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index 0000000..9ac7f64
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index eca20b9..b32a537 100644
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index b08829a..b65b5a8 100644
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diff --git a/docs/_build/doctrees/models.doctree b/docs/_build/doctrees/models.doctree
new file mode 100644
index 0000000..1b705ba
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diff --git a/docs/_build/doctrees/usage.doctree b/docs/_build/doctrees/usage.doctree
new file mode 100644
index 0000000..363cdae
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diff --git a/docs/_build/html/_sources/demo.rst.txt b/docs/_build/html/_sources/demo.rst.txt
new file mode 100644
index 0000000..e69de29
diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt
index 709d3c2..6d96db2 100644
--- a/docs/_build/html/_sources/index.rst.txt
+++ b/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`
-* :ref:`modindex`
-* :ref:`search`
+.. Indices and tables
+ ==================
+ * :ref:`genindex`
+ * :ref:`modindex`
+ * :ref:`search`
diff --git a/docs/_build/html/_sources/models.rst.txt b/docs/_build/html/_sources/models.rst.txt
new file mode 100644
index 0000000..654effb
--- /dev/null
+++ b/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.
\ No newline at end of file
diff --git a/docs/_build/html/_sources/usage.rst.txt b/docs/_build/html/_sources/usage.rst.txt
new file mode 100644
index 0000000..4d61fe2
--- /dev/null
+++ b/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 `__.
+
+It will also return one .png picture for each model simulated.
diff --git a/docs/_build/html/demo.html b/docs/_build/html/demo.html
new file mode 100644
index 0000000..648b604
--- /dev/null
+++ b/docs/_build/html/demo.html
@@ -0,0 +1,96 @@
+
+
+
+
+
+
+
+ <no title> — Soil 0.1 documentation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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.
All the models are imported to the main file. The initialization look like this:
+
importsettings
+
+networkStatus={}# Dict that will contain the status of every agent in the network
+
+sentimentCorrelationNodeArray=[]
+forxinrange(0,settings.number_of_nodes):
+ sentimentCorrelationNodeArray.append({'id':x})
+# Initialize agent states. Let's assume everyone is normal.
+init_states=[{'id':0,}for_inrange(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:
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.
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 Usage.
There are three types of network implemented, but you could add more.
+
ifsettings.network_type==0:
+ G=nx.complete_graph(settings.number_of_nodes)
+ifsettings.network_type==1:
+ G=nx.barabasi_albert_graph(settings.number_of_nodes,10)
+ifsettings.network_type==2:
+ G=nx.margulis_gabber_galil_graph(settings.number_of_nodes,None)
+# More types of networks can be added here
+
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.
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.
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/index.rst b/docs/index.rst
index 709d3c2..6d96db2 100644
--- a/docs/index.rst
+++ b/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`
-* :ref:`modindex`
-* :ref:`search`
+.. Indices and tables
+ ==================
+ * :ref:`genindex`
+ * :ref:`modindex`
+ * :ref:`search`
diff --git a/docs/models.rst b/docs/models.rst
new file mode 100644
index 0000000..654effb
--- /dev/null
+++ b/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.
\ No newline at end of file
diff --git a/docs/usage.rst b/docs/usage.rst
new file mode 100644
index 0000000..4d61fe2
--- /dev/null
+++ b/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 `__.
+
+It will also return one .png picture for each model simulated.
diff --git a/settings.py b/settings.py
index e4d1806..1b1f531 100644
--- a/settings.py
+++ b/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
diff --git a/simulation_settings.json b/simulation_settings.json
index e162f12..ac9bcd0 100644
--- a/simulation_settings.json
+++ b/simulation_settings.json
@@ -1,5 +1,4 @@
{
-
"agent": ["BaseBehaviour","SISaModel","ControlModelM2"],
diff --git a/soil.py b/soil.py
index ae4c537..332667a 100644
--- a/soil.py
+++ b/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))
-
-
-
-
-
-