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J. Fernando Sánchez
2017-10-16 19:23:52 +02:00
parent dbc182c6d0
commit af76f54a28
11 changed files with 343 additions and 89 deletions
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docs/quickstart.rst
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@@ -13,7 +13,7 @@ Here's an example (``example.yml``).
name: MyExampleSimulation
max_time: 50
num_trials: 3
timeout: 2
interval: 2
network_params:
network_type: barabasi_albert_graph
n: 100
@@ -34,6 +34,12 @@ Here's an example (``example.yml``).
environment_params:
prob_infect: 0.075
This example configuration will run three trials of a simulation containing a randomly generated network.
The 100 nodes in the network will be SISaModel agents, 10% of them will start in the content state, 10% in the discontent state, and the remaining 80% in the neutral state.
All agents will have access to the environment, which only contains one variable, ``prob_infected``.
The state of the agents will be updated every 2 seconds (``interval``).
Now run the simulation with the command line tool:
.. code:: bash
@@ -41,7 +47,7 @@ Now run the simulation with the command line tool:
soil example.yml
Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
Four types of objects are saved by default: a pickle of the simulation, a ``YAML`` representation of the simulation (to re-launch it), for every trial, a csv file with the content of the state of every network node and the environment parameters at every step of the simulation as well as the network in gephi format (``gexf``).
Four types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a csv file with the content of the state of every network node and the environment parameters at every step of the simulation, as well as the network in gephi format (``gexf``).
.. code::
@@ -54,12 +60,6 @@ Four types of objects are saved by default: a pickle of the simulation, a ``YAML
│   └── Sim_prob_0_trial_0.gexf
This example configuration will run three trials of a simulation containing a randomly generated network.
The 100 nodes in the network will be SISaModel agents, 10% of them will start in the content state, 10% in the discontent state, and the remaining 80% in the neutral state.
All agents will have access to the environment, which only contains one variable, ``prob_infected``.
The state of the agents will be updated every 2 seconds (``timeout``).
Network
=======
@@ -94,7 +94,7 @@ For example, the following configuration is equivalent to :code:`nx.complete_gra
Environment
============
The environment is the place where the shared state of the simulation is stored.
For instance, the probability of certain events.
For instance, the probability of disease outbreak.
The configuration file may specify the initial value of the environment parameters:
.. code:: yaml
@@ -103,14 +103,17 @@ The configuration file may specify the initial value of the environment paramete
daily_probability_of_earthquake: 0.001
number_of_earthquakes: 0
Any agent has unrestricted access to the environment.
However, for the sake of simplicity, we recommend limiting environment updates to environment agents.
Agents
======
Agents are a way of modelling behavior.
Agents can be characterized with two variables: an agent type (``agent_type``) and its state.
Only one agent is executed at a time (generally, every ``timeout`` seconds), and it has access to its state and the environment parameters.
Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters.
Through the environment, it can access the network topology and the state of other agents.
There are three three types of agents according to how they are added to the simulation: network agents, environment agent, and other agents.
There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
Network Agents
##############
@@ -118,13 +121,13 @@ Network agents are attached to a node in the topology.
The configuration file allows you to specify how agents will be mapped to topology nodes.
The simplest way is to specify a single type of agent.
Hence, every node in the network will have an associated agent of that type.
Hence, every node in the network will be associated to an agent of that type.
.. code:: yaml
agent_type: SISaModel
It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (``weight``).
It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property).
For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
.. code:: yaml