TrainingAreaModel: target
-    HavenModel: home
-    TerroristNetworkModel: person
-  colors:
-    - attr_id: civilian
-      color: '#40de40'
-    - attr_id: terrorist
-      color: red
-    - attr_id: leader
-      color: '#c16a6a'
-  background_image: 'map_4800x2860.jpg'
-  background_opacity: '0.9'
-  background_filter_color: 'blue'
-skip_test: true  # This simulation takes too long for automated tests.

examples/torvalds.edgelist

@@ -1,2 +0,0 @@
-balkian Torvalds {}
-anonymous Torvalds {}

examples/torvalds.yml

@@ -1,14 +0,0 @@
----
-name: torvalds_example
-max_time: 10
-interval: 2
-agent_type: CounterModel
-default_state:
-  skill_level: 'beginner'
-network_params:
-  path: 'torvalds.edgelist'
-states:
-  Torvalds:
-    skill_level: 'God'
-  balkian:
-    skill_level: 'developer'

models/BaseBehaviour/BaseBehaviour.py

@@ -0,0 +1,38 @@
+import settings
+from nxsim import BaseNetworkAgent
+from .. import networkStatus
+
+
+class BaseBehaviour(BaseNetworkAgent):
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self._attrs = {}
+
+    @property
+    def attrs(self):
+        now = self.env.now
+        if now not in self._attrs:
+            self._attrs[now] = {}
+        return self._attrs[now]
+
+    @attrs.setter
+    def attrs(self, value):
+        self._attrs[self.env.now] = value
+
+    def run(self):
+        while True:
+            self.step(self.env.now)
+            yield self.env.timeout(settings.network_params["timeout"])
+
+    def step(self, now):
+        networkStatus['agent_%s'% self.id] = self.to_json()
+
+    def to_json(self):
+        final = {}
+        for stamp, attrs in self._attrs.items():
+            for a in attrs:
+                if a not in final:
+                    final[a] = {}
+                final[a][stamp] = attrs[a]
+        return final

models/BaseBehaviour/__init__.py

@@ -0,0 +1 @@
+from .BaseBehaviour import BaseBehaviour

models/TerroristModel/TerroristModel.py

@@ -0,0 +1,367 @@
+import random
+import numpy as np
+from ..BaseBehaviour import *
+import settings
+import networkx as nx
+
+
+
+POPULATION = 0
+LEADERS = 1
+HAVEN = 2
+TRAININGENV = 3
+
+NON_RADICAL = 0
+NEUTRAL = 1
+RADICAL = 2
+
+POPNON =0
+POPNE=1
+POPRAD=2
+
+HAVNON=3
+HAVNE=4
+HAVRAD=5
+
+LEADER=6
+
+TRAINING = 7
+
+
+class TerroristModel(BaseBehaviour):
+    num_agents = 0
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+
+        self.population = settings.network_params["number_of_nodes"] * settings.environment_params['initial_population']
+        self.havens = settings.network_params["number_of_nodes"] * settings.environment_params['initial_havens']
+        self.training_enviroments = settings.network_params["number_of_nodes"] * settings.environment_params['initial_training_enviroments']
+
+        self.initial_radicalism = settings.environment_params['initial_radicalism']
+        self.information_spread_intensity = settings.environment_params['information_spread_intensity']
+        self.influence = settings.environment_params['influence']
+        self.relative_inequality = settings.environment_params['relative_inequality']
+        self.additional_influence = settings.environment_params['additional_influence']
+
+        if TerroristModel.num_agents < self.population:
+            self.state['type'] = POPULATION
+            TerroristModel.num_agents = TerroristModel.num_agents + 1
+            random1 = random.random()
+            if random1 < 0.7:
+                self.state['id'] = NON_RADICAL
+                self.state['fstatus'] = POPNON
+            elif random1 >= 0.7 and random1 < 0.9:
+                self.state['id'] = NEUTRAL
+                self.state['fstatus'] = POPNE
+            elif random1 >= 0.9:
+                self.state['id'] = RADICAL
+                self.state['fstatus'] = POPRAD
+
+        elif TerroristModel.num_agents < self.havens + self.population:
+            self.state['type'] = HAVEN
+            TerroristModel.num_agents = TerroristModel.num_agents + 1
+            random2 = random.random()
+            random1 = random2 + self.initial_radicalism
+            if random1 < 1.2:
+                self.state['id'] = NON_RADICAL
+                self.state['fstatus'] = HAVNON
+            elif random1 >= 1.2 and random1 < 1.6:
+                self.state['id'] = NEUTRAL
+                self.state['fstatus'] = HAVNE
+            elif random1 >= 1.6:
+                self.state['id'] = RADICAL
+                self.state['fstatus'] = HAVRAD
+
+        elif TerroristModel.num_agents < self.training_enviroments + self.havens + self.population:
+            self.state['type'] = TRAININGENV
+            self.state['fstatus'] = TRAINING
+            TerroristModel.num_agents = TerroristModel.num_agents + 1
+
+    def step(self, now):
+        if self.state['type'] == POPULATION:
+            self.population_and_leader_conduct()
+        if self.state['type'] == LEADERS:
+            self.population_and_leader_conduct()
+        if self.state['type'] == HAVEN:
+            self.haven_conduct()
+        if self.state['type'] == TRAININGENV:
+            self.training_enviroment_conduct()
+
+        self.attrs['status'] = self.state['id']
+        self.attrs['type'] = self.state['type']
+        self.attrs['radicalism'] = self.state['rad']
+        self.attrs['fstatus'] = self.state['fstatus']
+        super().step(now)
+
+    def population_and_leader_conduct(self):
+        if self.state['id'] == NON_RADICAL:
+            if self.state['rad'] == 0.000:
+                self.state['rad'] = self.set_radicalism()
+            self.non_radical_behaviour()
+        if self.state['id'] == NEUTRAL:
+            if self.state['rad'] == 0.000:
+                self.state['rad'] = self.set_radicalism()
+            while self.state['id'] == RADICAL:
+                self.radical_behaviour()
+                break
+            self.neutral_behaviour()
+        if self.state['id'] == RADICAL:
+            if self.state['rad'] == 0.000:
+                self.state['rad'] = self.set_radicalism()
+            self.radical_behaviour()
+
+    def haven_conduct(self):
+        non_radical_neighbors = self.get_neighboring_agents(state_id=NON_RADICAL)
+        neutral_neighbors = self.get_neighboring_agents(state_id=NEUTRAL)
+        radical_neighbors = self.get_neighboring_agents(state_id=RADICAL)
+
+        neighbors_of_non_radical = len(neutral_neighbors) + len(radical_neighbors)
+        neighbors_of_neutral = len(non_radical_neighbors) + len(radical_neighbors)
+        neighbors_of_radical = len(non_radical_neighbors) + len(neutral_neighbors)
+        threshold = 8
+        if (len(non_radical_neighbors) > neighbors_of_non_radical) and len(non_radical_neighbors) >= threshold:
+            self.state['id'] = NON_RADICAL
+        elif (len(neutral_neighbors) > neighbors_of_neutral) and len(neutral_neighbors) >= threshold:
+            self.state['id'] = NEUTRAL
+        elif (len(radical_neighbors) > neighbors_of_radical) and len(radical_neighbors) >= threshold:
+            self.state['id'] = RADICAL
+
+        if self.state['id'] == NEUTRAL:
+            for neighbor in non_radical_neighbors:
+                neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
+                if neighbor.state['rad'] >= 0.3 and neighbor.state['rad'] <= 0.59:
+                    neighbor.state['id'] = NEUTRAL
+                    if neighbor.state['type'] == POPULATION:
+                        neighbor.state['fstatus'] = POPNE
+                    elif neighbor.state['type'] == HAVEN:
+                        neighbor.state['fstatus'] = HAVNE
+                elif neighbor.state['rad'] > 0.59:
+                    neighbor.state['rad'] = 0.59
+                    neighbor.state['id'] = NEUTRAL
+                    if neighbor.state['type'] == POPULATION:
+                        neighbor.state['fstatus'] = POPNE
+                    elif neighbor.state['type'] == HAVEN:
+                        neighbor.state['fstatus'] = HAVNE
+
+        if self.state['id'] == RADICAL:
+
+            for neighbor in non_radical_neighbors:
+                neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
+                if neighbor.state['rad'] >= 0.3 and neighbor.state['rad'] <= 0.59:
+                    neighbor.state['id'] = NEUTRAL
+                    if neighbor.state['type'] == POPULATION:
+                        neighbor.state['fstatus'] = POPNE
+                    elif neighbor.state['type'] == HAVEN:
+                        neighbor.state['fstatus'] = HAVNE
+                elif neighbor.state['rad'] > 0.59:
+                    neighbor.state['rad'] = 0.59
+                    neighbor.state['id'] = NEUTRAL
+                    if neighbor.state['type'] == POPULATION:
+                        neighbor.state['fstatus'] = POPNE
+                    elif neighbor.state['type'] == HAVEN:
+                        neighbor.state['fstatus'] = HAVNE
+
+            for neighbor in neutral_neighbors:
+                neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
+                if neighbor.state['rad'] >= 0.6:
+                    neighbor.state['id'] = RADICAL
+                    if neighbor.state['type'] != HAVEN and neighbor.state['type']!=TRAININGENV:
+                        if neighbor.state['rad'] >= 0.62:
+                            if create_leader(neighbor):
+                                neighbor.state['type'] = LEADERS
+                                neighbor.state['fstatus'] = LEADER
+                            # elif neighbor.state['type'] == LEADERS:
+                            #     neighbor.state['type'] = POPULATION
+                            #     neighbor.state['fstatus'] = POPRAD
+                            elif neighbor.state['type'] == POPULATION:
+                                neighbor.state['fstatus'] = POPRAD
+                    elif neighbor.state['type'] == HAVEN:
+                        neighbor.state['fstatus'] = HAVRAD
+
+    def training_enviroment_conduct(self):
+        self.state['id'] = RADICAL
+        self.state['rad'] = 1
+        neighbors = self.get_neighboring_agents()
+        for neighbor in neighbors:
+            if neighbor.state['id'] == NON_RADICAL:
+                neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
+                if neighbor.state['rad'] >= 0.3 and self.state['rad'] <= 0.59:
+                    neighbor.state['id'] = NEUTRAL
+                    if neighbor.state['type'] == POPULATION:
+                        neighbor.state['fstatus'] = POPNE
+                    elif neighbor.state['type'] == HAVEN:
+                        neighbor.state['fstatus'] = HAVNE
+                elif neighbor.state['rad'] > 0.59:
+                    neighbor.state['rad'] = 0.59
+                    neighbor.state['id'] = NEUTRAL
+                    if neighbor.state['type'] == POPULATION:
+                        neighbor.state['fstatus'] = POPNE
+                    elif neighbor.state['type'] == HAVEN:
+                        neighbor.state['fstatus'] = HAVNE
+
+
+            neighbor.state['rad'] = neighbor.state['rad'] + (neighbor.influence + neighbor.additional_influence) * neighbor.information_spread_intensity
+            if neighbor.state['rad'] >= 0.3 and neighbor.state['rad'] <= 0.59:
+                neighbor.state['id'] = NEUTRAL
+                if neighbor.state['type'] == POPULATION:
+                    neighbor.state['fstatus'] = POPNE
+                elif neighbor.state['type'] == HAVEN:
+                    neighbor.state['fstatus'] = HAVNE
+            elif neighbor.state['rad'] >= 0.6:
+                neighbor.state['id'] = RADICAL
+                if neighbor.state['type'] != HAVEN and neighbor.state['type'] != TRAININGENV:
+                    if neighbor.state['rad'] >= 0.62:
+                        if create_leader(neighbor):
+                            neighbor.state['type'] = LEADERS
+                            neighbor.state['fstatus'] = LEADER
+                        # elif neighbor.state['type'] == LEADERS:
+                        #     neighbor.state['type'] = POPULATION
+                        #     neighbor.state['fstatus'] = POPRAD
+                        elif neighbor.state['type'] == POPULATION:
+                            neighbor.state['fstatus'] = POPRAD
+                elif neighbor.state['type'] == HAVEN:
+                    neighbor.state['fstatus'] = HAVRAD
+
+    def non_radical_behaviour(self):
+        neighbors = self.get_neighboring_agents()
+
+        for neighbor in neighbors:
+            if neighbor.state['type'] == POPULATION:
+                if neighbor.state['id'] == NEUTRAL or neighbor.state['id'] == RADICAL:
+                    self.state['rad'] = self.state['rad'] + self.influence * self.information_spread_intensity
+                    if self.state['rad'] >= 0.3 and self.state['rad'] <= 0.59:
+                        self.state['id'] = NEUTRAL
+
+                        if self.state['type']==POPULATION:
+                            self.state['fstatus'] = POPNE
+                        elif self.state['type'] == HAVEN:
+                            self.state['fstatus'] = HAVNE
+                    elif self.state['rad'] > 0.59:
+                        self.state['rad'] = 0.59
+                        self.state['id'] = NEUTRAL
+                        if self.state['type']==POPULATION:
+                            self.state['fstatus'] = POPNE
+                        elif self.state['type'] == HAVEN:
+                            self.state['fstatus'] = HAVNE
+
+            elif neighbor.state['type'] == LEADERS:
+
+                if neighbor.state['id'] == NEUTRAL or neighbor.state['id'] == RADICAL:
+                    self.state['rad'] = self.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
+                    if self.state['rad'] >= 0.3 and self.state['rad'] <= 0.59:
+                        self.state['id'] = NEUTRAL
+                        if self.state['type']==POPULATION:
+                            self.state['fstatus'] = POPNE
+                        elif self.state['type'] == HAVEN:
+                            self.state['fstatus'] = HAVNE
+                    elif self.state['rad'] > 0.59:
+                        self.state['rad'] = 0.59
+                        self.state['id'] = NEUTRAL
+                        if self.state['type']==POPULATION:
+                            self.state['fstatus'] = POPNE
+                        elif self.state['type'] == HAVEN:
+                            self.state['fstatus'] = HAVNE
+
+
+    def neutral_behaviour(self):
+        neighbors = self.get_neighboring_agents()
+        for neighbor in neighbors:
+            if neighbor.state['type'] == POPULATION:
+                if neighbor.state['id'] == RADICAL:
+                    self.state['rad'] = self.state['rad'] + self.influence * self.information_spread_intensity
+                    if self.state['rad'] >= 0.6:
+                        self.state['id'] = RADICAL
+                        if self.state['type'] != HAVEN:
+                            if self.state['rad'] >= 0.62:
+                                if create_leader(self):
+                                    self.state['type'] = LEADERS
+
+                                    self.state['fstatus'] = LEADER
+                                # elif self.state['type'] == LEADERS:
+                                #     self.state['type'] = POPULATION
+                                #     self.state['fstatus'] = POPRAD
+                                elif neighbor.state['type'] == POPULATION:
+                                    self.state['fstatus'] = POPRAD
+                        elif self.state['type'] == HAVEN:
+                            self.state['fstatus'] = HAVRAD
+
+
+            elif neighbor.state['type'] == LEADERS:
+                if neighbor.state['id'] == RADICAL:
+                    self.state['rad'] = self.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
+                    if self.state['rad'] >= 0.6:
+                        self.state['id'] = RADICAL
+                        if self.state['type'] != HAVEN:
+                            if self.state['rad'] >= 0.62:
+                                if create_leader(self):
+                                    self.state['type'] = LEADERS
+                                    self.state['fstatus'] = LEADER
+                                # elif self.state['type'] == LEADERS:
+                                #     self.state['type'] = POPULATION
+                                #     self.state['fstatus'] = POPRAD
+                                elif neighbor.state['type'] == POPULATION:
+                                    self.state['fstatus'] = POPRAD
+                        elif self.state['type'] == HAVEN:
+                            self.state['fstatus'] = HAVRAD
+
+
+
+
+
+    def radical_behaviour(self):
+        neighbors = self.get_neighboring_agents(state_id=RADICAL)
+
+        for neighbor in neighbors:
+            if self.state['rad']< neighbor.state['rad'] and self.state['type']== LEADERS and neighbor.state['type']==LEADERS:
+                self.state['type'] = POPULATION
+                self.state['fstatus'] = POPRAD
+
+
+    def set_radicalism(self):
+        if self.state['id'] == NON_RADICAL:
+            radicalism = random.uniform(0.0, 0.29) * self.relative_inequality
+            return radicalism
+        elif self.state['id'] == NEUTRAL:
+            radicalism = 0.3 + random.uniform(0.3, 0.59) * self.relative_inequality
+            if radicalism >= 0.6:
+                self.state['id'] = RADICAL
+            return radicalism
+        elif self.state['id'] == RADICAL:
+            radicalism = 0.6 + random.uniform(0.6, 1.0) * self.relative_inequality
+            return radicalism
+
+def get_partition(agent):
+    return settings.partition_param[agent.id]
+
+def get_centrality(agent):
+    return settings.centrality_param[agent.id]
+def get_centrality_given_id(id):
+    return settings.centrality_param[id]
+
+def get_leader(partition):
+    if not bool(settings.leaders) or partition not in settings.leaders.keys():
+        return None
+    return settings.leaders[partition]
+
+def set_leader(partition, agent):
+    settings.leaders[partition] = agent.id
+
+def create_leader(agent):
+    my_partition = get_partition(agent)
+    old_leader = get_leader(my_partition)
+
+    if old_leader == None:
+        set_leader(my_partition, agent)
+        return True
+    else:
+        my_centrality = get_centrality(agent)
+        old_leader_centrality = get_centrality_given_id(old_leader)
+        if my_centrality > old_leader_centrality:
+            set_leader(my_partition, agent)
+            return True
+        return False
+
+
+

models/TerroristModel/__init__.py

@@ -0,0 +1 @@
+from .TerroristModel import TerroristModel

models/__init__.py

@@ -0,0 +1,3 @@
+from .models import *
+from .BaseBehaviour import *
+from .TerroristModel import *

models/models.py

@@ -0,0 +1,7 @@
+import settings
+
+networkStatus = {}  # Dict that will contain the status of every agent in the network
+
+# Initialize agent states. Let's assume everyone is normal and all types are population.
+init_states = [{'id': 0, 'type': 0, 'rad': 0, 'fstatus':0,  } for _ in range(settings.network_params["number_of_nodes"])]
+

requirements.txt

@@ -1,9 +1,5 @@
-networkx>=2.5
+nxsim
+simpy
+networkx
 numpy
-matplotlib
-pyyaml>=5.1
-pandas>=0.23
-SALib>=1.3
-Jinja2
-Mesa>=0.8
-tsih>=0.1.5
+matplotlib

settings.json

@@ -0,0 +1,23 @@
+[
+  {
+    "network_type": 0,
+    "number_of_nodes": 80,
+    "max_time": 50,
+    "num_trials": 1,
+    "timeout": 2
+  },
+
+  {
+    "agent": ["TerroristModel"],
+
+    "initial_population": 0.85,
+    "initial_havens": 0.1,
+    "initial_training_enviroments": 0.05,
+
+    "initial_radicalism": 0.12,
+    "relative_inequality": 0.33,
+    "information_spread_intensity": 0.1,
+    "influence": 0.4,
+    "additional_influence": 0.1
+  }
+]

settings.py

@@ -0,0 +1,13 @@
+# General configuration
+import json
+
+with open('settings.json', 'r') as f:
+    settings = json.load(f)
+
+network_params = settings[0]
+environment_params = settings[1]
+
+centrality_param = {}
+partition_param={}
+leaders={}
+