Updated readthedocs

docs/_build/html/_sources/index.rst.txt

@@ -13,11 +13,13 @@ Soil is an Agent-based Social Simulator in Python for modelling and simulation o
    :caption: Learn more about soil:
 
    installation
+   usage
+   models
 
 
-Indices and tables
-==================
 
-* :ref:`genindex`
+.. Indices and tables
-* :ref:`modindex`
+   ==================
-* :ref:`search`
+   * :ref:`genindex`
+   * :ref:`modindex`
+   * :ref:`search`

docs/_build/html/_sources/models.rst.txt

@@ -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 Simulation 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.number_of_nodes):
+        sentimentCorrelationNodeArray.append({'id': x})
+    # 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
+
+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. 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/_build/html/_sources/usage.rst.txt

@@ -0,0 +1,98 @@
+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.py file you will find the configuration of the network.
+
+  .. code:: python
+
+    # Network settings
+    network_type = 1
+    number_of_nodes = 1000
+    max_time = 50
+    num_trials = 1
+    timeout = 2
+
+* In the Simulation Settings JSON file, you will 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 Simulation Settings JSON file.
+
+.. 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.
+
+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
+
+    python 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.

docs/_build/html/demo.html

@@ -0,0 +1,96 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+  "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+
+
+<html xmlns="http://www.w3.org/1999/xhtml">
+  <head>
+    <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+    
+    <title>&lt;no title&gt; &#8212; Soil 0.1 documentation</title>
+    
+    <link rel="stylesheet" href="_static/alabaster.css" type="text/css" />
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+        SOURCELINK_SUFFIX: '.txt'
+      };
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+    <link rel="index" title="Index" href="genindex.html" />
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+    <link rel="prev" title="Models" href="models.html" />
+   
+  <link rel="stylesheet" href="_static/custom.css" type="text/css" />
+  
+  
+  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
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+  </head>
+  <body role="document">
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+        <div class="bodywrapper">
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+        </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper"><div class="relations">
+<h3>Related Topics</h3>
+<ul>
+  <li><a href="index.html">Documentation overview</a><ul>
+      <li>Previous: <a href="models.html" title="previous chapter">Models</a></li>
+  </ul></li>
+</ul>
+</div>
+  <div role="note" aria-label="source link">
+    <h3>This Page</h3>
+    <ul class="this-page-menu">
+      <li><a href="_sources/demo.rst.txt"
+            rel="nofollow">Show Source</a></li>
+    </ul>
+   </div>
+<div id="searchbox" style="display: none" role="search">
+  <h3>Quick search</h3>
+    <form class="search" action="search.html" method="get">
+      <div><input type="text" name="q" /></div>
+      <div><input type="submit" value="Go" /></div>
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docs/_build/html/index.html

@@ -49,16 +49,22 @@
 <p class="caption"><span class="caption-text">Learn more about soil:</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="usage.html">Usage</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="usage.html#simulation-settings">Simulation Settings</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage.html#network-types">Network Types</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage.html#models-settings">Models Settings</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage.html#running-the-simulation">Running the Simulation</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="models.html">Developing new models</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="models.html#what-is-a-model">What is a model?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="models.html#models-code">Models Code</a></li>
+<li class="toctree-l2"><a class="reference internal" href="models.html#variable-initialization">Variable Initialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="models.html#example-model">Example Model</a></li>
+</ul>
+</li>
 </ul>
 </div>
-</div>
-<div class="section" id="indices-and-tables">
-<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
-<ul class="simple">
-<li><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></li>
-<li><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></li>
-<li><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></li>
-</ul>
 </div>
 
 
@@ -66,13 +72,7 @@
         </div>
       </div>
       <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
+        <div class="sphinxsidebarwrapper"><div class="relations">
-  <h3><a href="#">Table Of Contents</a></h3>
-  <ul>
-<li><a class="reference internal" href="#">Welcome to Soil&#8217;s documentation!</a></li>
-<li><a class="reference internal" href="#indices-and-tables">Indices and tables</a></li>
-</ul>
-<div class="relations">
 <h3>Related Topics</h3>
 <ul>
   <li><a href="#">Documentation overview</a><ul>

docs/_build/html/models.html

@@ -0,0 +1,210 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+  "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+
+
+<html xmlns="http://www.w3.org/1999/xhtml">
+  <head>
+    <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+    
+    <title>Developing new models &#8212; Soil 0.1 documentation</title>
+    
+    <link rel="stylesheet" href="_static/alabaster.css" type="text/css" />
+    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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+        HAS_SOURCE:  true,
+        SOURCELINK_SUFFIX: '.txt'
+      };
+    </script>
+    <script type="text/javascript" src="_static/jquery.js"></script>
+    <script type="text/javascript" src="_static/underscore.js"></script>
+    <script type="text/javascript" src="_static/doctools.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="prev" title="Usage" href="usage.html" />
+   
+  <link rel="stylesheet" href="_static/custom.css" type="text/css" />
+  
+  
+  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
+
+  </head>
+  <body role="document">
+  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <div class="section" id="developing-new-models">
+<h1>Developing new models<a class="headerlink" href="#developing-new-models" title="Permalink to this headline">¶</a></h1>
+<p>This document describes how to develop a new analysis model.</p>
+<div class="section" id="what-is-a-model">
+<h2>What is a model?<a class="headerlink" href="#what-is-a-model" title="Permalink to this headline">¶</a></h2>
+<p>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:</p>
+<ul class="simple">
+<li>Python module: the actual code that describes the behaviour.</li>
+<li>Setting up the variables in the Simulation Settings JSON file.</li>
+</ul>
+<p>This separation allows us to run the simulation with different agents.</p>
+</div>
+<div class="section" id="models-code">
+<h2>Models Code<a class="headerlink" href="#models-code" title="Permalink to this headline">¶</a></h2>
+<p>All the models are imported to the main file. The initialization look like this:</p>
+<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">settings</span>
+
+<span class="n">networkStatus</span> <span class="o">=</span> <span class="p">{}</span>  <span class="c1"># Dict that will contain the status of every agent in the network</span>
+
+<span class="n">sentimentCorrelationNodeArray</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">):</span>
+    <span class="n">sentimentCorrelationNodeArray</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">&#39;id&#39;</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
+<span class="c1"># Initialize agent states. Let&#39;s assume everyone is normal.</span>
+<span class="n">init_states</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">&#39;id&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="p">}</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">)]</span>
+    <span class="c1"># add keys as as necessary, but &quot;id&quot; must always refer to that state category</span>
+</pre></div>
+</div>
+<p>A new model have to inherit the BaseBehaviour class which is in the same module.
+There are two basics methods:</p>
+<ul class="simple">
+<li>__init__</li>
+<li>step: used to define the behaviour over time.</li>
+</ul>
+</div>
+<div class="section" id="variable-initialization">
+<h2>Variable Initialization<a class="headerlink" href="#variable-initialization" title="Permalink to this headline">¶</a></h2>
+<p>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.</p>
+<div class="code json highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
+    <span class="s2">&quot;agent&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;SISaModel&quot;</span><span class="p">,</span><span class="s2">&quot;ControlModelM2&quot;</span><span class="p">],</span>
+
+    <span class="s2">&quot;neutral_discontent_spon_prob&quot;</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
+    <span class="s2">&quot;neutral_discontent_infected_prob&quot;</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
+    <span class="s2">&quot;neutral_content_spon_prob&quot;</span><span class="p">:</span> <span class="mf">0.18</span><span class="p">,</span>
+    <span class="s2">&quot;neutral_content_infected_prob&quot;</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
+
+    <span class="s2">&quot;discontent_neutral&quot;</span><span class="p">:</span> <span class="mf">0.13</span><span class="p">,</span>
+    <span class="s2">&quot;discontent_content&quot;</span><span class="p">:</span> <span class="mf">0.07</span><span class="p">,</span>
+    <span class="s2">&quot;variance_d_c&quot;</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
+
+    <span class="s2">&quot;content_discontent&quot;</span><span class="p">:</span> <span class="mf">0.009</span><span class="p">,</span>
+    <span class="s2">&quot;variance_c_d&quot;</span><span class="p">:</span> <span class="mf">0.003</span><span class="p">,</span>
+    <span class="s2">&quot;content_neutral&quot;</span><span class="p">:</span> <span class="mf">0.088</span><span class="p">,</span>
+
+    <span class="s2">&quot;standard_variance&quot;</span><span class="p">:</span> <span class="mf">0.055</span><span class="p">,</span>
+
+
+    <span class="s2">&quot;prob_neutral_making_denier&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
+
+    <span class="s2">&quot;prob_infect&quot;</span><span class="p">:</span> <span class="mf">0.075</span><span class="p">,</span>
+
+    <span class="s2">&quot;prob_cured_healing_infected&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
+    <span class="s2">&quot;prob_cured_vaccinate_neutral&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
+
+    <span class="s2">&quot;prob_vaccinated_healing_infected&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
+    <span class="s2">&quot;prob_vaccinated_vaccinate_neutral&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
+    <span class="s2">&quot;prob_generate_anti_rumor&quot;</span><span class="p">:</span> <span class="mf">0.035</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>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. For the usage, see <a class="reference internal" href="usage.html"><span class="doc">Usage</span></a>.</p>
+</div>
+<div class="section" id="example-model">
+<h2>Example Model<a class="headerlink" href="#example-model" title="Permalink to this headline">¶</a></h2>
+<p>In this section, we will implement a Sentiment Correlation Model.</p>
+<p>The class would look like this:</p>
+<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">..BaseBehaviour</span> <span class="k">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">..</span> <span class="k">import</span> <span class="n">sentimentCorrelationNodeArray</span>
+
+<span class="k">class</span> <span class="nc">SentimentCorrelationModel</span><span class="p">(</span><span class="n">BaseBehaviour</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">environment</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">agent_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">state</span><span class="o">=</span><span class="p">()):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="n">environment</span><span class="o">=</span><span class="n">environment</span><span class="p">,</span> <span class="n">agent_id</span><span class="o">=</span><span class="n">agent_id</span><span class="p">,</span> <span class="n">state</span><span class="o">=</span><span class="n">state</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">outside_effects_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">&#39;outside_effects_prob&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">anger_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">&#39;anger_prob&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">joy_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">&#39;joy_prob&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sadness_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">&#39;sadness_prob&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">disgust_prob</span> <span class="o">=</span> <span class="n">environment</span><span class="o">.</span><span class="n">environment_params</span><span class="p">[</span><span class="s1">&#39;disgust_prob&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">time_awareness</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>  <span class="c1"># In this model we have 4 sentiments</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">time_awareness</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># 0-&gt; Anger, 1-&gt; joy, 2-&gt;sadness, 3 -&gt; disgust</span>
+        <span class="n">sentimentCorrelationNodeArray</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">id</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="o">.</span><span class="n">now</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">now</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">behaviour</span><span class="p">()</span>  <span class="c1"># Method which define the behaviour</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">now</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file.</p>
+</div>
+</div>
+
+
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+  <h3><a href="index.html">Table Of Contents</a></h3>
+  <ul>
+<li><a class="reference internal" href="#">Developing new models</a><ul>
+<li><a class="reference internal" href="#what-is-a-model">What is a model?</a></li>
+<li><a class="reference internal" href="#models-code">Models Code</a></li>
+<li><a class="reference internal" href="#variable-initialization">Variable Initialization</a></li>
+<li><a class="reference internal" href="#example-model">Example Model</a></li>
+</ul>
+</li>
+</ul>
+<div class="relations">
+<h3>Related Topics</h3>
+<ul>
+  <li><a href="index.html">Documentation overview</a><ul>
+      <li>Previous: <a href="usage.html" title="previous chapter">Usage</a></li>
+  </ul></li>
+</ul>
+</div>
+  <div role="note" aria-label="source link">
+    <h3>This Page</h3>
+    <ul class="this-page-menu">
+      <li><a href="_sources/models.rst.txt"
+            rel="nofollow">Show Source</a></li>
+    </ul>
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+  <h3>Quick search</h3>
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docs/_build/html/usage.html

@@ -0,0 +1,196 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+  "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+
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+<html xmlns="http://www.w3.org/1999/xhtml">
+  <head>
+    <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+    
+    <title>Usage &#8212; Soil 0.1 documentation</title>
+    
+    <link rel="stylesheet" href="_static/alabaster.css" type="text/css" />
+    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
+    
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+    <script type="text/javascript" src="_static/doctools.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="Developing new models" href="models.html" />
+    <link rel="prev" title="Installation" href="installation.html" />
+   
+  <link rel="stylesheet" href="_static/custom.css" type="text/css" />
+  
+  
+  <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
+
+  </head>
+  <body role="document">
+  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <div class="section" id="usage">
+<h1>Usage<a class="headerlink" href="#usage" title="Permalink to this headline">¶</a></h1>
+<p>First of all, you need to install the package. See <a class="reference internal" href="installation.html"><span class="doc">Installation</span></a> for installation instructions.</p>
+<div class="section" id="simulation-settings">
+<h2>Simulation Settings<a class="headerlink" href="#simulation-settings" title="Permalink to this headline">¶</a></h2>
+<p>Once installed, before running a simulation, you need to configure it.</p>
+<ul>
+<li><p class="first">In the settings.py file you will find the configuration of the network.</p>
+<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="c1"># Network settings</span>
+<span class="n">network_type</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">number_of_nodes</span> <span class="o">=</span> <span class="mi">1000</span>
+<span class="n">max_time</span> <span class="o">=</span> <span class="mi">50</span>
+<span class="n">num_trials</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">timeout</span> <span class="o">=</span> <span class="mi">2</span>
+</pre></div>
+</div>
+</li>
+<li><p class="first">In the Simulation Settings JSON file, you will find the configuration of the models.</p>
+</li>
+</ul>
+</div>
+<div class="section" id="network-types">
+<h2>Network Types<a class="headerlink" href="#network-types" title="Permalink to this headline">¶</a></h2>
+<p>There are three types of network implemented, but you could add more.</p>
+<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">network_type</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+    <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">complete_graph</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">)</span>
+<span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">network_type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+    <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">barabasi_albert_graph</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">network_type</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+    <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">margulis_gabber_galil_graph</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+<span class="c1"># More types of networks can be added here</span>
+</pre></div>
+</div>
+</div>
+<div class="section" id="models-settings">
+<h2>Models Settings<a class="headerlink" href="#models-settings" title="Permalink to this headline">¶</a></h2>
+<p>After having configured the simulation, the next step is setting up the variables of the models.
+For this, you will need to modify the Simulation Settings JSON file.</p>
+<div class="code json highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
+    <span class="s2">&quot;agent&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;SISaModel&quot;</span><span class="p">,</span><span class="s2">&quot;ControlModelM2&quot;</span><span class="p">],</span>
+
+    <span class="s2">&quot;neutral_discontent_spon_prob&quot;</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
+    <span class="s2">&quot;neutral_discontent_infected_prob&quot;</span><span class="p">:</span> <span class="mf">0.04</span><span class="p">,</span>
+    <span class="s2">&quot;neutral_content_spon_prob&quot;</span><span class="p">:</span> <span class="mf">0.18</span><span class="p">,</span>
+    <span class="s2">&quot;neutral_content_infected_prob&quot;</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
+
+    <span class="s2">&quot;discontent_neutral&quot;</span><span class="p">:</span> <span class="mf">0.13</span><span class="p">,</span>
+    <span class="s2">&quot;discontent_content&quot;</span><span class="p">:</span> <span class="mf">0.07</span><span class="p">,</span>
+    <span class="s2">&quot;variance_d_c&quot;</span><span class="p">:</span> <span class="mf">0.02</span><span class="p">,</span>
+
+    <span class="s2">&quot;content_discontent&quot;</span><span class="p">:</span> <span class="mf">0.009</span><span class="p">,</span>
+    <span class="s2">&quot;variance_c_d&quot;</span><span class="p">:</span> <span class="mf">0.003</span><span class="p">,</span>
+    <span class="s2">&quot;content_neutral&quot;</span><span class="p">:</span> <span class="mf">0.088</span><span class="p">,</span>
+
+    <span class="s2">&quot;standard_variance&quot;</span><span class="p">:</span> <span class="mf">0.055</span><span class="p">,</span>
+
+
+    <span class="s2">&quot;prob_neutral_making_denier&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
+
+    <span class="s2">&quot;prob_infect&quot;</span><span class="p">:</span> <span class="mf">0.075</span><span class="p">,</span>
+
+    <span class="s2">&quot;prob_cured_healing_infected&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
+    <span class="s2">&quot;prob_cured_vaccinate_neutral&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
+
+    <span class="s2">&quot;prob_vaccinated_healing_infected&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
+    <span class="s2">&quot;prob_vaccinated_vaccinate_neutral&quot;</span><span class="p">:</span> <span class="mf">0.035</span><span class="p">,</span>
+    <span class="s2">&quot;prob_generate_anti_rumor&quot;</span><span class="p">:</span> <span class="mf">0.035</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>In this file you will define the different models you are going to simulate. You can simulate as many models
+as you want.</p>
+<p>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.</p>
+<p>Parameter validation will fail if a required parameter without a default has not been provided.</p>
+</div>
+<div class="section" id="running-the-simulation">
+<h2>Running the Simulation<a class="headerlink" href="#running-the-simulation" title="Permalink to this headline">¶</a></h2>
+<p>After setting all the configuration, you will be able to run the simulation. All you need to do is execute:</p>
+<div class="code bash highlight-default"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">soil</span><span class="o">.</span><span class="n">py</span>
+</pre></div>
+</div>
+<p>The simulation will return a dynamic graph .gexf file which could be visualized with
+<a class="reference external" href="https://gephi.org/users/download/">Gephi</a>.</p>
+<p>It will also return one .png picture for each model simulated.</p>
+</div>
+</div>
+
+
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+  <h3><a href="index.html">Table Of Contents</a></h3>
+  <ul>
+<li><a class="reference internal" href="#">Usage</a><ul>
+<li><a class="reference internal" href="#simulation-settings">Simulation Settings</a></li>
+<li><a class="reference internal" href="#network-types">Network Types</a></li>
+<li><a class="reference internal" href="#models-settings">Models Settings</a></li>
+<li><a class="reference internal" href="#running-the-simulation">Running the Simulation</a></li>
+</ul>
+</li>
+</ul>
+<div class="relations">
+<h3>Related Topics</h3>
+<ul>
+  <li><a href="index.html">Documentation overview</a><ul>
+      <li>Previous: <a href="installation.html" title="previous chapter">Installation</a></li>
+      <li>Next: <a href="models.html" title="next chapter">Developing new models</a></li>
+  </ul></li>
+</ul>
+</div>
+  <div role="note" aria-label="source link">
+    <h3>This Page</h3>
+    <ul class="this-page-menu">
+      <li><a href="_sources/usage.rst.txt"
+            rel="nofollow">Show Source</a></li>
+    </ul>
+   </div>
+<div id="searchbox" style="display: none" role="search">
+  <h3>Quick search</h3>
+    <form class="search" action="search.html" method="get">
+      <div><input type="text" name="q" /></div>
+      <div><input type="submit" value="Go" /></div>
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+        </div>
+      </div>
+      <div class="clearer"></div>
+    </div>
+    <div class="footer">
+      &copy;2017, GSI.
+      
+      |
+      Powered by <a href="http://sphinx-doc.org/">Sphinx 1.5.1</a>
+      &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.9</a>
+      
+      |
+      <a href="_sources/usage.rst.txt"
+          rel="nofollow">Page source</a>
+    </div>
+
+    
+
+    
+  </body>
+</html>

docs/index.rst

@@ -13,11 +13,13 @@ Soil is an Agent-based Social Simulator in Python for modelling and simulation o
    :caption: Learn more about soil:
 
    installation
+   usage
+   models
 
 
-Indices and tables
-==================
 
-* :ref:`genindex`
+.. Indices and tables
-* :ref:`modindex`
+   ==================
-* :ref:`search`
+   * :ref:`genindex`
+   * :ref:`modindex`
+   * :ref:`search`

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 Simulation 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.number_of_nodes):
+        sentimentCorrelationNodeArray.append({'id': x})
+    # 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
+
+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. 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/usage.rst

@@ -0,0 +1,98 @@
+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.py file you will find the configuration of the network.
+
+  .. code:: python
+
+    # Network settings
+    network_type = 1
+    number_of_nodes = 1000
+    max_time = 50
+    num_trials = 1
+    timeout = 2
+
+* In the Simulation Settings JSON file, you will 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 Simulation Settings JSON file.
+
+.. 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.
+
+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
+
+    python 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.

settings.py

@@ -1,5 +1,4 @@
 # General configuration
-
 import json
 
 # Network settings
@@ -9,12 +8,11 @@ max_time = 50
 num_trials = 1
 timeout = 2
 
-
 with open('simulation_settings.json', 'r') as f:
     environment_params = json.load(f)
 
-'''
 
+'''
 environment_params = {
     # Zombie model
     'bite_prob': 0.01,  # 0-1

simulation_settings.json

@@ -1,5 +1,4 @@
 {
-
     "agent": ["BaseBehaviour","SISaModel","ControlModelM2"],
 
 

soil.py

@@ -8,6 +8,7 @@ import models
 import math
 import json
 
+
 #################
 # Visualization #
 #################
@@ -25,7 +26,6 @@ def visualization(graph_name):
             attributes[attribute] = emotionStatusAux
             G.add_node(x, attributes)
 
-
     print("Done!")
 
     with open('data.txt', 'w') as outfile:
@@ -33,9 +33,11 @@ def visualization(graph_name):
 
     nx.write_gexf(G, graph_name+".gexf", version="1.2draft")
 
+
 ###########
 # Results #
 ###########
+
 def results(model_name):
     x_values = []
     infected_values = []
@@ -106,14 +108,12 @@ if settings.network_type == 2:
 
 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.max_time,
-                        num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
+                                num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
         sim.run_simulation()
         print(str(agent))
         results(str(agent))
@@ -121,13 +121,7 @@ if len(agents) > 1:
 else:
     agent = agents[0]
     sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.max_time,
-                        num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
+                            num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
     sim.run_simulation()
     results(str(agent))
     visualization(str(agent))
-
-
-
-
-
-