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test:
docker-compose exec dev python -m pytest -s -v
.PHONY: test

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Configuring a simulation
------------------------
There are two ways to configure a simulation: programmatically and with a configuration file.
In both cases, the parameters used are the same.
The advantage of a configuration file is that it is a clean declarative description, and it makes it easier to reproduce.
Simulation configuration files can be formatted in ``json`` or ``yaml`` and they define all the parameters of a simulation.
Here's an example (``example.yml``).
.. code:: yaml
---
name: MyExampleSimulation
max_time: 50
num_trials: 3
interval: 2
network_params:
generator: barabasi_albert_graph
n: 100
m: 2
network_agents:
- agent_type: SISaModel
weight: 1
state:
id: content
- agent_type: SISaModel
weight: 1
state:
id: discontent
- agent_type: SISaModel
weight: 8
state:
id: neutral
environment_params:
prob_infect: 0.075
This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil.
10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
The state of the agents will be updated every 2 seconds (``interval``).
Now run the simulation with the command line tool:
.. code:: bash
soil example.yml
Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
Three types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a ``sqlite`` file with the content of the state of every network node and the environment parameters at every step of the simulation.
.. code::
soil_output
└── MyExampleSimulation
├── MyExampleSimulation.dumped.yml
├── MyExampleSimulation.simulation.pickle
├── MyExampleSimulation_trial_0.db.sqlite
├── MyExampleSimulation_trial_1.db.sqlite
└── MyExampleSimulation_trial_2.db.sqlite
You may also ask soil to export the states in a ``csv`` file, and the network in gephi format (``gexf``).
Network
=======
The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx.
Loading a network
#################
To load an existing network, specify its path in the configuration:
.. code:: yaml
---
network_params:
path: /tmp/mynetwork.gexf
Soil will try to guess what networkx method to use to read the file based on its extension.
However, we only test using ``gexf`` files.
For simple networks, you may also include them in the configuration itself using , using the ``topology`` parameter like so:
.. code:: yaml
---
topology:
nodes:
- id: First
- id: Second
links:
- source: First
target: Second
Generating a random network
###########################
To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm <https://networkx.github.io/documentation/development/reference/generators.html>`_ and other parameters.
For example, the following configuration is equivalent to :code:`nx.complete_graph(n=100)`:
.. code:: yaml
network_params:
generator: complete_graph
n: 100
Environment
============
The environment is the place where the shared state of the simulation is stored.
For instance, the probability of disease outbreak.
The configuration file may specify the initial value of the environment parameters:
.. code:: yaml
environment_params:
daily_probability_of_earthquake: 0.001
number_of_earthquakes: 0
All agents have access to the environment parameters.
In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.
Agents
======
Agents are a way of modelling behavior.
Agents can be characterized with two variables: agent type (``agent_type``) and state.
Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters.
Through the environment, it can access the network topology and the state of other agents.
There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
Network Agents
##############
Network agents are attached to a node in the topology.
The configuration file allows you to specify how agents will be mapped to topology nodes.
The simplest way is to specify a single type of agent.
Hence, every node in the network will be associated to an agent of that type.
.. code:: yaml
agent_type: SISaModel
It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property).
For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
.. code:: yaml
network_agents:
- agent_type: SISaModel
weight: 1
- agent_type: CounterModel
weight: 5
The third option is to specify the type of agent on the node itself, e.g.:
.. code:: yaml
topology:
nodes:
- id: first
agent_type: BaseAgent
states:
first:
agent_type: SISaModel
This would also work with a randomly generated network:
.. code:: yaml
network:
generator: complete
n: 5
agent_type: BaseAgent
states:
- agent_type: SISaModel
In addition to agent type, you may add a custom initial state to the distribution.
This is very useful to add the same agent type with different states.
e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state:
.. code:: yaml
network_agents:
- agent_type: SISaModel
weight: 9
state:
id: neutral
- agent_type: SISaModel
weight: 1
state:
id: discontent
Lastly, the configuration may include initial state for one or more nodes.
For instance, to add a state for the two nodes in this configuration:
.. code:: yaml
agent_type: SISaModel
network:
generator: complete_graph
n: 2
states:
- id: content
- id: discontent
Or to add state only to specific nodes (by ``id``).
For example, to apply special skills to Linux Torvalds in a simulation:
.. literalinclude:: ../examples/torvalds.yml
:language: yaml
Environment Agents
##################
In addition to network agents, more agents can be added to the simulation.
These agents are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes.
.. code::
environment_agents:
- agent_type: MyAgent
state:
mood: happy
- agent_type: DummyAgent
You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance.
They are also useful to add behavior that has little to do with the network and the interactions within that network.

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Welcome to Soil's documentation!
================================
Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks.
Soil is an Agent-based Social Simulator in Python focused on Social Networks.
If you use Soil in your research, do not forget to cite this paper:
@ -39,6 +39,7 @@ If you use Soil in your research, do not forget to cite this paper:
installation
quickstart
configuration
Tutorial <soil_tutorial>
..

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Quickstart
----------
This section shows how to run simulations from simulation configuration files.
First of all, you need to install the package (See :doc:`installation`)
This section shows how to run your first simulation with Soil.
For installation instructions, see :doc:`installation`.
Simulation configuration files are ``json`` or ``yaml`` files that define all the parameters of a simulation.
Here's an example (``example.yml``).
.. code:: yaml
---
name: MyExampleSimulation
max_time: 50
num_trials: 3
interval: 2
network_params:
network_type: barabasi_albert_graph
n: 100
m: 2
agent_distribution:
- agent_type: SISaModel
weight: 1
state:
id: content
- agent_type: SISaModel
weight: 1
state:
id: discontent
- agent_type: SISaModel
weight: 8
state:
id: neutral
environment_params:
prob_infect: 0.075
There are mainly two parts in a simulation: agent classes and simulation configuration.
An agent class defines how the agent will behave throughout the simulation.
The configuration includes things such as number of agents to use and their type, network topology to use, etc.
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
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 (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``).
.. image:: soil.png
:width: 80%
:align: center
.. code::
Soil includes several agent classes in the ``soil.agents`` module, and we will use them in this quickstart.
If you are interested in developing your own agents classes, see :doc:`soil_tutorial`.
The configuration is the following:
soil_output
├── Sim_prob_0
│   ├── Sim_prob_0.dumped.yml
│   ├── Sim_prob_0.simulation.pickle
│   ├── Sim_prob_0_trial_0.environment.csv
│   └── Sim_prob_0_trial_0.gexf
Network
=======
The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx.
Loading a network
#################
To load an existing network, specify its path in the configuration:
.. code:: yaml
---
network_params:
path: /tmp/mynetwork.gexf
Soil will try to guess what networkx method to use to read the file based on its extension.
However, we only test using ``gexf`` files.
Generating a random network
###########################
To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm <https://networkx.github.io/documentation/development/reference/generators.html>`_ and other parameters.
For example, the following configuration is equivalent to :code:`nx.complete_graph(100)`:
.. code:: yaml
network_params:
network_type: complete_graph
n: 100
Environment
============
The environment is the place where the shared state of the simulation is stored.
For instance, the probability of disease outbreak.
The configuration file may specify the initial value of the environment parameters:
.. code:: yaml
environment_params:
daily_probability_of_earthquake: 0.001
number_of_earthquakes: 0
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 ``interval`` seconds), and it has access to its state and the environment parameters.
Through the environment, it can access the network topology and the state of other agents.
There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
Network Agents
##############
Network agents are attached to a node in the topology.
The configuration file allows you to specify how agents will be mapped to topology nodes.
The simplest way is to specify a single type of agent.
Hence, every node in the network will be associated to an agent of that type.
.. code:: yaml
agent_type: SISaModel
It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property).
For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
.. code:: yaml
agent_distribution:
- agent_type: SISaModel
weight: 1
- agent_type: CounterModel
weight: 5
In addition to agent type, you may also add a custom initial state to the distribution.
This is very useful to add the same agent type with different states.
e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state:
.. code:: yaml
agent_distribution:
- agent_type: SISaModel
weight: 9
state:
id: neutral
- agent_type: SISaModel
weight: 1
state:
id: discontent
Lastly, the configuration may include initial state for one or more nodes.
For instance, to add a state for the two nodes in this configuration:
.. code:: yaml
agent_type: SISaModel
network:
network_type: complete_graph
n: 2
states:
- id: content
- id: discontent
Or to add state only to specific nodes (by ``id``).
For example, to apply special skills to Linux Torvalds in a simulation:
.. literalinclude:: ../examples/torvalds.yml
.. literalinclude:: quickstart.yml
:language: yaml
Environment Agents
##################
In addition to network agents, more agents can be added to the simulation.
These agens are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes.
You may :download:`download the file <quickstart.yml>` directly.
The agent type used, SISa, is a very simple model.
It only has three states (neutral, content and discontent),
Its parameters are the probabilities to change from one state to another, either spontaneously or because of contagion from neighboring agents.
To see the simulation in action, simply point soil to the configuration, and tell it to store the graph and the history of agent states and environment parameters at every point.
.. code::
environment_agents:
- agent_type: MyAgent
state:
mood: happy
- agent_type: DummyAgent
❯ soil --graph --csv quickstart.yml [13:35:29]
INFO:soil:Using config(s): quickstart
INFO:soil:Dumping results to soil_output/quickstart : ['csv', 'gexf']
INFO:soil:Starting simulation quickstart at 13:35:30.
INFO:soil:Starting Simulation quickstart trial 0 at 13:35:30.
INFO:soil:Finished Simulation quickstart trial 0 at 13:35:49 in 19.43677067756653 seconds
INFO:soil:Starting Dumping simulation quickstart trial 0 at 13:35:49.
INFO:soil:Finished Dumping simulation quickstart trial 0 at 13:35:51 in 1.7733407020568848 seconds
INFO:soil:Dumping results to soil_output/quickstart
INFO:soil:Finished simulation quickstart at 13:35:51 in 21.29862952232361 seconds
Visualizing the results
=======================
The ``CSV`` file should look like this:
The simulation will return a dynamic graph .gexf file which could be visualized with
.. code::
agent_id,t_step,key,value
env,0,neutral_discontent_spon_prob,0.05
env,0,neutral_discontent_infected_prob,0.1
env,0,neutral_content_spon_prob,0.2
env,0,neutral_content_infected_prob,0.4
env,0,discontent_neutral,0.2
env,0,discontent_content,0.05
env,0,content_discontent,0.05
env,0,variance_d_c,0.05
env,0,variance_c_d,0.1
The environment variables are marked as ``agent_id`` env.
Th exported values are only stored when they change.
To find out how to get every key and value at every point in the simulation, check out the :doc:`soil_tutorial`.
The dynamic graph is exported as a .gexf file which could be visualized with
`Gephi <https://gephi.org/users/download/>`__.
Now it is your turn to experiment with the simulation.
Change some of the parameters, such as the number of agents, the probability of becoming content, or the type of network, and see how the results change.

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---
name: quickstart
num_trials: 1
max_time: 1000
network_agents:
- agent_type: SISaModel
state:
id: neutral
weight: 1
- agent_type: SISaModel
state:
id: content
weight: 2
network_params:
n: 100
k: 5
p: 0.2
generator: newman_watts_strogatz_graph
environment_params:
neutral_discontent_spon_prob: 0.05
neutral_discontent_infected_prob: 0.1
neutral_content_spon_prob: 0.2
neutral_content_infected_prob: 0.4
discontent_neutral: 0.2
discontent_content: 0.05
content_discontent: 0.05
variance_d_c: 0.05
variance_c_d: 0.1
content_neutral: 0.1
standard_variance: 0.1

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@ -214,7 +214,7 @@ nodes in that network. Notice how node 0 is the only one with a TV.
MAX_TIME = 100
EVENT_TIME = 10
sim = soil.simulation.SoilSimulation(topology=G,
sim = soil.Simulation(topology=G,
num_trials=1,
max_time=MAX_TIME,
environment_agents=[{'agent_type': NewsEnvironmentAgent,

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@ -2,14 +2,22 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:41.843Z"
},
"scrolled": false
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import soil\n",
"import networkx as nx\n",
@ -39,26 +47,216 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 288K\r\n",
"drwxr-xr-x 7 j users 4.0K May 23 12:48 .\r\n",
"drwxr-xr-x 15 j users 20K May 7 18:59 ..\r\n",
"-rw-r--r-- 1 j users 451 Oct 17 2017 complete.yml\r\n",
"drwxr-xr-x 2 j users 4.0K Feb 18 11:22 .ipynb_checkpoints\r\n",
"drwxr-xr-x 2 j users 4.0K Oct 17 2017 long_running\r\n",
"-rw-r--r-- 1 j users 1.2K May 23 12:49 .nbgrader.log\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:23 newsspread\r\n",
"-rw-r--r-- 1 j users 225K May 4 11:23 NewsSpread.ipynb\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:21 rabbits\r\n",
"-rw-r--r-- 1 j users 42 Jul 3 2017 torvalds.edgelist\r\n",
"-rw-r--r-- 1 j users 245 Oct 13 2017 torvalds.yml\r\n",
"drwxr-xr-x 4 j users 4.0K May 4 11:23 tutorial\r\n"
]
}
],
"source": [
"!ls "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:43.440Z"
}
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_all_dumb\r\n",
"network_agents:\r\n",
"- agent_type: DumbViewer\r\n",
" state:\r\n",
" has_tv: false\r\n",
" weight: 1\r\n",
"- agent_type: DumbViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_half_herd\r\n",
"network_agents:\r\n",
"- agent_type: DumbViewer\r\n",
" state:\r\n",
" has_tv: false\r\n",
" weight: 1\r\n",
"- agent_type: DumbViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"- agent_type: HerdViewer\r\n",
" state:\r\n",
" has_tv: false\r\n",
" weight: 1\r\n",
"- agent_type: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_all_herd\r\n",
"network_agents:\r\n",
"- agent_type: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"- agent_type: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
" prob_neighbor_cure: 0.1\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_wise_herd\r\n",
"network_agents:\r\n",
"- agent_type: HerdViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"- agent_type: WiseViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n",
"---\r\n",
"default_state: {}\r\n",
"load_module: newsspread\r\n",
"environment_agents: []\r\n",
"environment_params:\r\n",
" prob_neighbor_spread: 0.0\r\n",
" prob_tv_spread: 0.01\r\n",
" prob_neighbor_cure: 0.1\r\n",
"interval: 1\r\n",
"max_time: 30\r\n",
"name: Sim_all_wise\r\n",
"network_agents:\r\n",
"- agent_type: WiseViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" id: neutral\r\n",
" weight: 1\r\n",
"- agent_type: WiseViewer\r\n",
" state:\r\n",
" has_tv: true\r\n",
" weight: 1\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"network_params:\r\n",
" generator: barabasi_albert_graph\r\n",
" n: 500\r\n",
" m: 5\r\n",
"num_trials: 50\r\n"
]
}
],
"source": [
"!cat NewsSpread.yml"
"!cat newsspread/NewsSpread.yml"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"start_time": "2017-11-02T09:48:43.879Z"
}
},
"outputs": [],
"outputs": [
{
"ename": "ValueError",
"evalue": "No objects to concatenate",
"output_type": "error",
"traceback": [
"\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-bae848826594>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevodumb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'soil_output/Sim_all_dumb/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0miterable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgroup_trials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreorder_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m copy=copy)\n\u001b[0m\u001b[1;32m 213\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No objects to concatenate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
]
}
],
"source": [
"evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);"
]
@ -302,7 +500,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
"version": "3.6.5"
},
"toc": {
"colors": {

80808
examples/Untitled.ipynb Normal file
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File diff suppressed because it is too large Load Diff

2
examples/complete.yml
View File

@ -2,6 +2,7 @@
name: simple
dir_path: "/tmp/"
num_trials: 3
dry_run: True
max_time: 100
interval: 1
seed: "CompleteSeed!"
@ -17,6 +18,7 @@ network_agents:
- agent_type: AggregatedCounter
weight: 0.2
environment_agents: []
environment_class: Environment
environment_params:
am_i_complete: true
default_state:

10
examples/pubcrawl/README.md Normal file
View File

@ -0,0 +1,10 @@
Simulation of pubs and drinking pals that go from pub to pub.
Th custom environment includes a list of pubs and methods to allow agents to discover and enter pubs.
There are two types of agents:
* Patron. A patron will do three things, in this order:
* Look for other patrons to drink with
* Look for a pub where the agent and other agents in the same group can get in.
* While in the pub, patrons only drink, until they get drunk and taken home.
* Police. There is only one police agent that will take any drunk patrons home (kick them out of the pub).

174
examples/pubcrawl/pubcrawl.py Normal file
View File

@ -0,0 +1,174 @@
from soil.agents import FSM, state, default_state
from soil import Environment
from random import random, shuffle
from itertools import islice
import logging
class CityPubs(Environment):
'''Environment with Pubs'''
level = logging.INFO
def __init__(self, *args, number_of_pubs=3, pub_capacity=10, **kwargs):
super(CityPubs, self).__init__(*args, **kwargs)
pubs = {}
for i in range(number_of_pubs):
newpub = {
'name': 'The awesome pub #{}'.format(i),
'open': True,
'capacity': pub_capacity,
'occupancy': 0,
}
pubs[newpub['name']] = newpub
self['pubs'] = pubs
def enter(self, pub_id, *nodes):
'''Agents will try to enter. The pub checks if it is possible'''
try:
pub = self['pubs'][pub_id]
except KeyError:
raise ValueError('Pub {} is not available'.format(pub_id))
if not pub['open'] or (pub['capacity'] < (len(nodes) + pub['occupancy'])):
return False
pub['occupancy'] += len(nodes)
for node in nodes:
node['pub'] = pub_id
return True
def available_pubs(self):
for pub in self['pubs'].values():
if pub['open'] and (pub['occupancy'] < pub['capacity']):
yield pub['name']
def exit(self, pub_id, *node_ids):
'''Agents will notify the pub they want to leave'''
try:
pub = self['pubs'][pub_id]
except KeyError:
raise ValueError('Pub {} is not available'.format(pub_id))
for node_id in node_ids:
node = self.get_agent(node_id)
if pub_id == node['pub']:
del node['pub']
pub['occupancy'] -= 1
class Patron(FSM):
'''Agent that looks for friends to drink with. It will do three things:
1) Look for other patrons to drink with
2) Look for a bar where the agent and other agents in the same group can get in.
3) While in the bar, patrons only drink, until they get drunk and taken home.
'''
level = logging.INFO
defaults = {
'pub': None,
'drunk': False,
'pints': 0,
'max_pints': 3,
}
@default_state
@state
def looking_for_friends(self):
'''Look for friends to drink with'''
self.info('I am looking for friends')
available_friends = list(self.get_agents(drunk=False,
pub=None,
state_id=self.looking_for_friends.id))
if not available_friends:
self.info('Life sucks and I\'m alone!')
return self.at_home
befriended = self.try_friends(available_friends)
if befriended:
return self.looking_for_pub
@state
def looking_for_pub(self):
'''Look for a pub that accepts me and my friends'''
if self['pub'] != None:
return self.sober_in_pub
self.debug('I am looking for a pub')
group = list(self.get_neighboring_agents())
for pub in self.env.available_pubs():
self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group)))
if self.env.enter(pub, self, *group):
self.info('We\'re all {} getting in {}!'.format(len(group), pub))
return self.sober_in_pub
@state
def sober_in_pub(self):
'''Drink up.'''
self.drink()
if self['pints'] > self['max_pints']:
return self.drunk_in_pub
@state
def drunk_in_pub(self):
'''I'm out. Take me home!'''
self.info('I\'m so drunk. Take me home!')
self['drunk'] = True
pass # out drunk
@state
def at_home(self):
'''The end'''
self.debug('Life sucks. I\'m home!')
def drink(self):
self['pints'] += 1
self.debug('Cheers to that')
def kick_out(self):
self.set_state(self.at_home)
def befriend(self, other_agent, force=False):
'''
Try to become friends with another agent. The chances of
success depend on both agents' openness.
'''
if force or self['openness'] > random():
self.env.add_edge(self, other_agent)
self.info('Made some friend {}'.format(other_agent))
return True
return False
def try_friends(self, others):
''' Look for random agents around me and try to befriend them'''
befriended = False
k = int(10*self['openness'])
shuffle(others)
for friend in islice(others, k): # random.choice >= 3.7
if friend == self:
continue
if friend.befriend(self):
self.befriend(friend, force=True)
self.debug('Hooray! new friend: {}'.format(friend.id))
befriended = True
else:
self.debug('{} does not want to be friends'.format(friend.id))
return befriended
class Police(FSM):
'''Simple agent to take drunk people out of pubs.'''
level = logging.INFO
@default_state
@state
def patrol(self):
drunksters = list(self.get_agents(drunk=True,
state_id=Patron.drunk_in_pub.id))
for drunk in drunksters:
self.info('Kicking out the trash: {}'.format(drunk.id))
drunk.kick_out()
else:
self.info('No trash to take out. Too bad.')
if __name__ == '__main__':
from soil import simulation
simulation.run_from_config('pubcrawl.yml',
dry_run=True,
dump=None,
parallel=False)

26
examples/pubcrawl/pubcrawl.yml Normal file
View File

@ -0,0 +1,26 @@
---
name: pubcrawl
num_trials: 3
max_time: 10
dump: false
network_params:
# Generate 100 empty nodes. They will be assigned a network agent
generator: empty_graph
n: 30
network_agents:
- agent_type: pubcrawl.Patron
description: Extroverted patron
state:
openness: 1.0
weight: 9
- agent_type: pubcrawl.Patron
description: Introverted patron
state:
openness: 0.1
weight: 1
environment_agents:
- agent_type: pubcrawl.Police
environment_class: pubcrawl.CityPubs
environment_params:
altercations: 0
number_of_pubs: 3

2
examples/rabbits/rabbits.yml
View File

@ -1,7 +1,7 @@
---
load_module: rabbit_agents
name: rabbits_example
max_time: 1200
max_time: 500
interval: 1
seed: MySeed
agent_type: RabbitModel

4
examples/tutorial/soil_tutorial.html
View File

@ -12327,7 +12327,7 @@ Notice how node 0 is the only one with a TV.</p>
<span class="n">MAX_TIME</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">EVENT_TIME</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">sim</span> <span class="o">=</span> <span class="n">soil</span><span class="o">.</span><span class="n">simulation</span><span class="o">.</span><span class="n">SoilSimulation</span><span class="p">(</span><span class="n">topology</span><span class="o">=</span><span class="n">G</span><span class="p">,</span>
<span class="n">sim</span> <span class="o">=</span> <span class="n">soil</span><span class="o">.</span><span class="n">Simulation</span><span class="p">(</span><span class="n">topology</span><span class="o">=</span><span class="n">G</span><span class="p">,</span>
<span class="n">num_trials</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">max_time</span><span class="o">=</span><span class="n">MAX_TIME</span><span class="p">,</span>
<span class="n">environment_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
@ -21883,7 +21883,7 @@ bgAAAABJRU5ErkJggg==
<div class="output_subarea output_stream output_stdout output_text">
<pre>267M ../rabbits/soil_output/rabbits_example/
<pre>267M ../rabbits/soil_output/rabbits_example/
</pre>
</div>
</div>

2
examples/tutorial/soil_tutorial.ipynb
View File

@ -426,7 +426,7 @@
"MAX_TIME = 100\n",
"EVENT_TIME = 10\n",
"\n",
"sim = soil.simulation.SoilSimulation(topology=G,\n",
"sim = soil.Simulation(topology=G,\n",
" num_trials=1,\n",
" max_time=MAX_TIME,\n",
" environment_agents=[{'agent_type': NewsEnvironmentAgent,\n",

12
soil/__init__.py
View File

@ -14,12 +14,11 @@ except NameError:
logging.basicConfig()
from . import agents
from . import simulation
from . import environment
from .simulation import *
from .environment import Environment
from . import utils
from . import analysis
def main():
import argparse
from . import simulation
@ -46,11 +45,12 @@ def main():
args = parser.parse_args()
if args.module:
if os.getcwd() not in sys.path:
sys.path.append(os.getcwd())
if args.module:
importlib.import_module(args.module)
logging.info('Loading config file: {}'.format(args.file, args.output))
logging.info('Loading config file: {}'.format(args.file))
try:
dump = []
@ -64,7 +64,7 @@ def main():
dump=dump,
parallel=(not args.synchronous and not args.pdb),
results_dir=args.output)
except Exception as ex:
except Exception:
if args.pdb:
pdb.post_mortem()
else:

36
soil/agents/SISaModel.py
View File

@ -10,7 +10,7 @@ class SISaModel(FSM):
neutral_discontent_infected_prob
neutral_content_spong_prob
neutral_content_spon_prob
neutral_content_infected_prob
@ -29,27 +29,27 @@ class SISaModel(FSM):
standard_variance
"""
def __init__(self, environment=None, agent_id=0, state=()):
def __init__(self, environment, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state)
self.neutral_discontent_spon_prob = np.random.normal(environment.environment_params['neutral_discontent_spon_prob'],
environment.environment_params['standard_variance'])
self.neutral_discontent_infected_prob = np.random.normal(environment.environment_params['neutral_discontent_infected_prob'],
environment.environment_params['standard_variance'])
self.neutral_content_spon_prob = np.random.normal(environment.environment_params['neutral_content_spon_prob'],
environment.environment_params['standard_variance'])
self.neutral_content_infected_prob = np.random.normal(environment.environment_params['neutral_content_infected_prob'],
environment.environment_params['standard_variance'])
self.neutral_discontent_spon_prob = np.random.normal(self.env['neutral_discontent_spon_prob'],
self.env['standard_variance'])
self.neutral_discontent_infected_prob = np.random.normal(self.env['neutral_discontent_infected_prob'],
self.env['standard_variance'])
self.neutral_content_spon_prob = np.random.normal(self.env['neutral_content_spon_prob'],
self.env['standard_variance'])
self.neutral_content_infected_prob = np.random.normal(self.env['neutral_content_infected_prob'],
self.env['standard_variance'])
self.discontent_neutral = np.random.normal(environment.environment_params['discontent_neutral'],
environment.environment_params['standard_variance'])
self.discontent_content = np.random.normal(environment.environment_params['discontent_content'],
environment.environment_params['variance_d_c'])
self.discontent_neutral = np.random.normal(self.env['discontent_neutral'],
self.env['standard_variance'])
self.discontent_content = np.random.normal(self.env['discontent_content'],
self.env['variance_d_c'])
self.content_discontent = np.random.normal(environment.environment_params['content_discontent'],
environment.environment_params['variance_c_d'])
self.content_neutral = np.random.normal(environment.environment_params['content_neutral'],
environment.environment_params['standard_variance'])
self.content_discontent = np.random.normal(self.env['content_discontent'],
self.env['variance_c_d'])
self.content_neutral = np.random.normal(self.env['content_neutral'],
self.env['standard_variance'])
@state
def neutral(self):

69
soil/agents/__init__.py
View File

@ -16,23 +16,15 @@ from functools import wraps
from .. import utils, history
agent_types = {}
class MetaAgent(type):
def __init__(cls, name, bases, nmspc):
super(MetaAgent, cls).__init__(name, bases, nmspc)
agent_types[name] = cls
class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
class BaseAgent(nxsim.BaseAgent):
"""
A special simpy BaseAgent that keeps track of its state history.
"""
defaults = {}
def __init__(self, environment=None, agent_id=None, state=None,
def __init__(self, environment, agent_id=None, state=None,
name='network_process', interval=None, **state_params):
# Check for REQUIRED arguments
assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. '
@ -152,14 +144,18 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
def count_neighboring_agents(self, state_id=None):
return len(super().get_agents(state_id, limit_neighbors=True))
def get_agents(self, state_id=None, limit_neighbors=False, iterator=False, **kwargs):
def get_agents(self, state_id=None, agent_type=None, limit_neighbors=False, iterator=False, **kwargs):
agents = self.env.agents
if limit_neighbors:
agents = super().get_agents(state_id, limit_neighbors)
else:
agents = filter(lambda x: state_id is None or x.state.get('id', None) == state_id,
self.env.agents)
def matches_all(agent):
if state_id is not None:
if agent.state.get('id', None) != state_id:
return False
if agent_type is not None:
if type(agent) != agent_type:
return False
state = agent.state
for k, v in kwargs.items():
if state.get(k, None) != v:
@ -219,7 +215,7 @@ def default_state(func):
return func
class MetaFSM(MetaAgent):
class MetaFSM(type):
def __init__(cls, name, bases, nmspc):
super(MetaFSM, cls).__init__(name, bases, nmspc)
states = {}
@ -328,16 +324,42 @@ def calculate_distribution(network_agents=None,
return network_agents
def _serialize_distribution(network_agents):
d = _convert_agent_types(network_agents,
to_string=True)
def serialize_agent_type(agent_type):
if isinstance(agent_type, str):
return agent_type
type_name = agent_type.__name__
if type_name not in globals():
type_name = utils.name(agent_type)
return type_name
def serialize_distribution(network_agents):
'''
When serializing an agent distribution, remove the thresholds, in order
to avoid cluttering the YAML definition file.
'''
d = deepcopy(network_agents)
for v in d:
if 'threshold' in v:
del v['threshold']
v['agent_type'] = serialize_agent_type(v['agent_type'])
return d
def deserialize_type(agent_type, known_modules=[]):
if not isinstance(agent_type, str):
return agent_type
if agent_type in globals():
agent_type = globals()[agent_type]
else:
known = known_modules + ['soil.agents', 'soil.agents.custom' ]
agent_type = utils.deserializer(agent_type, known_modules=known)
return agent_type
def deserialize_distribution(ind):
d = deepcopy(ind)
for v in d:
v['agent_type'] = deserialize_type(v['agent_type'])
return d
@ -354,14 +376,9 @@ def _validate_states(states, topology):
def _convert_agent_types(ind, to_string=False):
'''Convenience method to allow specifying agents by class or class name.'''
d = deepcopy(ind)
for v in d:
agent_type = v['agent_type']
if to_string and not isinstance(agent_type, str):
v['agent_type'] = str(agent_type.__name__)
elif not to_string and isinstance(agent_type, str):
v['agent_type'] = agent_types[agent_type]
return d
if to_string:
return serialize_distribution(ind)
return deserialize_distribution(ind)
def _agent_from_distribution(distribution, value=-1):

2
soil/analysis.py
View File

@ -56,7 +56,7 @@ def read_csv(filename, keys=None, convert_types=False, **kwargs):
def convert_row(row):
row['value'] = utils.convert(row['value'], row['value_type'])
row['value'] = utils.deserialize(row['value_type'], row['value'])
return row

43
soil/environment.py
View File

@ -15,7 +15,7 @@ import nxsim
from . import utils, agents, analysis, history
class SoilEnvironment(nxsim.NetworkEnvironment):
class Environment(nxsim.NetworkEnvironment):
"""
The environment is key in a simulation. It contains the network topology,
a reference to network and environment agents, as well as the environment
@ -23,7 +23,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
The environment parameters and the state of every agent can be accessed
both by using the environment as a dictionary or with the environment's
:meth:`soil.environment.SoilEnvironment.get` method.
:meth:`soil.environment.Environment.get` method.
"""
def __init__(self, name=None,
@ -49,7 +49,8 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
self.dry_run = dry_run
self.interval = interval
self.dir_path = dir_path or tempfile.mkdtemp('soil-env')
self.get_path()
if not dry_run:
self.get_path()
self._history = history.History(name=self.name if not dry_run else None,
dir_path=self.dir_path)
# Add environment agents first, so their events get
@ -93,17 +94,35 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
if not network_agents:
return
for ix in self.G.nodes():
agent, state = agents._agent_from_distribution(network_agents)
self.set_agent(ix, agent_type=agent, state=state)
self.init_agent(ix, agent_distribution=network_agents)
def init_agent(self, agent_id, agent_distribution):
node = self.G.nodes[agent_id]
init = False
state = dict(node)
agent_type = None
if 'agent_type' in self.states.get(agent_id, {}):
agent_type = self.states[agent_id]
elif 'agent_type' in node:
agent_type = node['agent_type']
elif 'agent_type' in self.default_state:
agent_type = self.default_state['agent_type']
if agent_type:
agent_type = agents.deserialize_agent_type(agent_type)
else:
agent_type, state = agents._agent_from_distribution(agent_distribution)
return self.set_agent(agent_id, agent_type, state)
def set_agent(self, agent_id, agent_type, state=None):
node = self.G.nodes[agent_id]
defstate = deepcopy(self.default_state)
defstate = deepcopy(self.default_state) or {}
defstate.update(self.states.get(agent_id, {}))
defstate.update(node.get('state', {}))
if state:
defstate.update(state)
state = defstate
state.update(node.get('state', {}))
a = agent_type(environment=self,
agent_id=agent_id,
state=state)
@ -118,6 +137,10 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
return a
def add_edge(self, agent1, agent2, attrs=None):
if hasattr(agent1, 'id'):
agent1 = agent1.id
if hasattr(agent2, 'id'):
agent2 = agent2.id
return self.G.add_edge(agent1, agent2)
def run(self, *args, **kwargs):
@ -202,7 +225,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
with open(csv_name, 'w') as f:
cr = csv.writer(f)
cr.writerow(('agent_id', 't_step', 'key', 'value', 'value_type'))
cr.writerow(('agent_id', 't_step', 'key', 'value'))
for i in self.history_to_tuples():
cr.writerow(i)
@ -302,7 +325,6 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
state['network_agents'] = agents._serialize_distribution(self.network_agents)
state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
to_string=True)
del state['_queue']
return state
def __setstate__(self, state):
@ -311,3 +333,6 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
self.network_agents = self.calculate_distribution(self._convert_agent_types(self.network_agents))
self.environment_agents = self._convert_agent_types(self.environment_agents)
return state
SoilEnvironment = Environment

101
soil/history.py
View File

@ -17,12 +17,12 @@ class History:
if db_path is None and name:
db_path = os.path.join(dir_path or os.getcwd(),
'{}.db.sqlite'.format(name))
if db_path is None:
db_path = ":memory:"
else:
if db_path:
if backup and os.path.exists(db_path):
newname = db_path + '.backup{}.sqlite'.format(time.time())
os.rename(db_path, newname)
else:
db_path = ":memory:"
self.db_path = db_path
self.db = db_path
@ -34,12 +34,6 @@ class History:
self._dtypes = {}
self._tups = []
def conversors(self, key):
"""Get the serializer and deserializer for a given key."""
if key not in self._dtypes:
self.read_types()
return self._dtypes[key]
@property
def db(self):
try:
@ -58,55 +52,88 @@ class History:
@property
def dtypes(self):
self.read_types()
return {k:v[0] for k, v in self._dtypes.items()}
def save_tuples(self, tuples):
'''
Save a series of tuples, converting them to records if necessary
'''
self.save_records(Record(*tup) for tup in tuples)
def save_records(self, records):
with self.db:
for rec in records:
if not isinstance(rec, Record):
rec = Record(*rec)
if rec.key not in self._dtypes:
name = utils.name(rec.value)
serializer = utils.serializer(name)
deserializer = utils.deserializer(name)
self._dtypes[rec.key] = (name, serializer, deserializer)
self.db.execute("replace into value_types (key, value_type) values (?, ?)", (rec.key, name))
self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
'''
Save a collection of records
'''
for record in records:
if not isinstance(record, Record):
record = Record(*record)
self.save_record(*record)
def save_record(self, *args, **kwargs):
self._tups.append(Record(*args, **kwargs))
def save_record(self, agent_id, t_step, key, value):
'''
Save a collection of records to the database.
Database writes are cached.
'''
value = self.convert(key, value)
self._tups.append(Record(agent_id=agent_id,
t_step=t_step,
key=key,
value=value))
if len(self._tups) > 100:
self.flush_cache()
def convert(self, key, value):
"""Get the serialized value for a given key."""
if key not in self._dtypes:
self.read_types()
if key not in self._dtypes:
name = utils.name(value)
serializer = utils.serializer(name)
deserializer = utils.deserializer(name)
self._dtypes[key] = (name, serializer, deserializer)
with self.db:
self.db.execute("replace into value_types (key, value_type) values (?, ?)", (key, name))
return self._dtypes[key][1](value)
def recover(self, key, value):
"""Get the deserialized value for a given key, and the serialized version."""
if key not in self._dtypes:
self.read_types()
if key not in self._dtypes:
raise ValueError("Unknown datatype for {} and {}".format(key, value))
return self._dtypes[key][2](value)
def flush_cache(self):
'''
Use a cache to save state changes to avoid opening a session for every change.
The cache will be flushed at the end of the simulation, and when history is accessed.
'''
self.save_records(self._tups)
with self.db:
for rec in self._tups:
self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
self._tups = list()
def to_tuples(self):
self.flush_cache()
with self.db:
res = self.db.execute("select agent_id, t_step, key, value from history ").fetchall()
for r in res:
agent_id, t_step, key, value = r
_, _ , des = self.conversors(key)
yield agent_id, t_step, key, des(value)
self.flush_cache()
with self.db:
res = self.db.execute("select agent_id, t_step, key, value from history ").fetchall()
for r in res:
agent_id, t_step, key, value = r
value = self.recover(key, value)
yield agent_id, t_step, key, value
def read_types(self):
with self.db:
res = self.db.execute("select key, value_type from value_types ").fetchall()
for k, v in res:
serializer = utils.serializer(v)
deserializer = utils.deserializer(v)
self._dtypes[k] = (v, serializer, deserializer)
with self.db:
res = self.db.execute("select key, value_type from value_types ").fetchall()
for k, v in res:
serializer = utils.serializer(v)
deserializer = utils.deserializer(v)
self._dtypes[k] = (v, serializer, deserializer)
def __getitem__(self, key):
self.flush_cache()
key = Key(*key)
agent_ids = [key.agent_id] if key.agent_id is not None else []
t_steps = [key.t_step] if key.t_step is not None else []
@ -176,7 +203,7 @@ class History:
for k, v in self._dtypes.items():
if k in df_p:
dtype, _, deserial = v
df_p[k] = df_p[k].fillna(method='ffill').fillna(deserial()).astype(dtype)
df_p[k] = df_p[k].fillna(method='ffill').astype(dtype)
if t_steps:
df_p = df_p.reindex(t_steps, method='ffill')
return df_p.ffill()

72
soil/simulation.py
View File

@ -12,11 +12,12 @@ import pickle
from nxsim import NetworkSimulation
from . import utils, environment, basestring, agents
from . import utils, basestring, agents
from .environment import Environment
from .utils import logger
class SoilSimulation(NetworkSimulation):
class Simulation(NetworkSimulation):
"""
Subclass of nsim.NetworkSimulation with three main differences:
1) agent type can be specified by name or by class.
@ -43,13 +44,47 @@ class SoilSimulation(NetworkSimulation):
'agent_type_1'.
3) if no initial state is given, each node's state will be set
to `{'id': 0}`.
Parameters
---------
name : str, optional
name of the Simulation
topology : networkx.Graph instance, optional
network_params : dict
parameters used to create a topology with networkx, if no topology is given
network_agents : dict
definition of agents to populate the topology with
agent_type : NetworkAgent subclass, optional
Default type of NetworkAgent to use for nodes not specified in network_agents
states : list, optional
List of initial states corresponding to the nodes in the topology. Basic form is a list of integers
whose value indicates the state
dir_path : str, optional
Directory path where to save pickled objects
seed : str, optional
Seed to use for the random generator
num_trials : int, optional
Number of independent simulation runs
max_time : int, optional
Time how long the simulation should run
environment_params : dict, optional
Dictionary of globally-shared environmental parameters
environment_agents: dict, optional
Similar to network_agents. Distribution of Agents that control the environment
environment_class: soil.environment.Environment subclass, optional
Class for the environment. It defailts to soil.environment.Environment
load_module : str, module name, deprecated
If specified, soil will load the content of this module under 'soil.agents.custom'
"""
def __init__(self, name=None, topology=None, network_params=None,
network_agents=None, agent_type=None, states=None,
default_state=None, interval=1, dump=None, dry_run=False,
dir_path=None, num_trials=1, max_time=100,
agent_module=None, load_module=None, seed=None,
environment_agents=None, environment_params=None, **kwargs):
load_module=None, seed=None,
environment_agents=None, environment_params=None,
environment_class=None, **kwargs):
if topology is None:
topology = utils.load_network(network_params,
@ -70,11 +105,15 @@ class SoilSimulation(NetworkSimulation):
self.dump = dump
self.dry_run = dry_run
self.environment_params = environment_params or {}
self.environment_class = utils.deserialize(environment_class,
known_modules=['soil.environment',]) or Environment
self._loaded_module = None
if load_module:
path = sys.path + [self.dir_path, os.getcwd()]
f, fp, desc = imp.find_module(load_module, path)
imp.load_module('soil.agents.custom', f, fp, desc)
self._loaded_module = imp.load_module('soil.agents.custom', f, fp, desc)
environment_agents = environment_agents or []
self.environment_agents = agents._convert_agent_types(environment_agents)
@ -128,7 +167,7 @@ class SoilSimulation(NetworkSimulation):
'dir_path': self.dir_path,
})
opts.update(kwargs)
env = environment.SoilEnvironment(**opts)
env = self.environment_class(**opts)
return env
def run_trial(self, trial_id=0, until=None, return_env=True, **opts):
@ -177,11 +216,18 @@ class SoilSimulation(NetworkSimulation):
pickle.dump(self, f)
def __getstate__(self):
state = self.__dict__.copy()
state = {}
for k, v in self.__dict__.items():
if k[0] != '_':
state[k] = v
state['topology'] = json_graph.node_link_data(self.topology)
state['network_agents'] = agents._serialize_distribution(self.network_agents)
state['network_agents'] = agents.serialize_distribution(self.network_agents)
state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
to_string=True)
state['environment_class'] = utils.serialize(self.environment_class,
known_modules=['soil.environment', ])[1] # func, name
if state['load_module'] is None:
del state['load_module']
return state
def __setstate__(self, state):
@ -189,6 +235,8 @@ class SoilSimulation(NetworkSimulation):
self.topology = json_graph.node_link_graph(state['topology'])
self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents))
self.environment_agents = agents._convert_agent_types(self.environment_agents)
self.environment_class = utils.deserialize(self.environment_class,
known_modules=['soil.environment', ]) # func, name
return state
@ -197,11 +245,11 @@ def from_config(config):
if len(config) > 1:
raise AttributeError('Provide only one configuration')
config = config[0][0]
sim = SoilSimulation(**config)
sim = Simulation(**config)
return sim
def run_from_config(*configs, results_dir='soil_output', dry_run=False, dump=None, timestamp=False, **kwargs):
def run_from_config(*configs, results_dir='soil_output', dump=None, timestamp=False, **kwargs):
for config_def in configs:
# logger.info("Found {} config(s)".format(len(ls)))
for config, _ in utils.load_config(config_def):
@ -214,6 +262,8 @@ def run_from_config(*configs, results_dir='soil_output', dry_run=False, dump=Non
else:
sim_folder = name
dir_path = os.path.join(results_dir, sim_folder)
sim = SoilSimulation(dir_path=dir_path, dump=dump, **config)
if dump is not None:
config['dump'] = dump
sim = Simulation(dir_path=dir_path, **config)
logger.info('Dumping results to {} : {}'.format(sim.dir_path, sim.dump))
sim.run_simulation(**kwargs)

98
soil/utils.py
View File

@ -1,8 +1,9 @@
import os
import ast
import yaml
import logging
import importlib
from time import time
import time
from glob import glob
from random import random
from copy import deepcopy
@ -62,44 +63,89 @@ def load_config(config):
@contextmanager
def timer(name='task', pre="", function=logger.info, to_object=None):
start = time()
function('{}Starting {} at {}.'.format(pre, name, start))
start = time.time()
function('{}Starting {} at {}.'.format(pre, name,
time.strftime("%X", time.gmtime(start))))
yield start
end = time()
function('{}Finished {} in {} seconds'.format(pre, name, str(end-start)))
end = time.time()
function('{}Finished {} at {} in {} seconds'.format(pre, name,
time.strftime("%X", time.gmtime(end)),
str(end-start)))
if to_object:
to_object.start = start
to_object.end = end
def repr(v):
func = serializer(v)
tname = name(v)
return func(v), tname
builtins = importlib.import_module('builtins')
def name(v):
return type(v).__name__
def name(value, known_modules=[]):
'''Return a name that can be imported, to serialize/deserialize an object'''
if value is None:
return 'None'
if not isinstance(value, type): # Get the class name first
value = type(value)
tname = value.__name__
if hasattr(builtins, tname):
return tname
modname = value.__module__
if modname == '__main__':
return tname
if known_modules and modname in known_modules:
return tname
for mod_name in known_modules:
module = importlib.import_module(mod_name)
if hasattr(module, tname):
return tname
return '{}.{}'.format(modname, tname)
def serializer(type_):
if type_ == 'bool':
return lambda x: "true" if x else ""
if type_ != 'str' and hasattr(builtins, type_):
return repr
return lambda x: x
def deserializer(type_):
try:
# Check if it's a builtin type
module = importlib.import_module('builtins')
cls = getattr(module, type_)
except AttributeError:
# if not, separate module and class
def serialize(v, known_modules=[]):
'''Get a text representation of an object.'''
tname = name(v, known_modules=known_modules)
func = serializer(tname)
return func(v), tname
def deserializer(type_, known_modules=[]):
if type_ == 'str':
return lambda x='': x
if type_ == 'None':
return lambda x=None: None
if hasattr(builtins, type_): # Check if it's a builtin type
cls = getattr(builtins, type_)
return lambda x=None: ast.literal_eval(x) if x is not None else cls()
# Otherwise, see if we can find the module and the class
modules = known_modules or []
options = []
for mod in modules:
options.append((mod, type_))
if '.' in type_: # Fully qualified module
module, type_ = type_.rsplit(".", 1)
module = importlib.import_module(module)
cls = getattr(module, type_)
return cls
options.append ((module, type_))
errors = []
for module, name in options:
try:
module = importlib.import_module(module)
cls = getattr(module, name)
return getattr(cls, 'deserialize', cls)
except (ImportError, AttributeError) as ex:
errors.append((module, name, ex))
raise Exception('Could not find module {}. Tried: {}'.format(type_, errors))
def convert(value, type_):
return deserializer(type_)(value)
def deserialize(type_, value=None, **kwargs):
'''Get an object from a text representation'''
if not isinstance(type_, str):
return type_
des = deserializer(type_, **kwargs)
if value is None:
return des
return des(value)

2
soil/web/__init__.py
View File

@ -271,4 +271,4 @@ def main():
parser.add_argument('--verbose', '-v', help='verbose mode', action='store_true')
args = parser.parse_args()
run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose)
run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose)

45
tests/test_examples.py Normal file
View File

@ -0,0 +1,45 @@
from unittest import TestCase
import os
from os.path import join
from soil import utils, simulation
ROOT = os.path.abspath(os.path.dirname(__file__))
EXAMPLES = join(ROOT, '..', 'examples')
class TestExamples(TestCase):
pass
def make_example_test(path, config):
def wrapped(self):
root = os.getcwd()
os.chdir(os.path.dirname(path))
s = simulation.from_config(config)
envs = s.run_simulation(dry_run=True)
assert envs
for env in envs:
assert env
try:
n = config['network_params']['n']
assert len(list(env.network_agents)) == n
assert env.now > 2 # It has run
assert env.now <= config['max_time'] # But not further than allowed
except KeyError:
pass
os.chdir(root)
return wrapped
def add_example_tests():
for config, path in utils.load_config(join(EXAMPLES, '**', '*.yml')):
p = make_example_test(path=path, config=config)
fname = os.path.basename(path)
p.__name__ = 'test_example_file_%s' % fname
p.__doc__ = '%s should be a valid configuration' % fname
setattr(TestExamples, p.__name__, p)
del p
add_example_tests()

23
tests/test_history.py
View File

@ -116,6 +116,7 @@ class TestHistory(TestCase):
db_path = os.path.join(DBROOT, 'test')
h = history.History(db_path=db_path)
h.save_tuples(tuples)
h.flush_cache()
assert os.path.exists(db_path)
# Recover the data
@ -131,3 +132,25 @@ class TestHistory(TestCase):
assert newhistory._db_path == h._db_path
assert os.path.exists(backuppath)
assert not len(newhistory[None, None, None])
def test_history_tuples(self):
"""
The data recovered should be equal to the one recorded.
"""
tuples = (
('a_1', 0, 'id', 'v'),
('a_1', 1, 'id', 'a'),
('a_1', 2, 'id', 'l'),
('a_1', 3, 'id', 'u'),
('a_1', 4, 'id', 'e'),
('env', 1, 'prob', 1),
('env', 2, 'prob', 2),
('env', 3, 'prob', 3),
('a_2', 7, 'finished', True),
)
h = history.History()
h.save_tuples(tuples)
recovered = list(h.to_tuples())
assert recovered
for i in recovered:
assert i in tuples

97
tests/test_main.py
View File

@ -6,14 +6,12 @@ import networkx as nx
from functools import partial
from os.path import join
from soil import simulation, environment, agents, utils
from soil import simulation, Environment, agents, utils, history
ROOT = os.path.abspath(os.path.dirname(__file__))
EXAMPLES = join(ROOT, '..', 'examples')
class TestMain(TestCase):
def test_load_graph(self):
@ -188,8 +186,6 @@ class TestMain(TestCase):
recovered = yaml.load(serial)
with utils.timer('deleting'):
del recovered['topology']
del recovered['load_module']
del recovered['dry_run']
assert config == recovered
def test_configuration_changes(self):
@ -197,25 +193,17 @@ class TestMain(TestCase):
The configuration should not change after running
the simulation.
"""
config = utils.load_file('examples/complete.yml')[0]
config = utils.load_file(join(EXAMPLES, 'complete.yml'))[0]
s = simulation.from_config(config)
s.dry_run = True
for i in range(5):
s.run_simulation(dry_run=True)
nconfig = s.to_dict()
del nconfig['topology']
del nconfig['dry_run']
del nconfig['load_module']
assert config == nconfig
def test_examples(self):
"""
Make sure all examples in the examples folder are correct
"""
pass
def test_row_conversion(self):
env = environment.SoilEnvironment(dry_run=True)
env = Environment(dry_run=True)
env['test'] = 'test_value'
res = list(env.history_to_tuples())
@ -234,7 +222,7 @@ class TestMain(TestCase):
from geometric models. We should work around it.
"""
G = nx.random_geometric_graph(20, 0.1)
env = environment.SoilEnvironment(topology=G, dry_run=True)
env = Environment(topology=G, dry_run=True)
env.dump_gexf('/tmp/dump-gexf')
def test_save_graph(self):
@ -245,7 +233,7 @@ class TestMain(TestCase):
'''
G = nx.cycle_graph(5)
distribution = agents.calculate_distribution(None, agents.BaseAgent)
env = environment.SoilEnvironment(topology=G, network_agents=distribution, dry_run=True)
env = Environment(topology=G, network_agents=distribution, dry_run=True)
env[0, 0, 'testvalue'] = 'start'
env[0, 10, 'testvalue'] = 'finish'
nG = env.history_to_graph()
@ -253,33 +241,58 @@ class TestMain(TestCase):
assert ('start', 0, 10) in values
assert ('finish', 10, None) in values
def test_serialize_class(self):
ser, name = utils.serialize(agents.BaseAgent)
assert name == 'soil.agents.BaseAgent'
assert ser == agents.BaseAgent
def make_example_test(path, config):
def wrapped(self):
root = os.getcwd()
os.chdir(os.path.dirname(path))
s = simulation.from_config(config)
envs = s.run_simulation(dry_run=True)
assert envs
for env in envs:
assert env
try:
n = config['network_params']['n']
assert len(env.get_agents()) == n
except KeyError:
pass
os.chdir(root)
return wrapped
class CustomAgent(agents.BaseAgent):
pass
ser, name = utils.serialize(CustomAgent)
assert name == 'test_main.CustomAgent'
assert ser == CustomAgent
def add_example_tests():
for config, path in utils.load_config(join(EXAMPLES, '*.yml')):
p = make_example_test(path=path, config=config)
fname = os.path.basename(path)
p.__name__ = 'test_example_file_%s' % fname
p.__doc__ = '%s should be a valid configuration' % fname
setattr(TestMain, p.__name__, p)
del p
def test_serialize_builtin_types(self):
for i in [1, None, True, False, {}, [], list(), dict()]:
ser, name = utils.serialize(i)
assert type(ser) == str
des = utils.deserialize(name, ser)
assert i == des
add_example_tests()
def test_deserialize_agent_distribution(self):
agent_distro = [
{
'agent_type': 'CounterModel',
'weight': 1
},
{
'agent_type': 'BaseAgent',
'weight': 2
},
]
converted = agents.deserialize_distribution(agent_distro)
assert converted[0]['agent_type'] == agents.CounterModel
assert converted[1]['agent_type'] == agents.BaseAgent
def test_serialize_agent_distribution(self):
agent_distro = [
{
'agent_type': agents.CounterModel,
'weight': 1
},
{
'agent_type': agents.BaseAgent,
'weight': 2
},
]
converted = agents.serialize_distribution(agent_distro)
assert converted[0]['agent_type'] == 'CounterModel'
assert converted[1]['agent_type'] == 'BaseAgent'
def test_history(self):
'''Test storing in and retrieving from history (sqlite)'''
h = history.History()
h.save_record(agent_id=0, t_step=0, key="test", value="hello")
assert h[0, 0, "test"] == "hello"