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All settings as JSON and documentation updated
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docs/_build/html/_sources/models.rst.txt
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@ -9,7 +9,7 @@ A model defines the behaviour of the agents with a view to assessing their effec
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In practice, a model consists of at least two parts:
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* Python module: the actual code that describes the behaviour.
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* Setting up the variables in the Simulation Settings JSON file.
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* Setting up the variables in the Settings JSON file.
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This separation allows us to run the simulation with different agents.
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@ -25,10 +25,10 @@ All the models are imported to the main file. The initialization look like this:
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networkStatus = {} # Dict that will contain the status of every agent in the network
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sentimentCorrelationNodeArray = []
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for x in range(0, settings.number_of_nodes):
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for x in range(0, settings.network_params["number_of_nodes"]):
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sentimentCorrelationNodeArray.append({'id': x})
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# Initialize agent states. Let's assume everyone is normal.
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init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)]
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init_states = [{'id': 0, } for _ in range(settings.network_params["number_of_nodes"])]
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# add keys as as necessary, but "id" must always refer to that state category
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A new model have to inherit the BaseBehaviour class which is in the same module.
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@ -77,7 +77,7 @@ passed as a parameter to the simulation.
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}
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In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
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The simulation returns one result for each model. For the usage, see :doc:`usage`.
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The simulation returns one result for each model, executing each model separately. For the usage, see :doc:`usage`.
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Example Model
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=============
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23
docs/_build/html/_sources/usage.rst.txt
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docs/_build/html/_sources/usage.rst.txt
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@ -8,18 +8,19 @@ Simulation Settings
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Once installed, before running a simulation, you need to configure it.
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* In the settings.py file you will find the configuration of the network.
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* In the Settings JSON file you will find the configuration of the network.
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.. code:: python
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# Network settings
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network_type = 1
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number_of_nodes = 1000
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max_time = 50
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num_trials = 1
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timeout = 2
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{
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"network_type": 1,
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"number_of_nodes": 1000,
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"max_time": 50,
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"num_trials": 1,
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"timeout": 2
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}
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* In the Simulation Settings JSON file, you will find the configuration of the models.
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* In the Settings JSON file, you will also find the configuration of the models.
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Network Types
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=============
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@ -40,7 +41,7 @@ Models Settings
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===============
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After having configured the simulation, the next step is setting up the variables of the models.
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For this, you will need to modify the Simulation Settings JSON file.
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For this, you will need to modify the Settings JSON file again.
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.. code:: json
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@ -76,7 +77,7 @@ For this, you will need to modify the Simulation Settings JSON file.
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}
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In this file you will define the different models you are going to simulate. You can simulate as many models
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as you want.
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as you want. Each model will be simulated separately.
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After setting up the models, you have to initialize the parameters of each one. You will find the parameters needed
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in the documentation of each model.
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@ -90,7 +91,7 @@ After setting all the configuration, you will be able to run the simulation. All
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.. code:: bash
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python soil.py
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python3 soil.py
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The simulation will return a dynamic graph .gexf file which could be visualized with
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`Gephi <https://gephi.org/users/download/>`__.
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docs/_build/html/models.html
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@ -51,7 +51,7 @@
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In practice, a model consists of at least two parts:</p>
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<ul class="simple">
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<li>Python module: the actual code that describes the behaviour.</li>
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<li>Setting up the variables in the Simulation Settings JSON file.</li>
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<li>Setting up the variables in the Settings JSON file.</li>
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</ul>
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<p>This separation allows us to run the simulation with different agents.</p>
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</div>
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@ -63,10 +63,10 @@ In practice, a model consists of at least two parts:</p>
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<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>
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<span class="n">sentimentCorrelationNodeArray</span> <span class="o">=</span> <span class="p">[]</span>
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<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>
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<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">network_params</span><span class="p">[</span><span class="s2">"number_of_nodes"</span><span class="p">]):</span>
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<span class="n">sentimentCorrelationNodeArray</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'id'</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
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<span class="c1"># Initialize agent states. Let's assume everyone is normal.</span>
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<span class="n">init_states</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'id'</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>
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<span class="n">init_states</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'id'</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">network_params</span><span class="p">[</span><span class="s2">"number_of_nodes"</span><span class="p">])]</span>
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<span class="c1"># add keys as as necessary, but "id" must always refer to that state category</span>
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</pre></div>
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</div>
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@ -114,7 +114,7 @@ passed as a parameter to the simulation.</p>
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</pre></div>
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</div>
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<p>In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
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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>
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The simulation returns one result for each model, executing each model separately. For the usage, see <a class="reference internal" href="usage.html"><span class="doc">Usage</span></a>.</p>
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</div>
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<div class="section" id="example-model">
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<h2>Example Model<a class="headerlink" href="#example-model" title="Permalink to this headline">¶</a></h2>
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@ -1 +1 @@
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|
23
docs/_build/html/usage.html
vendored
23
docs/_build/html/usage.html
vendored
@ -50,17 +50,18 @@
|
||||
<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>
|
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<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>
|
||||
<li><p class="first">In the Settings JSON file you will find the configuration of the network.</p>
|
||||
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
|
||||
<span class="s2">"network_type"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
|
||||
<span class="s2">"number_of_nodes"</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span>
|
||||
<span class="s2">"max_time"</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
|
||||
<span class="s2">"num_trials"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
|
||||
<span class="s2">"timeout"</span><span class="p">:</span> <span class="mi">2</span>
|
||||
<span class="p">}</span>
|
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</pre></div>
|
||||
</div>
|
||||
</li>
|
||||
<li><p class="first">In the Simulation Settings JSON file, you will find the configuration of the models.</p>
|
||||
<li><p class="first">In the Settings JSON file, you will also find the configuration of the models.</p>
|
||||
</li>
|
||||
</ul>
|
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</div>
|
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@ -80,7 +81,7 @@
|
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<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>
|
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For this, you will need to modify the Settings JSON file again.</p>
|
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<div class="code json highlight-default"><div class="highlight"><pre><span></span><span class="p">{</span>
|
||||
<span class="s2">"agent"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"SISaModel"</span><span class="p">,</span><span class="s2">"ControlModelM2"</span><span class="p">],</span>
|
||||
|
||||
@ -114,7 +115,7 @@ For this, you will need to modify the Simulation Settings JSON file.</p>
|
||||
</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>
|
||||
as you want. Each model will be simulated separately.</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>
|
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@ -122,7 +123,7 @@ in the documentation of each model.</p>
|
||||
<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>
|
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<p>After setting all the configuration, you will be able to run the simulation. All you need to do is execute:</p>
|
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<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>
|
||||
<div class="code bash highlight-default"><div class="highlight"><pre><span></span><span class="n">python3</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
|
||||
|
@ -9,7 +9,7 @@ A model defines the behaviour of the agents with a view to assessing their effec
|
||||
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.
|
||||
* Setting up the variables in the Settings JSON file.
|
||||
|
||||
This separation allows us to run the simulation with different agents.
|
||||
|
||||
@ -25,10 +25,10 @@ All the models are imported to the main file. The initialization look like this:
|
||||
networkStatus = {} # Dict that will contain the status of every agent in the network
|
||||
|
||||
sentimentCorrelationNodeArray = []
|
||||
for x in range(0, settings.number_of_nodes):
|
||||
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.number_of_nodes)]
|
||||
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.
|
||||
@ -77,7 +77,7 @@ passed as a parameter to the simulation.
|
||||
}
|
||||
|
||||
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`.
|
||||
The simulation returns one result for each model, executing each model separately. For the usage, see :doc:`usage`.
|
||||
|
||||
Example Model
|
||||
=============
|
||||
|
@ -8,18 +8,19 @@ 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.
|
||||
* In the Settings JSON 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
|
||||
{
|
||||
"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.
|
||||
* In the Settings JSON file, you will also find the configuration of the models.
|
||||
|
||||
Network Types
|
||||
=============
|
||||
@ -40,7 +41,7 @@ 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.
|
||||
For this, you will need to modify the Settings JSON file again.
|
||||
|
||||
.. code:: json
|
||||
|
||||
@ -76,7 +77,7 @@ For this, you will need to modify the Simulation Settings JSON file.
|
||||
}
|
||||
|
||||
In this file you will define the different models you are going to simulate. You can simulate as many models
|
||||
as you want.
|
||||
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.
|
||||
@ -90,7 +91,7 @@ After setting all the configuration, you will be able to run the simulation. All
|
||||
|
||||
.. code:: bash
|
||||
|
||||
python soil.py
|
||||
python3 soil.py
|
||||
|
||||
The simulation will return a dynamic graph .gexf file which could be visualized with
|
||||
`Gephi <https://gephi.org/users/download/>`__.
|
||||
|
@ -23,7 +23,7 @@ class BaseBehaviour(BaseNetworkAgent):
|
||||
def run(self):
|
||||
while True:
|
||||
self.step(self.env.now)
|
||||
yield self.env.timeout(settings.timeout)
|
||||
yield self.env.timeout(settings.network_params["timeout"])
|
||||
|
||||
def step(self, now):
|
||||
networkStatus['agent_%s'% self.id] = self.to_json()
|
||||
|
@ -24,12 +24,12 @@ class ControlModelM2(BaseBehaviour):
|
||||
"""
|
||||
|
||||
# Init infected
|
||||
init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 1}
|
||||
init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 1}
|
||||
init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 1}
|
||||
init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 1}
|
||||
|
||||
# Init beacons
|
||||
init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
|
||||
init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
|
||||
init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 4}
|
||||
init_states[random.randint(0, settings.network_params["number_of_nodes"]-1)] = {'id': 4}
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
|
@ -23,8 +23,8 @@ class SpreadModelM2(BaseBehaviour):
|
||||
prob_generate_anti_rumor
|
||||
"""
|
||||
|
||||
init_states[random.randint(0, settings.number_of_nodes)] = {'id': 1}
|
||||
init_states[random.randint(0, settings.number_of_nodes)] = {'id': 1}
|
||||
init_states[random.randint(0, settings.network_params["number_of_nodes"])] = {'id': 1}
|
||||
init_states[random.randint(0, settings.network_params["number_of_nodes"])] = {'id': 1}
|
||||
|
||||
def __init__(self, environment=None, agent_id=0, state=()):
|
||||
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
||||
|
@ -3,8 +3,8 @@ 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):
|
||||
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.number_of_nodes)]
|
||||
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
|
||||
|
@ -1,4 +1,13 @@
|
||||
{
|
||||
[
|
||||
{
|
||||
"network_type": 1,
|
||||
"number_of_nodes": 1000,
|
||||
"max_time": 50,
|
||||
"num_trials": 1,
|
||||
"timeout": 2
|
||||
},
|
||||
|
||||
{
|
||||
"agent": ["BaseBehaviour","SISaModel","ControlModelM2"],
|
||||
|
||||
|
||||
@ -49,4 +58,5 @@
|
||||
"prob_vaccinated_healing_infected": 0.035,
|
||||
"prob_vaccinated_vaccinate_neutral": 0.035,
|
||||
"prob_generate_anti_rumor": 0.035
|
||||
}
|
||||
}
|
||||
]
|
12
settings.py
12
settings.py
@ -1,15 +1,11 @@
|
||||
# General configuration
|
||||
import json
|
||||
|
||||
# Network settings
|
||||
network_type = 1
|
||||
number_of_nodes = 1000
|
||||
max_time = 50
|
||||
num_trials = 1
|
||||
timeout = 2
|
||||
with open('settings.json', 'r') as f:
|
||||
settings = json.load(f)
|
||||
|
||||
with open('simulation_settings.json', 'r') as f:
|
||||
environment_params = json.load(f)
|
||||
network_params = settings[0]
|
||||
environment_params = settings[1]
|
||||
|
||||
|
||||
'''
|
||||
|
30
soil.py
30
soil.py
@ -1,6 +1,6 @@
|
||||
from models import *
|
||||
from nxsim import NetworkSimulation
|
||||
import numpy
|
||||
# import numpy
|
||||
from matplotlib import pyplot as plt
|
||||
import networkx as nx
|
||||
import settings
|
||||
@ -15,7 +15,7 @@ import json
|
||||
|
||||
def visualization(graph_name):
|
||||
|
||||
for x in range(0, settings.number_of_nodes):
|
||||
for x in range(0, settings.network_params["number_of_nodes"]):
|
||||
for attribute in models.networkStatus["agent_%s" % x]:
|
||||
emotionStatusAux = []
|
||||
for t_step in models.networkStatus["agent_%s" % x][attribute]:
|
||||
@ -46,14 +46,14 @@ def results(model_name):
|
||||
vaccinated_values = []
|
||||
|
||||
attribute_plot = 'status'
|
||||
for time in range(0, settings.max_time):
|
||||
for time in range(0, settings.network_params["max_time"]):
|
||||
value_infectados = 0
|
||||
value_neutral = 0
|
||||
value_cured = 0
|
||||
value_vaccinated = 0
|
||||
real_time = time * settings.timeout
|
||||
real_time = time * settings.network_params["timeout"]
|
||||
activity = False
|
||||
for x in range(0, settings.number_of_nodes):
|
||||
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] == 1: ## Infected
|
||||
@ -93,12 +93,12 @@ def results(model_name):
|
||||
# Network creation #
|
||||
####################
|
||||
|
||||
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)
|
||||
if settings.network_params["network_type"] == 0:
|
||||
G = nx.complete_graph(settings.network_params["number_of_nodes"])
|
||||
if settings.network_params["network_type"] == 1:
|
||||
G = nx.barabasi_albert_graph(settings.network_params["number_of_nodes"], 10)
|
||||
if settings.network_params["network_type"] == 2:
|
||||
G = nx.margulis_gabber_galil_graph(settings.network_params["number_of_nodes"], None)
|
||||
# More types of networks can be added here
|
||||
|
||||
|
||||
@ -112,16 +112,16 @@ 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)
|
||||
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))
|
||||
visualization(str(agent))
|
||||
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)
|
||||
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))
|
||||
visualization(str(agent))
|
||||
|
Loading…
Reference in New Issue
Block a user