Delete TerroristModel_tipo.png

.dockerignore

@@ -1,2 +0,0 @@
-**/soil_output
-.*

.gitlab-ci.yml

@@ -1,25 +0,0 @@
-stages:
-  - build
-  - test
-
-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
-    - /kaniko/executor --context $CI_PROJECT_DIR --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
-  only:
-    - tags
-
-
-test:
-  tags:
-    - docker
-  image: python:3.7
-  stage: test
-  script:
-    - python setup.py test

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

@@ -1,4 +0,0 @@
-include requirements.txt
-include test-requirements.txt
-include README.rst
-graft soil

Makefile

@@ -1,4 +0,0 @@
-test:
-	docker-compose exec dev python -m pytest -s -v
-
-.PHONY: test

README.md

@@ -1,34 +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).
+The purpose of Soil (SOcial network sImuLator) is provding an Agent-based Social Simulator written in Python for Social Networks.
+
+
+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).
 
-Follow our [tutorial](examples/tutorial/soil_tutorial.ipynb) to develop your own agent models.
 
-If you use Soil in your research, don't forget to cite this paper:
 
-```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",
-}
 
-```
 
 @Copyright GSI - Universidad Politécnica de Madrid 2017
-
-[![SOIL](logo_gsi.png)](https://www.gsi.dit.upm.es)
-

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/configuration.rst

@@ -1,244 +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``).
-
-.. code:: yaml
-          
-    ---
-    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
-
-
-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.

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 using soil programmatically:
-
-.. code:: python
-
-   import soil
-   print(soil.__version__)
-
-The latest version can be installed through `GitLab <https://lab.cluster.gsi.dit.upm.es/soil/soil.git>`_.
+   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/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

@@ -1,28 +0,0 @@
----
-name: simple
-dir_path: "/tmp/"
-num_trials: 3
-dry_run: True
-max_time: 100
-interval: 1
-seed: "CompleteSeed!"
-dump: false
-network_params:
-  generator: complete_graph
-  n: 10
-network_agents:
-  - agent_type: CounterModel
-    weight: 1
-    state:
-      id: 0
-  - agent_type: AggregatedCounter
-    weight: 0.2
-environment_agents: []
-environment_class: Environment
-environment_params:
-  am_i_complete: true
-default_state:
-  incidents: 0
-states:
-  - name: 'The first node'
-  - name: 'The second node'

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: 30
-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: 30
-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: 30
-name: Sim_all_herd
-network_agents:
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    id: neutral
-  weight: 1
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    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: 30
-name: Sim_wise_herd
-network_agents:
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    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: 30
-name: Sim_all_wise
-network_agents:
-- agent_type: WiseViewer
-  state:
-    has_tv: true
-    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

@@ -1,81 +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.
-    '''
-
-    level = logging.DEBUG
-
-    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/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

@@ -1,174 +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.INFO
-
-    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'''
-        self.debug('Life sucks. I\'m home!')
-    
-    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

@@ -1,120 +0,0 @@
-from soil.agents import FSM, state, default_state, BaseAgent
-from enum import Enum
-from random import random, choice
-from itertools import islice
-import logging
-import math
-
-
-class Genders(Enum):
-    male = 'male'
-    female = 'female'
-
-
-class RabbitModel(FSM):
-
-    level = logging.INFO
-
-    defaults = {
-        'age': 0,
-        'gender': Genders.male.value,
-        'mating_prob': 0.001,
-        'offspring': 0,
-    }
-
-    sexual_maturity = 4*30
-    life_expectancy = 365 * 3
-    gestation = 33
-    pregnancy = -1
-    max_females = 5
-
-    @default_state
-    @state
-    def newborn(self):
-        self['age'] += 1
-
-        if self['age'] >= self.sexual_maturity:
-            return self.fertile
-
-    @state
-    def fertile(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        if self['gender'] == Genders.female.value:
-            return
-
-        # Males try to mate
-        females = self.get_agents(state_id=self.fertile.id, gender=Genders.female.value, limit_neighbors=False)
-        for f in islice(females, self.max_females):
-            r = random()
-            if r < self['mating_prob']:
-                self.impregnate(f)
-                break # Take a break
-
-    def impregnate(self, whom):
-        if self['gender'] == Genders.female.value:
-            raise NotImplementedError('Females cannot impregnate')
-        whom['pregnancy'] = 0
-        whom['mate'] = self.id
-        whom.set_state(whom.pregnant)
-        self.debug('{} impregnating: {}. {}'.format(self.id, whom.id, whom.state))
-
-    @state
-    def pregnant(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        self['pregnancy'] += 1
-        self.debug('Pregnancy: {}'.format(self['pregnancy']))
-        if self['pregnancy'] >= self.gestation:
-            number_of_babies = int(8+4*random())
-            self.info('Having {} babies'.format(number_of_babies))
-            for i in range(number_of_babies):
-                state = {}
-                state['gender'] = choice(list(Genders)).value
-                child = self.env.add_node(self.__class__, state)
-                self.env.add_edge(self.id, child.id)
-                self.env.add_edge(self['mate'], child.id)
-                # self.add_edge()
-                self.debug('A BABY IS COMING TO LIFE')
-                self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.global_topology.number_of_nodes())+1
-                self.debug('Rabbits alive: {}'.format(self.env['rabbits_alive']))
-                self['offspring'] += 1
-                self.env.get_agent(self['mate'])['offspring'] += 1
-            del self['mate']
-            self['pregnancy'] = -1
-            return self.fertile
-
-    @state
-    def dead(self):
-        self.info('Agent {} is dying'.format(self.id))
-        if 'pregnancy' in self and self['pregnancy'] > -1:
-            self.info('A mother has died carrying a baby!!')
-        self.die()
-        return
-
-
-class RandomAccident(BaseAgent):
-
-    level = logging.DEBUG
-
-    def step(self):
-        rabbits_total = self.global_topology.number_of_nodes()
-        rabbits_alive = self.env.get('rabbits_alive', rabbits_total)
-        prob_death = self.env.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
-        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
-        for i in self.env.network_agents:
-            if i.state['id'] == i.dead.id:
-                continue
-            r = random()
-            if r < prob_death:
-                self.debug('I killed a rabbit: {}'.format(i.id))
-                rabbits_alive = self.env['rabbits_alive'] = rabbits_alive -1
-                self.log('Rabbits alive: {}'.format(self.env['rabbits_alive']))
-                i.set_state(i.dead)
-        self.log('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
-        if self.count_agents(state_id=RabbitModel.dead.id) == self.global_topology.number_of_nodes():
-            self.die()

examples/rabbits/rabbits.yml

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

examples/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

@@ -1,7 +1,5 @@
 nxsim
 simpy
-networkx>=2.0
+networkx
 numpy
 matplotlib
-pyyaml
-pandas

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={}
+

setup.cfg

@@ -1,4 +0,0 @@
-[aliases]
-test=pytest
-[tool:pytest]
-addopts = --verbose

setup.py

@@ -1,54 +0,0 @@
-import os
-from setuptools import setup
-
-
-with open(os.path.join('soil', 'VERSION')) as f:
-    __version__ = f.readlines()[0].strip()
-    assert __version__
-
-
-def parse_requirements(filename):
-    """ load requirements from a pip requirements file """
-    with open(filename, 'r') as f:
-        lineiter = list(line.strip() for line in f)
-    return [line for line in lineiter if line and not line.startswith("#")]
-
-
-install_reqs = parse_requirements("requirements.txt")
-test_reqs = parse_requirements("test-requirements.txt")
-
-
-setup(
-    name='soil',
-    packages=['soil'],  # this must be the same as the name above
-    version=__version__,
-    description=('An Agent-Based Social Simulator for Social Networks'),
-    author='J. Fernando Sanchez',
-    author_email='jf.sanchez@upm.es',
-    url='https://github.com/gsi-upm/soil',  # use the URL to the github repo
-    download_url='https://github.com/gsi-upm/soil/archive/{}.tar.gz'.format(
-        __version__),
-    keywords=['agent', 'social', 'simulator'],
-    classifiers=[
-        'Development Status :: 5 - Production/Stable',
-        'Environment :: Console',
-        'Intended Audience :: End Users/Desktop',
-        'Intended Audience :: Developers',
-        'License :: OSI Approved :: Apache Software License',
-        'Operating System :: MacOS :: MacOS X',
-        'Operating System :: Microsoft :: Windows',
-        'Operating System :: POSIX',
-        'Programming Language :: Python :: 3'],
-    install_requires=install_reqs,
-    extras_require={
-        'web': ['tornado']
-
-    },
-    tests_require=test_reqs,
-    setup_requires=['pytest-runner', ],
-    include_package_data=True,
-    entry_points={
-        'console_scripts':
-        ['soil = soil.__init__:main',
-        'soil-web = soil.web.__init__:main']
-    })

soil.py

@@ -0,0 +1,215 @@
+from models import *
+from nxsim import NetworkSimulation
+# import numpy
+from matplotlib import pyplot as plt
+import networkx as nx
+import settings
+import models
+import math
+import json
+import operator
+import community
+
+
+
+POPULATION = 0
+LEADERS = 1
+HAVEN = 2
+TRAINING = 3
+
+NON_RADICAL = 0
+NEUTRAL = 1
+RADICAL = 2
+#################
+# Visualization #
+#################
+
+def visualization(graph_name):
+
+    for x in range(0, settings.network_params["number_of_nodes"]):
+        attributes = {}
+        spells = []
+        for attribute in models.networkStatus["agent_%s" % x]:
+            if attribute == 'visible':
+                lastvisible = False
+                laststep = 0
+                for t_step in models.networkStatus["agent_%s" % x][attribute]:
+                    nowvisible = models.networkStatus["agent_%s" % x][attribute][t_step]
+                    if nowvisible and not lastvisible:
+                        laststep = t_step
+                    if not nowvisible and lastvisible:
+                        spells.append((laststep, t_step))
+
+                    lastvisible = nowvisible
+                if lastvisible:
+                    spells.append((laststep, None))
+            else:
+                emotionStatusAux = []
+                for t_step in models.networkStatus["agent_%s" % x][attribute]:
+                    prec = 2
+                    output = math.floor(models.networkStatus["agent_%s" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec)  # 2 decimals
+                    emotionStatusAux.append((output, t_step, t_step + settings.network_params["timeout"]))
+                attributes[attribute] = emotionStatusAux
+        if spells:
+            G.add_node(x, attributes, spells=spells)
+        else:
+            G.add_node(x, attributes)
+
+    print("Done!")
+
+
+    with open('data.txt', 'w') as outfile:
+        json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
+
+    for node in range(settings.network_params["number_of_nodes"]):
+        G.node[node]['x'] = G.node[node]['pos'][0]
+        G.node[node]['y'] = G.node[node]['pos'][1]
+        G.node[node]['viz'] = {"position": {"x": G.node[node]['pos'][0], "y": G.node[node]['pos'][1], "z": 0.0}}
+        del (G.node[node]['pos'])
+
+    nx.write_gexf(G, graph_name+".gexf", version="1.2draft")
+
+###########
+# Results #
+###########
+
+def results(model_name):
+    x_values = []
+    neutral_values = []
+    non_radical_values = []
+    radical_values = []
+
+    attribute_plot = 'status'
+    for time in range(0, settings.network_params["max_time"]):
+        value_neutral = 0
+        value_non_radical = 0
+        value_radical = 0
+        real_time = time * settings.network_params["timeout"]
+        activity = False
+        for x in range(0, settings.network_params["number_of_nodes"]):
+            if attribute_plot in models.networkStatus["agent_%s" % x]:
+                if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
+                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == NON_RADICAL:  
+                        value_non_radical += 1
+                        activity = True
+                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == NEUTRAL:  
+                        value_neutral += 1
+                        activity = True
+                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == RADICAL:  
+                        value_radical += 1
+                        activity = True
+
+
+        if activity:
+            x_values.append(real_time)
+            neutral_values.append(value_neutral)
+            non_radical_values.append(value_non_radical)
+            radical_values.append(value_radical)
+            activity = False
+
+    fig1 = plt.figure()
+    ax1 = fig1.add_subplot(111)
+
+    non_radical_line = ax1.plot(x_values, non_radical_values, label='Non radical')
+    neutral_line = ax1.plot(x_values, neutral_values, label='Neutral')
+    radical_line = ax1.plot(x_values, radical_values, label='Radical')
+    ax1.legend()
+    fig1.savefig(model_name+'.png')
+    plt.show()
+
+###########
+# Results #
+###########
+
+def resultadosTipo(model_name):
+    x_values = []
+    population_values = []
+    leaders_values = []
+    havens_values = []
+    training_enviroments_values = []
+
+    attribute_plot = 'type'
+    for time in range(0, settings.network_params["max_time"]):
+        value_population = 0
+        value_leaders = 0
+        value_havens = 0
+        value_training_enviroments = 0
+        real_time = time * settings.network_params["timeout"]
+        activity = False
+        for x in range(0, settings.network_params["number_of_nodes"]):
+            if attribute_plot in models.networkStatus["agent_%s" % x]:
+                if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
+                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == POPULATION:
+                        value_population += 1
+                        activity = True
+                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == LEADERS:
+                        value_leaders += 1
+                        activity = True
+                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == HAVEN:
+                        value_havens += 1
+                        activity = True
+                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == TRAINING:
+                        value_training_enviroments += 1
+                        activity = True
+        if activity:
+            x_values.append(real_time)
+            population_values.append(value_population)
+            leaders_values.append(value_leaders)
+            havens_values.append(value_havens)
+            training_enviroments_values.append(value_training_enviroments)
+            activity = False
+
+    fig2 = plt.figure()
+    ax2 = fig2.add_subplot(111)
+
+    population_line = ax2.plot(x_values, population_values, label='Population')
+    leaders_line = ax2.plot(x_values, leaders_values, label='Leader')
+    havens_line = ax2.plot(x_values, havens_values, label='Havens')
+    training_enviroments_line = ax2.plot(x_values, training_enviroments_values, label='Training Enviroments')
+    ax2.legend()
+    fig2.savefig(model_name+'_type'+'.png')
+    plt.show()
+
+####################
+# Network creation #
+####################
+
+#  nx.degree_centrality(G);
+
+if settings.network_params["network_type"] == 0:
+    G = nx.random_geometric_graph(settings.network_params["number_of_nodes"], 0.2)
+
+    settings.partition_param = community.best_partition(G)
+    settings.centrality_param = nx.betweenness_centrality(G).copy()
+
+
+    # print(settings.centrality_param)
+    # print(settings.partition_param)
+# More types of networks can be added here
+
+##############
+# Simulation #
+##############
+
+agents = settings.environment_params['agent']
+
+print("Using Agent(s): {agents}".format(agents=agents))
+
+if len(agents) > 1:
+    for agent in agents:
+        sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params["max_time"],
+                                num_trials=settings.network_params["num_trials"], logging_interval=1.0, **settings.environment_params)
+        sim.run_simulation()
+        print(str(agent))
+        results(str(agent))
+        resultadosTipo(str(agent))
+        visualization(str(agent))
+else:
+    agent = agents[0]
+    sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params["max_time"],
+                            num_trials=settings.network_params["num_trials"], logging_interval=1.0, **settings.environment_params)
+    sim.run_simulation()
+    results(str(agent))
+    resultadosTipo(str(agent))
+
+    visualization(str(agent))

soil.py~

@@ -0,0 +1,394 @@
+from nxsim import NetworkSimulation
+from nxsim import BaseNetworkAgent
+from nxsim import BaseLoggingAgent
+from random import randint
+from matplotlib import pyplot as plt
+import random
+import numpy as np
+import networkx as nx
+import settings
+
+
+settings.init()
+
+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,3)
+if settings.network_type == 2:
+    G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
+
+
+myList=[]
+networkStatus=[]
+for x in range(0, settings.number_of_nodes):
+    networkStatus.append({'id':x})
+
+
+
+# # Just like subclassing a process in SimPy
+# class MyAgent(BaseNetworkAgent):
+#     def __init__(self, environment=None, agent_id=0, state=()):  # Make sure to have these three keyword arguments
+#         super().__init__(environment=environment, agent_id=agent_id, state=state)
+#         # Add your own attributes here
+
+#     def run(self):
+#         # Add your behaviors here
+
+
+
+
+class SentimentCorrelationModel(BaseNetworkAgent):
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.outside_effects_prob = settings.outside_effects_prob
+        self.anger_prob = settings.anger_prob
+        self.joy_prob = settings.joy_prob
+        self.sadness_prob = settings.sadness_prob
+        self.disgust_prob = settings.disgust_prob
+        self.time_awareness=[]
+        for i in range(4):
+            self.time_awareness.append(0)     #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
+        networkStatus[self.id][self.env.now]=0
+
+
+    def run(self):
+        while True:
+            if self.env.now > 10:
+                G.add_node(205)
+                G.add_edge(205,0)
+            angry_neighbors_1_time_step=[]
+            joyful_neighbors_1_time_step=[]
+            sad_neighbors_1_time_step=[]
+            disgusted_neighbors_1_time_step=[]
+
+
+            angry_neighbors = self.get_neighboring_agents(state_id=1)
+            for x in angry_neighbors:
+                if x.time_awareness[0] > (self.env.now-500):
+                    angry_neighbors_1_time_step.append(x)
+            num_neighbors_angry = len(angry_neighbors_1_time_step)
+
+
+            joyful_neighbors = self.get_neighboring_agents(state_id=2)
+            for x in joyful_neighbors:
+                if x.time_awareness[1] > (self.env.now-500):
+                    joyful_neighbors_1_time_step.append(x)
+            num_neighbors_joyful = len(joyful_neighbors_1_time_step)
+
+
+            sad_neighbors = self.get_neighboring_agents(state_id=3)
+            for x in sad_neighbors:
+                if x.time_awareness[2] > (self.env.now-500):
+                    sad_neighbors_1_time_step.append(x)
+            num_neighbors_sad = len(sad_neighbors_1_time_step)
+
+
+            disgusted_neighbors = self.get_neighboring_agents(state_id=4)
+            for x in disgusted_neighbors:
+                if x.time_awareness[3] > (self.env.now-500):
+                    disgusted_neighbors_1_time_step.append(x)
+            num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
+
+            # #Outside effects. Asignamos un estado aleatorio
+            # if random.random() < settings.outside_effects_prob:
+            #     if self.state['id'] == 0:
+            #         self.state['id'] = random.randint(1,4)
+            #         myList.append(self.id)
+            #         networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
+            #         self.time_awareness = self.env.now #Para saber cuando se han contagiado
+            #         yield self.env.timeout(settings.timeout)
+            #     else:
+            #         yield self.env.timeout(settings.timeout)
+
+
+            # #Imitation effects-Joy
+
+            # if random.random() < (settings.joy_prob*(num_neighbors_joyful)/10):
+            #     myList.append(self.id)
+            #     self.state['id'] = 2
+            #     networkStatus[self.id][self.env.now]=2
+            #     yield self.env.timeout(settings.timeout)
+
+
+            # #Imitation effects-Sadness
+
+            # if random.random() < (settings.sadness_prob*(num_neighbors_sad)/10):
+            #     myList.append(self.id)
+            #     self.state['id'] = 3
+            #     networkStatus[self.id][self.env.now]=3
+            #     yield self.env.timeout(settings.timeout)
+
+
+            # #Imitation effects-Disgust
+
+            # if random.random() < (settings.disgust_prob*(num_neighbors_disgusted)/10):
+            #     myList.append(self.id)
+            #     self.state['id'] = 4
+            #     networkStatus[self.id][self.env.now]=4
+            #     yield self.env.timeout(settings.timeout)
+
+            # #Imitation effects-Anger
+
+            # if random.random() < (settings.anger_prob*(num_neighbors_angry)/10):
+            #     myList.append(self.id)
+            #     self.state['id'] = 1
+            #     networkStatus[self.id][self.env.now]=1
+            #     yield self.env.timeout(settings.timeout)
+
+            # yield self.env.timeout(settings.timeout)
+
+###########################################
+
+
+            anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
+            print("anger_prob " + str(anger_prob))
+            joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
+            print("joy_prob " + str(joy_prob))
+            sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
+            print("sadness_prob "+ str(sadness_prob))
+            disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
+            print("disgust_prob " + str(disgust_prob))
+            outside_effects_prob= settings.outside_effects_prob
+            print("outside_effects_prob " + str(outside_effects_prob))
+
+
+            num = random.random()
+
+
+            if(num<outside_effects_prob):
+                self.state['id'] = random.randint(1,4)
+                myList.append(self.id)
+                networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
+                self.time_awareness[self.state['id']-1] = self.env.now
+                yield self.env.timeout(settings.timeout)
+
+
+            if(num<anger_prob):
+
+                myList.append(self.id)
+                self.state['id'] = 1
+                networkStatus[self.id][self.env.now]=1
+                self.time_awareness[self.state['id']-1] = self.env.now
+            elif (num<joy_prob+anger_prob and num>anger_prob):
+
+                myList.append(self.id)
+                self.state['id'] = 2
+                networkStatus[self.id][self.env.now]=2
+                self.time_awareness[self.state['id']-1] = self.env.now
+            elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
+
+                myList.append(self.id)
+                self.state['id'] = 3
+                networkStatus[self.id][self.env.now]=3
+                self.time_awareness[self.state['id']-1] = self.env.now
+            elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
+
+                myList.append(self.id)
+                self.state['id'] = 4
+                networkStatus[self.id][self.env.now]=4
+                self.time_awareness[self.state['id']-1] = self.env.now
+
+            yield self.env.timeout(settings.timeout)
+
+
+            # anger_propagation = settings.anger_prob*num_neighbors_angry/10
+            # joy_propagation = anger_propagation + (settings.joy_prob*num_neighbors_joyful/10)
+            # sadness_propagation = joy_propagation + (settings.sadness_prob*num_neighbors_sad/10)
+            # disgust_propagation = sadness_propagation + (settings.disgust_prob*num_neighbors_disgusted/10)
+            # outside_effects_propagation = disgust_propagation + settings.outside_effects_prob
+
+            # if (num<anger_propagation):
+            #     if(self.state['id'] !=0):
+            #         myList.append(self.id)
+            #         self.state['id'] = 1
+            #         networkStatus[self.id][self.env.now]=1
+            #         yield self.env.timeout(settings.timeout)
+            # if (num<joy_propagation):
+            #     if(self.state['id'] !=0):
+            #         myList.append(self.id)
+            #         self.state['id'] = 2
+            #         networkStatus[self.id][self.env.now]=2
+            #         yield self.env.timeout(settings.timeout)
+            # if(num<sadness_propagation):
+            #     if(self.state['id'] !=0):
+            #         myList.append(self.id)
+            #         self.state['id'] = 3
+            #         networkStatus[self.id][self.env.now]=3
+            #         yield self.env.timeout(settings.timeout)
+            # # if(num<disgust_propagation):
+            # #     if(self.state['id'] !=0):
+            # #         myList.append(self.id)
+            # #         self.state['id'] = 4
+            # #         networkStatus[self.id][self.env.now]=4
+            # #         yield self.env.timeout(settings.timeout)
+            # if(num <outside_effects_propagation):
+            #     if self.state['id'] == 0:
+            #         self.state['id'] = random.randint(1,4)
+            #         myList.append(self.id)
+            #         networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
+            #         self.time_awareness = self.env.now #Para saber cuando se han contagiado
+            #         yield self.env.timeout(settings.timeout)
+            #     else:
+            #         yield self.env.timeout(settings.timeout)
+            # else:
+            #     yield self.env.timeout(settings.timeout)
+
+
+
+
+
+class BassModel(BaseNetworkAgent):
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.innovation_prob = settings.innovation_prob
+        self.imitation_prob = settings.imitation_prob
+        networkStatus[self.id][self.env.now]=0
+
+    def run(self):
+        while True:
+
+
+            #Outside effects
+            if random.random() < settings.innovation_prob:
+                if self.state['id'] == 0:
+                    self.state['id'] = 1
+                    myList.append(self.id)
+                    networkStatus[self.id][self.env.now]=1
+                    yield self.env.timeout(settings.timeout)
+                else:
+                    yield self.env.timeout(settings.timeout)
+
+            #Imitation effects
+            if self.state['id'] == 0:
+                aware_neighbors = self.get_neighboring_agents(state_id=1)
+                num_neighbors_aware = len(aware_neighbors)
+                if random.random() < (settings.imitation_prob*num_neighbors_aware):
+                    myList.append(self.id)
+                    self.state['id'] = 1
+                    networkStatus[self.id][self.env.now]=1
+                    yield self.env.timeout(settings.timeout)
+                else:
+                    yield self.env.timeout(settings.timeout)
+
+class IndependentCascadeModel(BaseNetworkAgent):
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.innovation_prob = settings.innovation_prob
+        self.imitation_prob = settings.imitation_prob
+        self.time_awareness = 0
+        networkStatus[self.id][self.env.now]=0
+
+    def run(self):
+        while True:
+            aware_neighbors_1_time_step=[]
+            #Outside effects
+            if random.random() < settings.innovation_prob:
+                if self.state['id'] == 0:
+                    self.state['id'] = 1
+                    myList.append(self.id)
+                    networkStatus[self.id][self.env.now]=1
+                    self.time_awareness = self.env.now #Para saber cuando se han contagiado
+                    yield self.env.timeout(settings.timeout)
+                else:
+                    yield self.env.timeout(settings.timeout)
+
+            #Imitation effects
+            if self.state['id'] == 0:
+                aware_neighbors = self.get_neighboring_agents(state_id=1)
+                for x in aware_neighbors:
+                    if x.time_awareness == (self.env.now-1):
+                        aware_neighbors_1_time_step.append(x)
+                num_neighbors_aware = len(aware_neighbors_1_time_step)
+                if random.random() < (settings.imitation_prob*num_neighbors_aware):
+                    myList.append(self.id)
+                    self.state['id'] = 1
+                    networkStatus[self.id][self.env.now]=1
+                    yield self.env.timeout(settings.timeout)
+                else:
+                    yield self.env.timeout(settings.timeout)
+
+
+class ZombieOutbreak(BaseNetworkAgent):
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.bite_prob = settings.bite_prob
+        networkStatus[self.id][self.env.now]=0
+
+
+    def run(self):
+        while True:
+            if random.random() < settings.heal_prob:
+                if self.state['id'] == 1:
+                    self.zombify()
+                    yield self.env.timeout(settings.timeout)
+                else:
+                    yield self.env.timeout(settings.timeout)
+            else:
+                if self.state['id'] == 1:
+                    print("Soy el zombie " + str(self.id) + " y me voy a curar porque el num aleatorio ha sido " + str(num))
+                    networkStatus[self.id][self.env.now]=0
+                    if self.id in myList:
+                        myList.remove(self.id)
+                    self.state['id'] = 0
+                    yield self.env.timeout(settings.timeout)
+                else:
+                    yield self.env.timeout(settings.timeout)
+
+
+    def zombify(self):
+        normal_neighbors = self.get_neighboring_agents(state_id=0)
+        for neighbor in normal_neighbors:
+            if random.random() < self.bite_prob:
+                print("Soy el zombie " + str(self.id) + " y voy a contagiar a " + str(neighbor.id))
+                neighbor.state['id'] = 1 # zombie
+                myList.append(neighbor.id)
+                networkStatus[self.id][self.env.now]=1
+                networkStatus[neighbor.id][self.env.now]=1
+                print(self.env.now, "Soy el zombie: "+ str(self.id), "Mi vecino es: "+ str(neighbor.id), sep='\t')
+                break
+
+
+# Initialize agent states. Let's assume everyone is normal.
+init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)]  # add keys as as necessary, but "id" must always refer to that state category
+
+# Seed a zombie
+#init_states[5] = {'id': 1}
+#init_states[3] = {'id': 1}
+
+sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorrelationModel,
+                        max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
+
+
+sim.run_simulation()
+
+myList = sorted(myList, key=int)
+#print("Los zombies son: " + str(myList))
+
+trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
+zombie_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
+
+#for x in range(len(myList)):
+#    G.node[myList[x]]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
+
+#G.node[1]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
+
+#lista = nx.nodes(G)
+#print('Nodos: ' + str(lista))
+for x in range(0, settings.number_of_nodes):
+    networkStatusAux=[]
+    for tiempo in networkStatus[x]:
+        if tiempo != 'id':
+	        networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
+    G.add_node(x, zombie= networkStatusAux)
+#print(networkStatus)
+
+
+nx.write_gexf(G,"test.gexf", version="1.2draft")
+plt.plot(zombie_census)
+plt.draw()  # pyplot draw()
+plt.savefig("zombie.png")
+#print(networkStatus)
+#nx.draw(G)
+#plt.show()
+#plt.savefig("path.png")

soil/VERSION

@@ -1 +0,0 @@
-0.13.4

soil/__init__.py

@@ -1,76 +0,0 @@
-import importlib
-import sys
-import os
-import pdb
-import logging
-
-from .version import __version__
-
-try:
-    basestring
-except NameError:
-    basestring = str
-
-from . import agents
-from .simulation import *
-from .environment import Environment
-from . import utils
-from . import analysis
-
-def main():
-    import argparse
-    from . import simulation
-
-    logging.basicConfig(level=logging.INFO)
-    logging.info('Running SOIL version: {}'.format(__version__))
-
-    parser = argparse.ArgumentParser(description='Run a SOIL simulation')
-    parser.add_argument('file', type=str,
-                        nargs="?",
-                        default='simulation.yml',
-                        help='python module containing the simulation configuration.')
-    parser.add_argument('--module', '-m', type=str,
-                        help='file containing the code of any custom agents.')
-    parser.add_argument('--dry-run', '--dry', action='store_true',
-                        help='Do not store the results of the simulation.')
-    parser.add_argument('--pdb', action='store_true',
-                        help='Use a pdb console in case of exception.')
-    parser.add_argument('--graph', '-g', action='store_true',
-                        help='Dump GEXF graph. Defaults to false.')
-    parser.add_argument('--csv', action='store_true',
-                        help='Dump history in CSV format. Defaults to false.')
-    parser.add_argument('--output', '-o', type=str, default="soil_output",
-                        help='folder to write results to. It defaults to the current directory.')
-    parser.add_argument('--synchronous', action='store_true',
-                        help='Run trials serially and synchronously instead of in parallel. Defaults to false.')
-
-    args = parser.parse_args()
-
-    if os.getcwd() not in sys.path:
-        sys.path.append(os.getcwd())
-    if args.module:
-        importlib.import_module(args.module)
-
-    logging.info('Loading config file: {}'.format(args.file))
-
-    try:
-        dump = []
-        if not args.dry_run:
-            if args.csv:
-                dump.append('csv')
-            if args.graph:
-                dump.append('gexf')
-        simulation.run_from_config(args.file,
-                                   dry_run=args.dry_run,
-                                   dump=dump,
-                                   parallel=(not args.synchronous),
-                                   results_dir=args.output)
-    except Exception:
-        if args.pdb:
-            pdb.post_mortem()
-        else:
-            raise
-
-
-if __name__ == '__main__':
-    main()

soil/__main__.py

@@ -1,4 +0,0 @@
-from . import main
-
-if __name__ == '__main__':
-    main()

soil/agents/BassModel.py

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

soil/agents/BigMarketModel.py

@@ -1,102 +0,0 @@
-import random
-from . import BaseAgent
-
-
-class BigMarketModel(BaseAgent):
-    """
-    Settings:
-        Names:
-            enterprises [Array]
-            
-            tweet_probability_enterprises [Array]
-        Users:
-            tweet_probability_users
-            
-            tweet_relevant_probability
-            
-            tweet_probability_about [Array]
-            
-            sentiment_about [Array]
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.enterprises = environment.environment_params['enterprises']
-        self.type = ""
-        self.number_of_enterprises = len(environment.environment_params['enterprises'])
-
-        if self.id < self.number_of_enterprises:  # Enterprises
-            self.state['id'] = self.id
-            self.type = "Enterprise"
-            self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id]
-        else:  # normal users
-            self.state['id'] = self.number_of_enterprises
-            self.type = "User"
-            self.tweet_probability = environment.environment_params['tweet_probability_users']
-            self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability']
-            self.tweet_probability_about = environment.environment_params['tweet_probability_about']  # List
-            self.sentiment_about = environment.environment_params['sentiment_about']  # List
-
-    def step(self):
-
-        if self.id < self.number_of_enterprises:  # Enterprise
-            self.enterpriseBehaviour()
-        else:  # Usuario
-            self.userBehaviour()
-            for i in range(self.number_of_enterprises):  # So that it never is set to 0 if there are not changes (logs)
-                self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
-
-    def enterpriseBehaviour(self):
-
-        if random.random() < self.tweet_probability:  # Tweets
-            aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbour users
-            for x in aware_neighbors:
-                if random.uniform(0,10) < 5:
-                    x.sentiment_about[self.id] += 0.1  # Increments for enterprise
-                else:
-                    x.sentiment_about[self.id] -= 0.1  # Decrements for enterprise
-
-                # Establecemos limites
-                if x.sentiment_about[self.id] > 1:
-                    x.sentiment_about[self.id] = 1
-                if x.sentiment_about[self.id]< -1:
-                    x.sentiment_about[self.id] = -1
-
-                x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
-
-    def userBehaviour(self):
-
-        if random.random() < self.tweet_probability:  # Tweets
-            if random.random() < self.tweet_relevant_probability:  # Tweets something relevant
-                # Tweet probability per enterprise
-                for i in range(self.number_of_enterprises):
-                    random_num = random.random()
-                    if random_num < self.tweet_probability_about[i]:
-                        # The condition is fulfilled, sentiments are evaluated towards that enterprise
-                        if self.sentiment_about[i] < 0:
-                            # NEGATIVO
-                            self.userTweets("negative",i)
-                        elif self.sentiment_about[i] == 0:
-                            # NEUTRO
-                            pass
-                        else:
-                            # POSITIVO
-                            self.userTweets("positive",i)
-
-    def userTweets(self,sentiment,enterprise):
-        aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbours users
-        for x in aware_neighbors:
-            if sentiment == "positive":
-                x.sentiment_about[enterprise] +=0.003
-            elif sentiment == "negative":
-                x.sentiment_about[enterprise] -=0.003
-            else:
-                pass
-
-            # Establecemos limites
-            if x.sentiment_about[enterprise] > 1:
-                x.sentiment_about[enterprise] = 1
-            if x.sentiment_about[enterprise] < -1:
-                x.sentiment_about[enterprise] = -1
-
-            x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]

soil/agents/CounterModel.py

@@ -1,32 +0,0 @@
-from . import BaseAgent
-
-
-class CounterModel(BaseAgent):
-    """
-    Dummy behaviour. It counts the number of nodes in the network and neighbors
-    in each step and adds it to its state.
-    """
-
-    def step(self):
-        # Outside effects
-        total = len(list(self.get_all_agents()))
-        neighbors = len(list(self.get_neighboring_agents()))
-        self['times'] = self.get('times', 0) + 1
-        self['neighbors'] = neighbors
-        self['total'] = total
-
-
-class AggregatedCounter(BaseAgent):
-    """
-    Dummy behaviour. It counts the number of nodes in the network and neighbors
-    in each step and adds it to its state.
-    """
-
-    def step(self):
-        # Outside effects
-        total = len(list(self.get_all_agents()))
-        neighbors = len(list(self.get_neighboring_agents()))
-        self['times'] = self.get('times', 0) + 1
-        self['neighbors'] = self.get('neighbors', 0) + neighbors
-        self['total'] = total = self.get('total', 0) + total
-        self.debug('Running for step: {}. Total: {}'.format(self.now, total))

soil/agents/DrawingAgent.py

@@ -1,18 +0,0 @@
-from . import BaseAgent
-
-import os.path
-import matplotlib
-import matplotlib.pyplot as plt
-import networkx as nx
-
-
-class DrawingAgent(BaseAgent):
-    """
-    Agent that draws the state of the network.
-    """
-
-    def step(self):
-        # Outside effects
-        f = plt.figure()
-        nx.draw(self.env.G, node_size=10, width=0.2, pos=nx.spring_layout(self.env.G, scale=100), ax=f.add_subplot(111))
-        f.savefig(os.path.join(self.env.get_path(), "graph-"+str(self.env.now)+".png"))

soil/agents/IndependentCascadeModel.py

@@ -1,49 +0,0 @@
-import random
-from . import BaseAgent
-
-
-class IndependentCascadeModel(BaseAgent):
-    """
-    Settings:
-        innovation_prob
-
-        imitation_prob
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.innovation_prob = environment.environment_params['innovation_prob']
-        self.imitation_prob = environment.environment_params['imitation_prob']
-        self.state['time_awareness'] = 0
-        self.state['sentimentCorrelation'] = 0
-
-    def step(self):
-        self.behaviour()
-
-    def behaviour(self):
-        aware_neighbors_1_time_step = []
-        # Outside effects
-        if random.random() < self.innovation_prob:
-            if self.state['id'] == 0:
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
-                self.state['time_awareness'] = self.env.now  # To know when they have been infected
-            else:
-                pass
-
-            return
-
-        # Imitation effects
-        if self.state['id'] == 0:
-            aware_neighbors = self.get_neighboring_agents(state_id=1)
-            for x in aware_neighbors:
-                if x.state['time_awareness'] == (self.env.now-1):
-                    aware_neighbors_1_time_step.append(x)
-            num_neighbors_aware = len(aware_neighbors_1_time_step)
-            if random.random() < (self.imitation_prob*num_neighbors_aware):
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
-            else:
-                pass
-
-            return

soil/agents/ModelM2.py

@@ -1,242 +0,0 @@
-import random
-import numpy as np
-from . import BaseAgent
-
-
-class SpreadModelM2(BaseAgent):
-    """
-    Settings:
-        prob_neutral_making_denier
-
-        prob_infect
-
-        prob_cured_healing_infected
-
-        prob_cured_vaccinate_neutral
-
-        prob_vaccinated_healing_infected
-
-        prob_vaccinated_vaccinate_neutral
-
-        prob_generate_anti_rumor
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
-                                                           environment.environment_params['standard_variance'])
-
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
-                                            environment.environment_params['standard_variance'])
-
-        self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
-                                                            environment.environment_params['standard_variance'])
-        self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
-                                                             environment.environment_params['standard_variance'])
-
-        self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
-                                                                 environment.environment_params['standard_variance'])
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
-                                                                  environment.environment_params['standard_variance'])
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
-                                                         environment.environment_params['standard_variance'])
-
-    def step(self):
-
-        if self.state['id'] == 0:  # Neutral
-            self.neutral_behaviour()
-        elif self.state['id'] == 1:  # Infected
-            self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
-            self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
-            self.vaccinated_behaviour()
-
-    def neutral_behaviour(self):
-
-        # Infected
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        if len(infected_neighbors) > 0:
-            if random.random() < self.prob_neutral_making_denier:
-                self.state['id'] = 3   # Vaccinated making denier
-
-    def infected_behaviour(self):
-
-        # Neutral
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
-                neighbor.state['id'] = 1  # Infected
-
-    def cured_behaviour(self):
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-    def vaccinated_behaviour(self):
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Generate anti-rumor
-        infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
-
-
-class ControlModelM2(BaseAgent):
-    """
-    Settings:
-        prob_neutral_making_denier
-
-        prob_infect
-        
-        prob_cured_healing_infected
-        
-        prob_cured_vaccinate_neutral
-        
-        prob_vaccinated_healing_infected
-        
-        prob_vaccinated_vaccinate_neutral
-        
-        prob_generate_anti_rumor
-    """
-
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
-                                                           environment.environment_params['standard_variance'])
-
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
-                                            environment.environment_params['standard_variance'])
-
-        self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
-                                                            environment.environment_params['standard_variance'])
-        self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
-                                                             environment.environment_params['standard_variance'])
-
-        self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
-                                                                 environment.environment_params['standard_variance'])
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
-                                                                  environment.environment_params['standard_variance'])
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
-                                                         environment.environment_params['standard_variance'])
-
-    def step(self):
-
-        if self.state['id'] == 0:  # Neutral
-            self.neutral_behaviour()
-        elif self.state['id'] == 1:  # Infected
-            self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
-            self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
-            self.vaccinated_behaviour()
-        elif self.state['id'] == 4:  # Beacon-off
-            self.beacon_off_behaviour()
-        elif self.state['id'] == 5:  # Beacon-on
-            self.beacon_on_behaviour()
-
-    def neutral_behaviour(self):
-        self.state['visible'] = False
-
-        # Infected
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        if len(infected_neighbors) > 0:
-            if random.random() < self.prob_neutral_making_denier:
-                self.state['id'] = 3   # Vaccinated making denier
-
-    def infected_behaviour(self):
-
-        # Neutral
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
-                neighbor.state['id'] = 1  # Infected
-        self.state['visible'] = False
-
-    def cured_behaviour(self):
-
-        self.state['visible'] = True
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-    def vaccinated_behaviour(self):
-        self.state['visible'] = True
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Generate anti-rumor
-        infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
-
-    def beacon_off_behaviour(self):
-        self.state['visible'] = False
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        if len(infected_neighbors) > 0:
-            self.state['id'] == 5  # Beacon on
-
-    def beacon_on_behaviour(self):
-        self.state['visible'] = False
-        # Cure (M2 feature added)
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
-            neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
-            for neighbor in neutral_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
-                    neighbor.state['id'] = 3  # Vaccinated
-            infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
-            for neighbor in infected_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
-                    neighbor.state['id'] = 2  # Cured
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated

soil/agents/SISaModel.py

@@ -1,93 +0,0 @@
-import random
-import numpy as np
-from . import FSM, state
-
-
-class SISaModel(FSM):
-    """
-    Settings:
-        neutral_discontent_spon_prob
-        
-        neutral_discontent_infected_prob
-        
-        neutral_content_spon_prob
-        
-        neutral_content_infected_prob
-        
-        discontent_neutral
-        
-        discontent_content
-        
-        variance_d_c
-        
-        content_discontent
-        
-        variance_c_d
-        
-        content_neutral
-        
-        standard_variance
-    """
-
-    def __init__(self, environment, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.neutral_discontent_spon_prob = np.random.normal(self.env['neutral_discontent_spon_prob'],
-                                                             self.env['standard_variance'])
-        self.neutral_discontent_infected_prob = np.random.normal(self.env['neutral_discontent_infected_prob'],
-                                                                 self.env['standard_variance'])
-        self.neutral_content_spon_prob = np.random.normal(self.env['neutral_content_spon_prob'],
-                                                          self.env['standard_variance'])
-        self.neutral_content_infected_prob = np.random.normal(self.env['neutral_content_infected_prob'],
-                                                              self.env['standard_variance'])
-
-        self.discontent_neutral = np.random.normal(self.env['discontent_neutral'],
-                                                   self.env['standard_variance'])
-        self.discontent_content = np.random.normal(self.env['discontent_content'],
-                                                   self.env['variance_d_c'])
-
-        self.content_discontent = np.random.normal(self.env['content_discontent'],
-                                                   self.env['variance_c_d'])
-        self.content_neutral = np.random.normal(self.env['content_neutral'],
-                                                self.env['standard_variance'])
-
-    @state
-    def neutral(self):
-        # Spontaneous effects
-        if random.random() < self.neutral_discontent_spon_prob:
-            return self.discontent
-        if random.random() < self.neutral_content_spon_prob:
-            return self.content
-
-        # Infected
-        discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent)
-        if random.random() < discontent_neighbors * self.neutral_discontent_infected_prob:
-            return self.discontent
-        content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
-        if random.random() < content_neighbors * self.neutral_content_infected_prob:
-            return self.content
-        return self.neutral
-
-    @state
-    def discontent(self):
-        # Healing
-        if random.random() < self.discontent_neutral:
-            return self.neutral
-
-        # Superinfected
-        content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
-        if random.random() < content_neighbors * self.discontent_content:
-            return self.content
-        return self.discontent
-
-    @state
-    def content(self):
-        # Healing
-        if random.random() < self.content_neutral:
-            return self.neutral
-
-        # Superinfected
-        discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent.id)
-        if random.random() < discontent_neighbors * self.content_discontent:
-            self.discontent
-        return self.content

soil/agents/SentimentCorrelationModel.py

@@ -1,102 +0,0 @@
-import random
-from . import BaseAgent
-
-
-class SentimentCorrelationModel(BaseAgent):
-    """
-    Settings:
-        outside_effects_prob
-        
-        anger_prob
-        
-        joy_prob
-        
-        sadness_prob
-        
-        disgust_prob
-    """
-
-    def __init__(self, environment, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.outside_effects_prob = environment.environment_params['outside_effects_prob']
-        self.anger_prob = environment.environment_params['anger_prob']
-        self.joy_prob = environment.environment_params['joy_prob']
-        self.sadness_prob = environment.environment_params['sadness_prob']
-        self.disgust_prob = environment.environment_params['disgust_prob']
-        self.state['time_awareness'] = []
-        for i in range(4):  # In this model we have 4 sentiments
-            self.state['time_awareness'].append(0)  # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
-        self.state['sentimentCorrelation'] = 0
-
-    def step(self):
-        self.behaviour()
-
-    def behaviour(self):
-
-        angry_neighbors_1_time_step = []
-        joyful_neighbors_1_time_step = []
-        sad_neighbors_1_time_step = []
-        disgusted_neighbors_1_time_step = []
-
-        angry_neighbors = self.get_neighboring_agents(state_id=1)
-        for x in angry_neighbors:
-            if x.state['time_awareness'][0] > (self.env.now-500):
-                angry_neighbors_1_time_step.append(x)
-        num_neighbors_angry = len(angry_neighbors_1_time_step)
-
-        joyful_neighbors = self.get_neighboring_agents(state_id=2)
-        for x in joyful_neighbors:
-            if x.state['time_awareness'][1] > (self.env.now-500):
-                joyful_neighbors_1_time_step.append(x)
-        num_neighbors_joyful = len(joyful_neighbors_1_time_step)
-
-        sad_neighbors = self.get_neighboring_agents(state_id=3)
-        for x in sad_neighbors:
-            if x.state['time_awareness'][2] > (self.env.now-500):
-                sad_neighbors_1_time_step.append(x)
-        num_neighbors_sad = len(sad_neighbors_1_time_step)
-
-        disgusted_neighbors = self.get_neighboring_agents(state_id=4)
-        for x in disgusted_neighbors:
-            if x.state['time_awareness'][3] > (self.env.now-500):
-                disgusted_neighbors_1_time_step.append(x)
-        num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
-
-        anger_prob = self.anger_prob+(len(angry_neighbors_1_time_step)*self.anger_prob)
-        joy_prob = self.joy_prob+(len(joyful_neighbors_1_time_step)*self.joy_prob)
-        sadness_prob = self.sadness_prob+(len(sad_neighbors_1_time_step)*self.sadness_prob)
-        disgust_prob = self.disgust_prob+(len(disgusted_neighbors_1_time_step)*self.disgust_prob)
-        outside_effects_prob = self.outside_effects_prob
-
-        num = random.random()
-
-        if num<outside_effects_prob:
-            self.state['id'] = random.randint(1, 4)
-
-            self.state['sentimentCorrelation'] = self.state['id'] # It is stored when it has been infected for the dynamic network
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-            self.state['sentiment'] = self.state['id']
-
-
-        if(num<anger_prob):
-
-            self.state['id'] = 1
-            self.state['sentimentCorrelation'] = 1
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-        elif (num<joy_prob+anger_prob and num>anger_prob):
-
-            self.state['id'] = 2
-            self.state['sentimentCorrelation'] = 2
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-        elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
-
-            self.state['id'] = 3
-            self.state['sentimentCorrelation'] = 3
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-        elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
-
-            self.state['id'] = 4
-            self.state['sentimentCorrelation'] = 4
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-
-        self.state['sentiment'] = self.state['id']