black formatting

Documentation needs some improvement

The API has been simplified to only allow for ONE topology per
NetworkEnvironment.
This covers the main use case, and simplifies the code.

.dockerignore

@@ -0,0 +1,5 @@
+**/soil_output
+.*
+**/__pycache__
+__pycache__
+*.pyc

.gitignore

@@ -8,3 +8,4 @@ soil_output
 docs/_build*
 build/*
 dist/*
+prof

.gitlab-ci.yml

@@ -0,0 +1,53 @@
+stages:
+  - test
+  - publish
+  - check_published
+
+docker:
+  stage: publish
+  image:
+    name: gcr.io/kaniko-project/executor:debug
+    entrypoint: [""]
+  tags:
+    - docker
+  script:
+    - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
+    # The skip-tls-verify flag is there because our registry certificate is self signed
+    - /kaniko/executor --context $CI_PROJECT_DIR --skip-tls-verify --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
+  only:
+    - tags
+
+test:
+  tags:
+    - docker
+  image: python:3.7
+  stage: test
+  script:
+    - pip install -r requirements.txt -r test-requirements.txt
+    - python setup.py test
+
+push_pypi:
+  only:
+    - tags
+  tags:
+    - docker
+  image: python:3.7
+  stage: publish
+  script:
+    - echo $CI_COMMIT_TAG > soil/VERSION
+    - pip install twine
+    - python setup.py sdist bdist_wheel
+    - TWINE_PASSWORD=$PYPI_PASSWORD TWINE_USERNAME=$PYPI_USERNAME python -m twine upload dist/*
+
+check_pypi:
+  only:
+    - tags
+  tags:
+    - docker
+  image: python:3.7
+  stage: check_published
+  script:
+    - pip install soil==$CI_COMMIT_TAG
+  # Allow PYPI to update its index before we try to install 
+  when: delayed
+  start_in: 2 minutes

CHANGELOG.md

@@ -0,0 +1,180 @@
+# Changelog
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [0.3 UNRELEASED]
+### Added
+* Simple debugging capabilities in `soil.debugging`, with a custom `pdb.Debugger` subclass that exposes commands to list agents and their status and set breakpoints on states (for FSM agents). Try it with `soil --debug <simulation file>`
+* Ability to run 
+* Ability to 
+* The `soil.exporters` module to export the results of datacollectors (model.datacollector) into files at the end of trials/simulations
+* A modular set of classes for environments/models. Now the ability to configure the agents through an agent definition and a topology through a network configuration is split into two classes (`soil.agents.BaseEnvironment` for agents, `soil.agents.NetworkEnvironment` to add topology).
+* FSM agents can now have generators as states. They work similar to normal states, with one caveat. Only `time` values can be yielded, not a state. This is because the state will not change, it will be resumed after the yield, at the appropriate time. The return value *can* be a state, or a `(state, time)` tuple, just like in normal states.
+### Changed
+* Configuration schema is very different now. Check `soil.config` for more information. We are also using Pydantic for (de)serialization.
+* There may be more than one topology/network in the simulation
+* Ability
+### Removed
+* Any `tsih` and `History` integration in the main classes. To record the state of environments/agents, just use a datacollector. In some cases this may be slower or consume more memory than the previous system. However, few cases actually used the full potential of the history, and it came at the cost of unnecessary complexity and worse performance for the majority of cases.
+
+
+## [0.20.7]
+### Changed
+* Creating a `time.When` from another `time.When` does not nest them anymore (it returns the argument)
+### Fixed
+* Bug with time.NEVER/time.INFINITY
+## [0.20.6]
+### Fixed
+* Agents now return `time.INFINITY` when dead, instead of 'inf'
+* `soil.__init__` does not re-export built-in time (change in `soil.simulation`. It used to create subtle import conflicts when importing soil.time.
+* Parallel simulations were broken because lambdas cannot be pickled properly, which is needed for multiprocessing.
+### Changed
+* Some internal simulation methods do not accept `*args` anymore, to avoid ambiguity and bugs.
+## [0.20.5]
+### Changed
+* Defaults are now set in the agent __init__, not in the environment. This decouples both classes a bit more, and it is more intuitive 
+## [0.20.4]
+### Added
+* Agents can now be given any kwargs, which will be used to set their state
+* Environments have a default logger `self.logger` and a log method, just like agents
+## [0.20.3]
+### Fixed
+* Default state values are now deepcopied again.
+* Seeds for environments only concatenate the trial id (i.e., a number), to provide repeatable results.
+* `Environment.run` now calls `Environment.step`, to allow for easy overloading of the environment step
+### Removed
+* Datacollectors are not being used for now.
+* `time.TimedActivation.step` does not use an `until` parameter anymore.
+### Changed
+* Simulations now run right up to `until` (open interval)
+* Time instants (`time.When`) don't need to be floats anymore. Now we can avoid precision issues with big numbers by using ints.
+* Rabbits simulation is more idiomatic (using subclasses)
+
+## [0.20.2]
+### Fixed
+* CI/CD testing issues
+## [0.20.1]
+### Fixed
+* Agents would run another step after dying.
+## [0.20.0]
+### Added
+* Integration with MESA
+* `not_agent_ids` parameter to get sql in history
+### Changed
+* `soil.Environment` now also inherits from `mesa.Model`
+* `soil.Agent` now also inherits from `mesa.Agent`
+* `soil.time` to replace `simpy` events, delays, duration, etc.
+* `agent.id` is not `agent.unique_id` to be compatible with `mesa`. A property `BaseAgent.id` has been added for compatibility.
+* `agent.environment` is now `agent.model`, for the same reason as above. The parameter name in `BaseAgent.__init__` has also been renamed.
+### Removed
+* `simpy` dependency and compatibility. Each agent used to be a simpy generator, but that made debugging and error handling more complex. That has been replaced by a scheduler within the `soil.Environment` class, similar to how `mesa` does it.
+* `soil.history` is now a separate package named `tsih`. The keys namedtuple uses `dict_id` instead of `agent_id`.
+### Added
+* An option to choose whether a database should be used for history 
+## [0.15.2]
+### Fixed
+* Pass the right known_modules and parameters to stats discovery in simulation
+* The configuration file must exist when launching through the CLI. If it doesn't, an error will be logged
+* Minor changes in the documentation of the CLI arguments
+### Changed
+* Stats are now exported by default
+## [0.15.1]
+### Added
+* read-only `History`
+### Fixed
+* Serialization problem with the `Environment` on parallel mode.
+* Analysis functions now work as they should in the tutorial
+## [0.15.0]
+### Added
+* Control logging level in CLI and simulation
+* `Stats` to calculate trial and simulation-wide statistics
+* Simulation statistics are stored in a separate table in history (see `History.get_stats` and `History.save_stats`, as well as `soil.stats`)
+* Aliased `NetworkAgent.G` to `NetworkAgent.topology`.
+### Changed
+* Templates in config files can be given as dictionaries in addition to strings
+* Samplers are used more explicitly
+* Removed nxsim dependency. We had already made a lot of changes, and nxsim has not been updated in 5 years.
+* Exporter methods renamed to `trial` and `end`. Added `start`.
+* `Distribution` exporter now a stats class
+* `global_topology` renamed to `topology`
+* Moved topology-related methods to `NetworkAgent`
+### Fixed
+* Temporary files used for history in dry_run mode are not longer left open 
+
+## [0.14.9]
+### Changed
+* Seed random before environment initialization
+## [0.14.8]
+### Fixed
+* Invalid directory names in Windows gsi-upm/soil#5
+## [0.14.7]
+### Changed
+* Minor change to traceback handling in async simulations
+### Fixed
+* Incomplete example in the docs (example.yml) caused an exception
+## [0.14.6]
+### Fixed
+* Bug with newer versions of networkx (0.24) where the Graph.node attribute has been removed. We have updated our calls, but the code in nxsim is not under our control, so we have pinned the networkx version until that issue is solved.
+### Changed
+* Explicit yaml.SafeLoader to avoid deprecation warnings when using yaml.load. It should not break any existing setups, but we could move to the FullLoader in the future if needed.
+
+## [0.14.4]
+### Fixed
+* Bug in `agent.get_agents()` when `state_id` is passed as a string. The tests have been modified accordingly.
+## [0.14.3]
+### Fixed
+* Incompatibility with py3.3-3.6 due to ModuleNotFoundError and TypeError in DryRunner
+## [0.14.2]
+### Fixed
+* Output path for exporters is now soil_output
+### Changed 
+* CSV output to stdout in dry_run mode
+## [0.14.1]
+### Changed
+* Exporter names in lower case
+* Add default exporter in runs
+## [0.14.0]
+### Added
+* Loading configuration from template definitions in the yaml, in preparation for SALib support.
+The definition of the variables and their possible values (i.e., a problem in SALib terms), as well as a sampler function, can be provided.
+Soil uses this definition and the template to generate a set of configurations.
+* Simulation group names, to link related simulations. For now, they are only used to group all simulations in the same group under the same folder.
+* Exporters unify exporting/dumping results and other files to disk. If `dry_run` is set to `True`, exporters will write to stdout instead of a file (useful for testing/debugging).
+* Distribution exporter, to write statistics about values and value_counts in every simulation. The results are dumped to two CSV files.
+
+### Changed
+* `dir_path` is now the directory for resources (modules, files)
+* Environments and simulations do not export or write anything by default. That task is delegated to Exporters
+
+### Removed
+* The output dir for environments and simulations (see Exporters)
+* DrawingAgent, because it wrote to disk and was not being used. We provide a partial alternative in the form of the GraphDrawing exporter. A complete alternative will be provided once the network at each state can be accessed by exporters.
+
+## Fixed
+* Modules with custom agents/environments failed to load when they were run from outside the directory of the definition file. Modules are now loaded from the directory of the simulation file in addition to the working directory
+* Memory databases (in history) can now be shared between threads.
+* Testing all examples, not just subdirectories
+
+## [0.13.8]
+### Changed
+* Moved TerroristNetworkModel to examples
+### Added
+* `get_agents` and `count_agents` methods now accept lists as inputs. They can be used to retrieve agents from node ids
+* `subgraph` in BaseAgent
+* `agents.select` method, to filter out agents
+* `skip_test` property in yaml definitions, to force skipping some examples
+* `agents.Geo`, with a search function based on postition
+* `BaseAgent.ego_search` to get nodes from the ego network of a node
+* `BaseAgent.degree` and `BaseAgent.betweenness`
+### Fixed
+
+## [0.13.7]
+### Changed
+* History now defaults to not backing up! This makes it more intuitive to load the history for examination, at the expense of rewriting something. That should not happen because History is only created in the Environment, and that has `backup=True`.
+### Added
+* Agent names are assigned based on their agent types
+* Agent logging uses the agent name.
+* FSM agents can now return a timeout in addition to a new state. e.g. `return self.idle, self.env.timeout(2)` will execute the *different_state* in 2 *units of time* (`t_step=now+2`).
+* Example of using timeouts in FSM (custom_timeouts)
+* `network_agents` entries may include an `ids` entry. If set, it should be a list of node ids that should be assigned that agent type. This complements the previous behavior of setting agent type with `weights`.

Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.7
+
+WORKDIR /usr/src/app
+
+COPY test-requirements.txt requirements.txt /usr/src/app/
+RUN pip install --no-cache-dir -r test-requirements.txt -r requirements.txt
+
+COPY ./ /usr/src/app
+
+RUN pip install '.[web]'
+
+ENTRYPOINT ["python", "-m", "soil"]

MANIFEST.in

@@ -1,4 +1,7 @@
 include requirements.txt
 include test-requirements.txt
 include README.rst
-graft soil
+graft soil
+global-exclude __pycache__
+global-exclude soil_output
+global-exclude *.py[co]

Makefile

@@ -0,0 +1,7 @@
+quick-test:
+	docker-compose exec dev python -m pytest -s -v
+
+test:
+	docker run -t -v $$PWD:/usr/src/app -w /usr/src/app python:3.7 python setup.py test
+
+.PHONY: test

README.md

@@ -3,7 +3,46 @@
 Soil is an extensible and user-friendly Agent-based Social Simulator for Social Networks.
 Learn how to run your own simulations with our [documentation](http://soilsim.readthedocs.io).
 
-Follow our [tutorial](notebooks/soil_tutorial.ipynb) to develop your own agent models.
+Follow our [tutorial](examples/tutorial/soil_tutorial.ipynb) to develop your own agent models.
+
+
+# Changes in version 0.3
+
+Version 0.3 came packed with many changes to provide much better integration with MESA.
+For a long time, we tried to keep soil backwards-compatible, but it turned out to be a big endeavour and the resulting code was less readable.
+This translates to harder maintenance and a worse experience for newcomers. 
+In the end, we decided to make some breaking changes.
+
+If you have an older Soil simulation, you have two options:
+
+* Update the necessary configuration files and code. You may use the examples in the `examples` folder for reference, as well as the documentation.
+* Keep using a previous `soil` version.
+
+## Mesa compatibility
+
+Soil is in the process of becoming fully compatible with MESA.
+The idea is to provide a set of modular classes and functions that extend the functionality of mesa, whilst staying compatible.
+In the end, it should be possible to add regular mesa agents to a soil simulation, or use a soil agent within a mesa simulation/model.
+
+This is a non-exhaustive list of tasks to achieve compatibility:
+
+- [ ] Integrate `soil.Simulation` with mesa's runners:
+  - [ ] `soil.Simulation` could mimic/become a `mesa.batchrunner`
+- [ ] Integrate `soil.Environment` with `mesa.Model`:
+  - [x] `Soil.Environment` inherits from `mesa.Model`
+  - [x] `Soil.Environment` includes a Mesa-like Scheduler (see the `soil.time` module.
+  - [ ] Allow for `mesa.Model` to be used in a simulation.
+- [ ] Integrate `soil.Agent` with `mesa.Agent`:
+  - [x] Rename agent.id to unique_id?
+  - [x] mesa agents can be used in soil simulations (see `examples/mesa`)
+- [ ] Provide examples
+  - [ ] Using mesa modules in a soil simulation
+  - [ ] Using soil modules in a mesa simulation
+- [ ] Document the new APIs and usage
+
+
+## Citation 
+
 
 If you use Soil in your research, don't forget to cite this paper:
 
@@ -28,7 +67,6 @@ If you use Soil in your research, don't forget to cite this paper:
 
 ```
 
-@Copyright GSI - Universidad Politécnica de Madrid 2017
-
-[![SOIL](logo_gsi.png)](https://www.gsi.dit.upm.es)
+@Copyright GSI - Universidad Politécnica de Madrid 2017-2021
 
+[![SOIL](logo_gsi.png)](https://www.gsi.upm.es)

docker-compose.yml

@@ -0,0 +1,12 @@
+version: '3'
+services:
+  dev:
+    build: .
+    environment:
+      PYTHONDONTWRITEBYTECODE: 1
+    volumes:
+      - .:/usr/src/app
+    tty: true
+    entrypoint: /bin/bash
+    ports:
+      - '8001:8001'

docs/conf.py

@@ -31,7 +31,7 @@
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = []
+extensions = ['IPython.sphinxext.ipython_console_highlighting']
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
@@ -69,7 +69,7 @@ language = None
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This patterns also effect to html_static_path and html_extra_path
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints']
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = 'sphinx'

docs/configuration.rst

@@ -0,0 +1,262 @@
+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``).
+
+.. literalinclude:: example.yml
+   :language: yaml
+
+
+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_class``), 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.
+That means both global parameters, such as the probability of disease outbreak.
+But it also means other data, such as a map, or a network topology that connects multiple agents.
+As a result, it is also typical to add custom functions in an environment that help agents interact with each other and with the state of the simulation.
+
+Last but not least, an environment controls when and how its agents will be executed.
+By default, soil environments incorporate a ``soil.time.TimedActivation`` model for agent execution (more on this on the following section).
+
+Soil environments are very similar, and often interchangeable with, mesa models (``mesa.Model``).
+
+A configuration 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 (and its 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_class``) and state.
+The agent type is a ``soil.Agent`` class, which contains the code that encapsulates the behavior of the agent.
+The state is a set of variables, which may change during the simulation, and that the code may use to control the behavior.
+All agents provide a ``step`` method either explicitly or implicitly (by inheriting it from a superclass), which controls how the agent will behave in each step of the simulation.
+
+When and how agent steps are executed in a simulation depends entirely on the ``environment``.
+Most environments will internally use a scheduler (``mesa.time.BaseScheduler``), which controls the activation of agents.
+
+In soil, we generally used the ``soil.time.TimedActivation`` scheduler, which allows agents to specify when their next activation will happen, defaulting to a 
+
+When an agent's step is executed (generally, every ``interval`` seconds), the agent has access to its state and the environment.
+Through the environment, it can access the network topology and the state of other agents.
+
+There are two 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_class: SISaModel
+
+It is also possible to add more than one type of agent to the simulation.
+
+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_class: SISaModel
+            weight: 1
+          - agent_class: 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_class: BaseAgent
+   states:
+       first:
+         agent_class: SISaModel
+          
+
+This would also work with a randomly generated network:
+
+
+.. code:: yaml
+
+   network:
+       generator: complete
+       n: 5
+   agent_class: BaseAgent
+   states:
+       - agent_class: 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_class: SISaModel
+            weight: 9
+            state:
+            id: neutral
+        - agent_class: 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_class: 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_class: MyAgent
+         state:
+           mood: happy
+       - agent_class: 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.
+
+Templating
+==========
+
+Sometimes, it is useful to parameterize a simulation and run it over a range of values in order to compare each run and measure the effect of those parameters in the simulation.
+For instance, you may want to run a simulation with different agent distributions.
+
+This can be done in Soil using **templates**.
+A template is a configuration where some of the values are specified with a variable.
+e.g.,  ``weight: "{{ var1 }}"`` instead of ``weight: 1``.
+There are two types of variables, depending on how their values are decided:
+
+* Fixed. A list of values is provided, and a new simulation is run for each possible value. If more than a variable is given, a new simulation will be run per combination of values.
+* Bounded/Sampled. The bounds of the variable are provided, along with a sampler method, which will be used to compute all the configuration combinations.
+
+When fixed and bounded variables are mixed, Soil generates a new configuration per combination of fixed values and bounded values.
+
+Here is an example with a single fixed variable and two bounded variable:
+
+.. literalinclude:: ../examples/template.yml
+   :language: yaml

docs/example.yml

@@ -0,0 +1,35 @@
+---
+name: MyExampleSimulation
+max_time: 50
+num_trials: 3
+interval: 2
+network_params:
+    generator: barabasi_albert_graph
+    n: 100
+    m: 2
+network_agents:
+    - agent_class: SISaModel
+      weight: 1
+      state:
+        id: content
+    - agent_class: SISaModel
+      weight: 1
+      state:
+        id: discontent
+    - agent_class: SISaModel
+      weight: 8
+      state:
+        id: neutral
+environment_params:
+    prob_infect: 0.075
+    neutral_discontent_spon_prob: 0.1
+    neutral_discontent_infected_prob: 0.3
+    neutral_content_spon_prob: 0.3
+    neutral_content_infected_prob: 0.4
+    discontent_neutral: 0.5
+    discontent_content: 0.5
+    variance_d_c: 0.2
+    content_discontent: 0.2
+    variance_c_d: 0.2
+    content_neutral: 0.2
+    standard_variance: 1

docs/index.rst

@@ -6,7 +6,7 @@
 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:
 
@@ -34,13 +34,15 @@ If you use Soil in your research, do not forget to cite this paper:
 
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 0
    :caption: Learn more about soil:
 
    installation
    quickstart
-   Tutorial - Spreading news
+   configuration
+   Tutorial <soil_tutorial>
 
+..
 
 
 .. Indices and tables

docs/installation.rst

@@ -1,7 +1,7 @@
 Installation
 ------------
 
-The easiest way to install Soil is through pip:
+The easiest way to install Soil is through pip, with Python >= 3.4:
 
 .. code:: bash
 
@@ -14,11 +14,11 @@ Now test that it worked by running the command line tool
 
    soil --help
 
-Or using soil programmatically:
+Or, if you're using using soil programmatically:
 
 .. code:: python
 
    import soil
    print(soil.__version__)
 
-The latest version can be installed through `GitLab <https://lab.cluster.gsi.dit.upm.es/soil/soil.git>`_.
+The latest version can be installed through `GitLab <https://lab.gsi.upm.es/soil/soil.git>`_ or `GitHub <https://github.com/gsi-upm/soil>`_.

docs/quickstart.rst

@@ -1,194 +1,93 @@
 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
-    timeout: 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
-
-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 (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``).
+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.
 
 
-.. code::
-
-    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
+.. image:: soil.png
+  :width: 80%
+  :align: center
 
 
-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``).
+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`.
 
+Configuration
+=============
+To get you started, we will use this configuration (:download:`download the file <quickstart.yml>` directly):
 
-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 certain events.
-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
-
-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.
-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.
-
-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 have an associated 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``).
-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
 
+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.
 
-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.
+Running the simulation
+======================
 
+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
+
+Results and visualization
+=========================
+
+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.
+
+
+Soil also includes a web server that allows you to upload your simulations, change parameters, and visualize the results, including a timeline of the network.
+To make it work, you have to install soil like this:
+
+.. code::
+
+  pip install soil[web]
+
+Once installed, the soil web UI can be run in two ways:
+
+.. code::
+
+  soil-web
+
+  # OR
+
+  python -m soil.web

docs/quickstart.yml

@@ -0,0 +1,30 @@
+---
+name: quickstart
+num_trials: 1
+max_time: 1000
+network_agents:
+  - agent_class: SISaModel
+    state:
+      id: neutral
+    weight: 1
+  - agent_class: 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

docs/requirements.txt

@@ -0,0 +1 @@
+ipython>=7.31.1

docs/soil-vs.rst

@@ -0,0 +1,12 @@
+### MESA
+
+Starting with version 0.3, Soil has been redesigned to complement Mesa, while remaining compatible with it.
+That means that every component in Soil (i.e., Models, Environments, etc.) can be mixed with existing mesa components.
+In fact, there are examples that show how that integration may be used, in the `examples/mesa` folder in the repository.
+
+Here are some reasons to use Soil instead of plain mesa:
+
+- Less boilerplate for common scenarios (by some definitions of common)
+- Functions to automatically populate a topology with an agent distribution (i.e., different ratios of agent class and state)
+- The `soil.Simulation` class allows you to run multiple instances of the same experiment (i.e., multiple trials with the same parameters but a different randomness seed)
+- Reporting functions that aggregate multiple

examples/complete.yml

@@ -1,24 +1,54 @@
 ---
+version: '2'
 name: simple
+group: tests
 dir_path: "/tmp/"
 num_trials: 3
-max_time: 100
+max_steps: 100
 interval: 1
-network_params:
-  generator: complete_graph
-  n: 10
-network_agents:
-  - agent_type: CounterModel
-    weight: 1
-    state:
-      id: 0
-  - agent_type: AggregatedCounter
-    weight: 0.2
-environment_agents: []
-environment_params:
+seed: "CompleteSeed!"
+model_class: Environment
+model_params:
   am_i_complete: true
-default_state:
-  incidents: 0
-states:
-  - name: 'The first node'
-  - name: 'The second node'
+  topology:
+    params:
+      generator: complete_graph
+      n: 12
+  environment:
+  agents:
+    agent_class: CounterModel
+    topology: true
+    state:
+      times: 1
+      # In this group we are not specifying any topology
+    fixed:
+      - name: 'Environment Agent 1'
+        agent_class: BaseAgent
+        group: environment
+        topology: false
+        hidden: true
+        state:
+          times: 10
+      - agent_class: CounterModel
+        id: 0
+        group: fixed_counters
+        state:
+          times: 1
+          total: 0
+      - agent_class: CounterModel
+        group: fixed_counters
+        id: 1
+    distribution:
+      - agent_class: CounterModel
+        weight: 1
+        group: distro_counters
+        state:
+          times: 3
+      - agent_class: AggregatedCounter
+        weight: 0.2
+    override:
+      - filter:
+          agent_class: AggregatedCounter
+        n: 2
+        state:
+          times: 5

examples/custom-agents/UnnamedSimulation.dumped.yml

@@ -1,17 +0,0 @@
-default_state: {}
-environment_agents: []
-environment_params: {prob_neighbor_spread: 0.0, prob_tv_spread: 0.01}
-interval: 1
-max_time: 20
-name: Sim_prob_0
-network_agents:
-- agent_type: NewsSpread
-  state: {has_tv: false}
-  weight: 1
-- agent_type: NewsSpread
-  state: {has_tv: true}
-  weight: 2
-network_params: {generator: erdos_renyi_graph, n: 500, p: 0.1}
-num_trials: 1
-states:
-- {has_tv: true}

examples/custom-agents/agent.py

@@ -1,20 +0,0 @@
-import soil
-import random
-
-class NewsSpread(soil.agents.FSM):
-    @soil.agents.default_state
-    @soil.agents.state
-    def neutral(self):
-        r = random.random()
-        if self['has_tv'] and r < self.env['prob_tv_spread']:
-                return self.infected
-        return
-
-    @soil.agents.state
-    def infected(self):
-        prob_infect = self.env['prob_neighbor_spread']
-        for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
-            r = random.random()
-            if r < prob_infect:
-                neighbor.state['id'] = self.infected.id
-        return

examples/custom_generator/custom_generator.yml

@@ -0,0 +1,16 @@
+---
+name: custom-generator
+description: Using a custom generator for the network
+num_trials: 3
+max_steps: 100
+interval: 1
+network_params:
+  generator: mymodule.mygenerator
+# These are custom parameters
+  n: 10
+  n_edges: 5  
+network_agents:
+  - agent_class: CounterModel
+    weight: 1
+    state:
+      state_id: 0

examples/custom_generator/mymodule.py

@@ -0,0 +1,22 @@
+from networkx import Graph
+import random
+import networkx as nx
+
+
+def mygenerator(n=5, n_edges=5):
+    """
+    Just a simple generator that creates a network with n nodes and
+    n_edges edges. Edges are assigned randomly, only avoiding self loops.
+    """
+    G = nx.Graph()
+
+    for i in range(n):
+        G.add_node(i)
+
+    for i in range(n_edges):
+        nodes = list(G.nodes)
+        n_in = random.choice(nodes)
+        nodes.remove(n_in)  # Avoid loops
+        n_out = random.choice(nodes)
+        G.add_edge(n_in, n_out)
+    return G

examples/custom_timeouts/custom_timeouts.py

@@ -0,0 +1,38 @@
+from soil.agents import FSM, state, default_state
+
+
+class Fibonacci(FSM):
+    """Agent that only executes in t_steps that are Fibonacci numbers"""
+
+    defaults = {"prev": 1}
+
+    @default_state
+    @state
+    def counting(self):
+        self.log("Stopping at {}".format(self.now))
+        prev, self["prev"] = self["prev"], max([self.now, self["prev"]])
+        return None, self.env.timeout(prev)
+
+
+class Odds(FSM):
+    """Agent that only executes in odd t_steps"""
+
+    @default_state
+    @state
+    def odds(self):
+        self.log("Stopping at {}".format(self.now))
+        return None, self.env.timeout(1 + self.now % 2)
+
+
+if __name__ == "__main__":
+    from soil import Simulation
+
+    s = Simulation(
+        network_agents=[
+            {"ids": [0], "agent_class": Fibonacci},
+            {"ids": [1], "agent_class": Odds},
+        ],
+        network_params={"generator": "complete_graph", "n": 2},
+        max_time=100,
+    )
+    s.run(dry_run=True)

examples/mesa/mesa.yml

@@ -0,0 +1,19 @@
+---
+name: mesa_sim
+group: tests
+dir_path: "/tmp"
+num_trials: 3
+max_steps: 100
+interval: 1
+seed: '1'
+model_class: social_wealth.MoneyEnv
+model_params:
+  generator: social_wealth.graph_generator
+  agents:
+    topology: true
+    distribution:
+      - agent_class: social_wealth.SocialMoneyAgent
+        weight: 1
+  N: 10
+  width: 50
+  height: 50

examples/mesa/server.py

@@ -0,0 +1,114 @@
+from mesa.visualization.ModularVisualization import ModularServer
+from soil.visualization import UserSettableParameter
+from mesa.visualization.modules import ChartModule, NetworkModule, CanvasGrid
+from social_wealth import MoneyEnv, graph_generator, SocialMoneyAgent
+import networkx as nx
+
+
+class MyNetwork(NetworkModule):
+    def render(self, model):
+        return self.portrayal_method(model)
+
+
+def network_portrayal(env):
+    # The model ensures there is 0 or 1 agent per node
+
+    portrayal = dict()
+    wealths = {
+        node_id: data["agent"].wealth for (node_id, data) in env.G.nodes(data=True)
+    }
+    portrayal["nodes"] = [
+        {
+            "id": node_id,
+            "size": 2 * (wealth + 1),
+            "color": "#CC0000" if wealth == 0 else "#007959",
+            # "color": "#CC0000",
+            "label": f"{node_id}: {wealth}",
+        }
+        for (node_id, wealth) in wealths.items()
+    ]
+
+    portrayal["edges"] = [
+        {"id": edge_id, "source": source, "target": target, "color": "#000000"}
+        for edge_id, (source, target) in enumerate(env.G.edges)
+    ]
+
+    return portrayal
+
+
+def gridPortrayal(agent):
+    """
+    This function is registered with the visualization server to be called
+    each tick to indicate how to draw the agent in its current state.
+    :param agent:  the agent in the simulation
+    :return: the portrayal dictionary
+    """
+    color = max(10, min(agent.wealth * 10, 100))
+    return {
+        "Shape": "rect",
+        "w": 1,
+        "h": 1,
+        "Filled": "true",
+        "Layer": 0,
+        "Label": agent.unique_id,
+        "Text": agent.unique_id,
+        "x": agent.pos[0],
+        "y": agent.pos[1],
+        "Color": f"rgba(31, 10, 255, 0.{color})",
+    }
+
+
+grid = MyNetwork(network_portrayal, 500, 500)
+chart = ChartModule(
+    [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
+)
+
+model_params = {
+    "N": UserSettableParameter(
+        "slider",
+        "N",
+        5,
+        1,
+        10,
+        1,
+        description="Choose how many agents to include in the model",
+    ),
+    "height": UserSettableParameter(
+        "slider",
+        "height",
+        5,
+        5,
+        10,
+        1,
+        description="Grid height",
+    ),
+    "width": UserSettableParameter(
+        "slider",
+        "width",
+        5,
+        5,
+        10,
+        1,
+        description="Grid width",
+    ),
+    "agent_class": UserSettableParameter(
+        "choice",
+        "Agent class",
+        value="MoneyAgent",
+        choices=["MoneyAgent", "SocialMoneyAgent"],
+    ),
+    "generator": graph_generator,
+}
+
+
+canvas_element = CanvasGrid(
+    gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500
+)
+
+
+server = ModularServer(
+    MoneyEnv, [grid, chart, canvas_element], "Money Model", model_params
+)
+server.port = 8521
+
+server.launch(open_browser=False)

examples/mesa/social_wealth.py

@@ -0,0 +1,137 @@
+"""
+This is an example that adds soil agents and environment in a normal
+mesa workflow.
+"""
+from mesa import Agent as MesaAgent
+from mesa.space import MultiGrid
+
+# from mesa.time import RandomActivation
+from mesa.datacollection import DataCollector
+from mesa.batchrunner import BatchRunner
+
+import networkx as nx
+
+from soil import NetworkAgent, Environment, serialization
+
+
+def compute_gini(model):
+    agent_wealths = [agent.wealth for agent in model.agents]
+    x = sorted(agent_wealths)
+    N = len(list(model.agents))
+    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
+    return 1 + (1 / N) - 2 * B
+
+
+class MoneyAgent(MesaAgent):
+    """
+    A MESA agent with fixed initial wealth.
+    It will only share wealth with neighbors based on grid proximity
+    """
+
+    def __init__(self, unique_id, model, wealth=1):
+        super().__init__(unique_id=unique_id, model=model)
+        self.wealth = wealth
+
+    def move(self):
+        possible_steps = self.model.grid.get_neighborhood(
+            self.pos, moore=True, include_center=False
+        )
+        new_position = self.random.choice(possible_steps)
+        self.model.grid.move_agent(self, new_position)
+
+    def give_money(self):
+        cellmates = self.model.grid.get_cell_list_contents([self.pos])
+        if len(cellmates) > 1:
+            other = self.random.choice(cellmates)
+            other.wealth += 1
+            self.wealth -= 1
+
+    def step(self):
+        print("Crying wolf", self.pos)
+        self.move()
+        if self.wealth > 0:
+            self.give_money()
+
+
+class SocialMoneyAgent(NetworkAgent, MoneyAgent):
+    wealth = 1
+
+    def give_money(self):
+        cellmates = set(self.model.grid.get_cell_list_contents([self.pos]))
+        friends = set(self.get_neighboring_agents())
+        self.info("Trying to give money")
+        self.info("Cellmates: ", cellmates)
+        self.info("Friends: ", friends)
+
+        nearby_friends = list(cellmates & friends)
+
+        if len(nearby_friends):
+            other = self.random.choice(nearby_friends)
+            other.wealth += 1
+            self.wealth -= 1
+
+
+def graph_generator(n=5):
+    G = nx.Graph()
+    for ix in range(n):
+        G.add_edge(0, ix)
+    return G
+
+
+class MoneyEnv(Environment):
+    """A model with some number of agents."""
+
+    def __init__(
+        self,
+        width,
+        height,
+        N,
+        generator=graph_generator,
+        agent_class=SocialMoneyAgent,
+        topology=None,
+        **kwargs
+    ):
+
+        generator = serialization.deserialize(generator)
+        agent_class = serialization.deserialize(agent_class, globs=globals())
+        topology = generator(n=N)
+        super().__init__(topology=topology, N=N, **kwargs)
+        self.grid = MultiGrid(width, height, False)
+
+        self.populate_network(agent_class=agent_class)
+
+        # Create agents
+        for agent in self.agents:
+            x = self.random.randrange(self.grid.width)
+            y = self.random.randrange(self.grid.height)
+            self.grid.place_agent(agent, (x, y))
+
+        self.datacollector = DataCollector(
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
+        )
+
+
+if __name__ == "__main__":
+
+    fixed_params = {
+        "generator": nx.complete_graph,
+        "width": 10,
+        "network_agents": [{"agent_class": SocialMoneyAgent, "weight": 1}],
+        "height": 10,
+    }
+
+    variable_params = {"N": range(10, 100, 10)}
+
+    batch_run = BatchRunner(
+        MoneyEnv,
+        variable_parameters=variable_params,
+        fixed_parameters=fixed_params,
+        iterations=5,
+        max_steps=100,
+        model_reporters={"Gini": compute_gini},
+    )
+    batch_run.run_all()
+
+    run_data = batch_run.get_model_vars_dataframe()
+    run_data.head()
+    print(run_data.Gini)

examples/mesa/wealth.py

@@ -0,0 +1,87 @@
+from mesa import Agent, Model
+from mesa.space import MultiGrid
+from mesa.time import RandomActivation
+from mesa.datacollection import DataCollector
+from mesa.batchrunner import BatchRunner
+
+
+def compute_gini(model):
+    agent_wealths = [agent.wealth for agent in model.schedule.agents]
+    x = sorted(agent_wealths)
+    N = model.num_agents
+    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
+    return 1 + (1 / N) - 2 * B
+
+
+class MoneyAgent(Agent):
+    """An agent with fixed initial wealth."""
+
+    def __init__(self, unique_id, model):
+        super().__init__(unique_id, model)
+        self.wealth = 1
+
+    def move(self):
+        possible_steps = self.model.grid.get_neighborhood(
+            self.pos, moore=True, include_center=False
+        )
+        new_position = self.random.choice(possible_steps)
+        self.model.grid.move_agent(self, new_position)
+
+    def give_money(self):
+        cellmates = self.model.grid.get_cell_list_contents([self.pos])
+        if len(cellmates) > 1:
+            other = self.random.choice(cellmates)
+            other.wealth += 1
+            self.wealth -= 1
+
+    def step(self):
+        self.move()
+        if self.wealth > 0:
+            self.give_money()
+
+
+class MoneyModel(Model):
+    """A model with some number of agents."""
+
+    def __init__(self, N, width, height):
+        self.num_agents = N
+        self.grid = MultiGrid(width, height, True)
+        self.schedule = RandomActivation(self)
+        self.running = True
+
+        # Create agents
+        for i in range(self.num_agents):
+            a = MoneyAgent(i, self)
+            self.schedule.add(a)
+            # Add the agent to a random grid cell
+            x = self.random.randrange(self.grid.width)
+            y = self.random.randrange(self.grid.height)
+            self.grid.place_agent(a, (x, y))
+
+        self.datacollector = DataCollector(
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
+        )
+
+    def step(self):
+        self.datacollector.collect(self)
+        self.schedule.step()
+
+
+if __name__ == "__main__":
+
+    fixed_params = {"width": 10, "height": 10}
+    variable_params = {"N": range(10, 500, 10)}
+
+    batch_run = BatchRunner(
+        MoneyModel,
+        variable_params,
+        fixed_params,
+        iterations=5,
+        max_steps=100,
+        model_reporters={"Gini": compute_gini},
+    )
+    batch_run.run_all()
+
+    run_data = batch_run.get_model_vars_dataframe()
+    run_data.head()
+    print(run_data.Gini)

examples/newsspread/NewsSpread.yml

@@ -0,0 +1,133 @@
+---
+default_state: {}
+environment_agents: []
+environment_params:
+  prob_neighbor_spread: 0.0
+  prob_tv_spread: 0.01
+interval: 1
+max_steps: 300
+name: Sim_all_dumb
+network_agents:
+- agent_class: newsspread.DumbViewer
+  state:
+    has_tv: false
+  weight: 1
+- agent_class: newsspread.DumbViewer
+  state:
+    has_tv: true
+  weight: 1
+network_params:
+  generator: barabasi_albert_graph
+  n: 500
+  m: 5
+num_trials: 50
+---
+default_state: {}
+environment_agents: []
+environment_params:
+  prob_neighbor_spread: 0.0
+  prob_tv_spread: 0.01
+interval: 1
+max_steps: 300
+name: Sim_half_herd
+network_agents:
+- agent_class: newsspread.DumbViewer
+  state:
+    has_tv: false
+  weight: 1
+- agent_class: newsspread.DumbViewer
+  state:
+    has_tv: true
+  weight: 1
+- agent_class: newsspread.HerdViewer
+  state:
+    has_tv: false
+  weight: 1
+- agent_class: newsspread.HerdViewer
+  state:
+    has_tv: true
+  weight: 1
+network_params:
+  generator: barabasi_albert_graph
+  n: 500
+  m: 5
+num_trials: 50
+---
+default_state: {}
+environment_agents: []
+environment_params:
+  prob_neighbor_spread: 0.0
+  prob_tv_spread: 0.01
+interval: 1
+max_steps: 300
+name: Sim_all_herd
+network_agents:
+- agent_class: newsspread.HerdViewer
+  state:
+    has_tv: true
+    state_id: neutral
+  weight: 1
+- agent_class: newsspread.HerdViewer
+  state:
+    has_tv: true
+    state_id: neutral
+  weight: 1
+network_params:
+  generator: barabasi_albert_graph
+  n: 500
+  m: 5
+num_trials: 50
+---
+default_state: {}
+environment_agents: []
+environment_params:
+  prob_neighbor_spread: 0.0
+  prob_tv_spread: 0.01
+  prob_neighbor_cure: 0.1
+interval: 1
+max_steps: 300
+name: Sim_wise_herd
+network_agents:
+- agent_class: newsspread.HerdViewer
+  state:
+    has_tv: true
+    state_id: neutral
+  weight: 1
+- agent_class: newsspread.WiseViewer
+  state:
+    has_tv: true
+  weight: 1
+network_params:
+  generator: barabasi_albert_graph
+  n: 500
+  m: 5
+num_trials: 50
+---
+default_state: {}
+environment_agents: []
+environment_params:
+  prob_neighbor_spread: 0.0
+  prob_tv_spread: 0.01
+  prob_neighbor_cure: 0.1
+interval: 1
+max_steps: 300
+name: Sim_all_wise
+network_agents:
+- agent_class: newsspread.WiseViewer
+  state:
+    has_tv: true
+    state_id: neutral
+  weight: 1
+- agent_class: newsspread.WiseViewer
+  state:
+    has_tv: true
+  weight: 1
+network_params:
+  generator: barabasi_albert_graph
+  n: 500
+  m: 5
+network_params:
+  generator: barabasi_albert_graph
+  n: 500
+  m: 5
+num_trials: 50

examples/newsspread/newsspread.py

@@ -0,0 +1,85 @@
+from soil.agents import FSM, NetworkAgent, state, default_state, prob
+import logging
+
+
+class DumbViewer(FSM, NetworkAgent):
+    """
+    A viewer that gets infected via TV (if it has one) and tries to infect
+    its neighbors once it's infected.
+    """
+
+    defaults = {
+        "prob_neighbor_spread": 0.5,
+        "prob_tv_spread": 0.1,
+    }
+
+    @default_state
+    @state
+    def neutral(self):
+        if self["has_tv"]:
+            if self.prob(self.model["prob_tv_spread"]):
+                return self.infected
+
+    @state
+    def infected(self):
+        for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
+            if self.prob(self.model["prob_neighbor_spread"]):
+                neighbor.infect()
+
+    def infect(self):
+        """
+        This is not a state. It is a function that other agents can use to try to
+        infect this agent. DumbViewer always gets infected, but other agents like
+        HerdViewer might not become infected right away
+        """
+
+        self.set_state(self.infected)
+
+
+class HerdViewer(DumbViewer):
+    """
+    A viewer whose probability of infection depends on the state of its neighbors.
+    """
+
+    def infect(self):
+        """Notice again that this is NOT a state. See DumbViewer.infect for reference"""
+        infected = self.count_neighboring_agents(state_id=self.infected.id)
+        total = self.count_neighboring_agents()
+        prob_infect = self.model["prob_neighbor_spread"] * infected / total
+        self.debug("prob_infect", prob_infect)
+        if self.prob(prob_infect):
+            self.set_state(self.infected)
+
+
+class WiseViewer(HerdViewer):
+    """
+    A viewer that can change its mind.
+    """
+
+    defaults = {
+        "prob_neighbor_spread": 0.5,
+        "prob_neighbor_cure": 0.25,
+        "prob_tv_spread": 0.1,
+    }
+
+    @state
+    def cured(self):
+        prob_cure = self.model["prob_neighbor_cure"]
+        for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
+            if self.prob(prob_cure):
+                try:
+                    neighbor.cure()
+                except AttributeError:
+                    self.debug("Viewer {} cannot be cured".format(neighbor.id))
+
+    def cure(self):
+        self.set_state(self.cured.id)
+
+    @state
+    def infected(self):
+        cured = max(self.count_neighboring_agents(self.cured.id), 1.0)
+        infected = max(self.count_neighboring_agents(self.infected.id), 1.0)
+        prob_cure = self.model["prob_neighbor_cure"] * (cured / infected)
+        if self.prob(prob_cure):
+            return self.cured
+        return self.set_state(super().infected)

examples/programmatic/.gitignore

@@ -0,0 +1 @@
+Programmatic*

examples/programmatic/programmatic.py

@@ -0,0 +1,41 @@
+"""
+Example of a fully programmatic simulation, without definition files.
+"""
+from soil import Simulation, agents
+from networkx import Graph
+import logging
+
+
+def mygenerator():
+    # Add only a node
+    G = Graph()
+    G.add_node(1)
+    return G
+
+
+class MyAgent(agents.FSM):
+    @agents.default_state
+    @agents.state
+    def neutral(self):
+        self.debug("I am running")
+        if agents.prob(0.2):
+            self.info("This runs 2/10 times on average")
+
+
+s = Simulation(
+    name="Programmatic",
+    network_params={"generator": mygenerator},
+    num_trials=1,
+    max_time=100,
+    agent_class=MyAgent,
+    dry_run=True,
+)
+
+
+# By default, logging will only print WARNING logs (and above).
+# You need to choose a lower logging level to get INFO/DEBUG traces
+logging.basicConfig(level=logging.INFO)
+envs = s.run()
+
+# Uncomment this to output the simulation to a YAML file
+# s.dump_yaml('simulation.yaml')

examples/pubcrawl/README.md

@@ -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).

examples/pubcrawl/pubcrawl.py

@@ -0,0 +1,175 @@
+from soil.agents import FSM, NetworkAgent, state, default_state
+from soil import Environment
+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, NetworkAgent):
+    """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.DEBUG
+
+    pub = None
+    drunk = False
+    pints = 0
+    max_pints = 3
+    kicked_out = False
+
+    @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.model.available_pubs():
+            self.debug("We're trying to get into {}: total: {}".format(pub, len(group)))
+            if self.model.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
+        if self.kicked_out:
+            return self.at_home
+        pass  # out drun
+
+    @state
+    def at_home(self):
+        """The end"""
+        others = self.get_agents(state_id=Patron.at_home.id, limit_neighbors=True)
+        self.debug("I'm home. Just like {} of my friends".format(len(others)))
+
+    def drink(self):
+        self["pints"] += 1
+        self.debug("Cheers to that")
+
+    def kick_out(self):
+        self.kicked_out = True
+
+    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"] > self.random.random():
+            self.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"])
+        self.random.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)

examples/pubcrawl/pubcrawl.yml

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

examples/rabbits/README.md

@@ -0,0 +1,14 @@
+There are two similar implementations of this simulation.
+
+- `basic`. Using simple primites
+- `improved`. Using more advanced features such as the `time` module to avoid unnecessary computations (i.e., skip steps), and generator functions.
+
+The examples can be run directly in the terminal, and they accept command like arguments.
+For example, to enable the CSV exporter and the Summary exporter, while setting `max_time` to `100` and `seed` to `CustomSeed`:
+
+```
+python rabbit_agents.py  --set max_time=100 --csv -e summary  --set 'seed="CustomSeed"'
+```
+
+To learn more about how this functionality works, check out the `soil.easy` function.
+

examples/rabbits/basic/rabbit_agents.py

@@ -0,0 +1,150 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+from collections import Counter
+import logging
+import math
+
+
+class RabbitEnv(Environment):
+    @property
+    def num_rabbits(self):
+        return self.count_agents(agent_class=Rabbit)
+
+    @property
+    def num_males(self):
+        return self.count_agents(agent_class=Male)
+
+    @property
+    def num_females(self):
+        return self.count_agents(agent_class=Female)
+
+
+class Rabbit(NetworkAgent, FSM):
+
+    sexual_maturity = 30
+    life_expectancy = 300
+
+    @default_state
+    @state
+    def newborn(self):
+        self.info("I am a newborn.")
+        self.age = 0
+        self.offspring = 0
+        return self.youngling
+
+    @state
+    def youngling(self):
+        self.age += 1
+        if self.age >= self.sexual_maturity:
+            self.info(f"I am fertile! My age is {self.age}")
+            return self.fertile
+
+    @state
+    def fertile(self):
+        raise Exception("Each subclass should define its fertile state")
+
+    @state
+    def dead(self):
+        self.die()
+
+
+class Male(Rabbit):
+    max_females = 5
+    mating_prob = 0.001
+
+    @state
+    def fertile(self):
+        self.age += 1
+
+        if self.age > self.life_expectancy:
+            return self.dead
+
+        # Males try to mate
+        for f in self.model.agents(
+            agent_class=Female, state_id=Female.fertile.id, limit=self.max_females
+        ):
+            self.debug("FOUND A FEMALE: ", repr(f), self.mating_prob)
+            if self.prob(self["mating_prob"]):
+                f.impregnate(self)
+                break  # Take a break
+
+
+class Female(Rabbit):
+    gestation = 10
+    pregnancy = -1
+
+    @state
+    def fertile(self):
+        # Just wait for a Male
+        self.age += 1
+        if self.age > self.life_expectancy:
+            return self.dead
+        if self.pregnancy >= 0:
+            return self.pregnant
+
+    def impregnate(self, male):
+        self.info(f"impregnated by {repr(male)}")
+        self.mate = male
+        self.pregnancy = 0
+        self.number_of_babies = int(8 + 4 * self.random.random())
+
+    @state
+    def pregnant(self):
+        self.info("I am pregnant")
+        self.age += 1
+
+        if self.age >= self.life_expectancy:
+            return self.die()
+
+        if self.pregnancy < self.gestation:
+            self.pregnancy += 1
+            return
+
+        self.info("Having {} babies".format(self.number_of_babies))
+        for i in range(self.number_of_babies):
+            state = {}
+            agent_class = self.random.choice([Male, Female])
+            child = self.model.add_node(agent_class=agent_class, **state)
+            child.add_edge(self)
+            try:
+                child.add_edge(self.mate)
+                self.model.agents[self.mate].offspring += 1
+            except ValueError:
+                self.debug("The father has passed away")
+
+            self.offspring += 1
+        self.mate = None
+        self.pregnancy = -1
+        return self.fertile
+
+    def die(self):
+        if "pregnancy" in self and self["pregnancy"] > -1:
+            self.info("A mother has died carrying a baby!!")
+        return super().die()
+
+
+class RandomAccident(BaseAgent):
+    def step(self):
+        rabbits_alive = self.model.G.number_of_nodes()
+
+        if not rabbits_alive:
+            return self.die()
+
+        prob_death = self.model.get("prob_death", 1e-100) * math.floor(
+            math.log10(max(1, rabbits_alive))
+        )
+        self.debug("Killing some rabbits with prob={}!".format(prob_death))
+        for i in self.iter_agents(agent_class=Rabbit):
+            if i.state_id == i.dead.id:
+                continue
+            if self.prob(prob_death):
+                self.info("I killed a rabbit: {}".format(i.id))
+                rabbits_alive -= 1
+                i.die()
+        self.debug("Rabbits alive: {}".format(rabbits_alive))
+
+
+if __name__ == "__main__":
+    from soil import easy
+
+    with easy("rabbits.yml") as sim:
+        sim.run()

examples/rabbits/basic/rabbits.yml

@@ -0,0 +1,42 @@
+---
+version: '2'
+name: rabbits_basic
+num_trials: 1
+seed: MySeed
+description: null
+group: null
+interval: 1.0
+max_time: 100
+model_class: rabbit_agents.RabbitEnv
+model_params:
+  agents:
+    topology: true
+    distribution:
+    - agent_class: rabbit_agents.Male
+      weight: 1
+    - agent_class: rabbit_agents.Female
+      weight: 1
+    fixed:
+    - agent_class: rabbit_agents.RandomAccident
+      topology: false
+      hidden: true
+      state:
+        group: environment
+    state:
+      group: network
+      mating_prob: 0.1
+  prob_death: 0.001
+  topology:
+    fixed:
+      directed: true
+      links: []
+      nodes:
+      - id: 1
+      - id: 0
+  model_reporters:
+    num_males: 'num_males'
+    num_females: 'num_females'
+    num_rabbits: |
+      py:lambda env: env.num_males + env.num_females
+extra:
+  visualization_params: {}

examples/rabbits/improved/rabbit_agents.py

@@ -0,0 +1,157 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+from soil.time import Delta
+from enum import Enum
+from collections import Counter
+import logging
+import math
+
+
+class RabbitEnv(Environment):
+    @property
+    def num_rabbits(self):
+        return self.count_agents(agent_class=Rabbit)
+
+    @property
+    def num_males(self):
+        return self.count_agents(agent_class=Male)
+
+    @property
+    def num_females(self):
+        return self.count_agents(agent_class=Female)
+
+
+class Rabbit(FSM, NetworkAgent):
+
+    sexual_maturity = 30
+    life_expectancy = 300
+    birth = None
+
+    @property
+    def age(self):
+        if self.birth is None:
+            return None
+        return self.now - self.birth
+
+    @default_state
+    @state
+    def newborn(self):
+        self.info("I am a newborn.")
+        self.birth = self.now
+        self.offspring = 0
+        return self.youngling, Delta(self.sexual_maturity - self.age)
+
+    @state
+    def youngling(self):
+        if self.age >= self.sexual_maturity:
+            self.info(f"I am fertile! My age is {self.age}")
+            return self.fertile
+
+    @state
+    def fertile(self):
+        raise Exception("Each subclass should define its fertile state")
+
+    @state
+    def dead(self):
+        self.die()
+
+
+class Male(Rabbit):
+    max_females = 5
+    mating_prob = 0.001
+
+    @state
+    def fertile(self):
+        if self.age > self.life_expectancy:
+            return self.dead
+
+        # Males try to mate
+        for f in self.model.agents(
+            agent_class=Female, state_id=Female.fertile.id, limit=self.max_females
+        ):
+            self.debug("FOUND A FEMALE: ", repr(f), self.mating_prob)
+            if self.prob(self["mating_prob"]):
+                f.impregnate(self)
+                break  # Do not try to impregnate other females
+
+
+class Female(Rabbit):
+    gestation = 10
+    conception = None
+
+    @state
+    def fertile(self):
+        # Just wait for a Male
+        if self.age > self.life_expectancy:
+            return self.dead
+        if self.conception is not None:
+            return self.pregnant
+
+    @property
+    def pregnancy(self):
+        if self.conception is None:
+            return None
+        return self.now - self.conception
+
+    def impregnate(self, male):
+        self.info(f"impregnated by {repr(male)}")
+        self.mate = male
+        self.conception = self.now
+        self.number_of_babies = int(8 + 4 * self.random.random())
+
+    @state
+    def pregnant(self):
+        self.debug("I am pregnant")
+
+        if self.age > self.life_expectancy:
+            self.info("Dying before giving birth")
+            return self.die()
+
+        if self.pregnancy >= self.gestation:
+            self.info("Having {} babies".format(self.number_of_babies))
+            for i in range(self.number_of_babies):
+                state = {}
+                agent_class = self.random.choice([Male, Female])
+                child = self.model.add_node(agent_class=agent_class, **state)
+                child.add_edge(self)
+                if self.mate:
+                    child.add_edge(self.mate)
+                    self.mate.offspring += 1
+                else:
+                    self.debug("The father has passed away")
+
+                self.offspring += 1
+            self.mate = None
+            return self.fertile
+
+    def die(self):
+        if self.pregnancy is not None:
+            self.info("A mother has died carrying a baby!!")
+        return super().die()
+
+
+class RandomAccident(BaseAgent):
+    def step(self):
+        rabbits_alive = self.model.G.number_of_nodes()
+
+        if not rabbits_alive:
+            return self.die()
+
+        prob_death = self.model.get("prob_death", 1e-100) * math.floor(
+            math.log10(max(1, rabbits_alive))
+        )
+        self.debug("Killing some rabbits with prob={}!".format(prob_death))
+        for i in self.iter_agents(agent_class=Rabbit):
+            if i.state_id == i.dead.id:
+                continue
+            if self.prob(prob_death):
+                self.info("I killed a rabbit: {}".format(i.id))
+                rabbits_alive -= 1
+                i.die()
+        self.debug("Rabbits alive: {}".format(rabbits_alive))
+
+
+if __name__ == "__main__":
+    from soil import easy
+
+    with easy("rabbits.yml") as sim:
+        sim.run()

examples/rabbits/improved/rabbits.yml

@@ -0,0 +1,42 @@
+---
+version: '2'
+name: rabbits_improved
+num_trials: 1
+seed: MySeed
+description: null
+group: null
+interval: 1.0
+max_time: 100
+model_class: rabbit_agents.RabbitEnv
+model_params:
+  agents:
+    topology: true
+    distribution:
+    - agent_class: rabbit_agents.Male
+      weight: 1
+    - agent_class: rabbit_agents.Female
+      weight: 1
+    fixed:
+    - agent_class: rabbit_agents.RandomAccident
+      topology: false
+      hidden: true
+      state:
+        group: environment
+    state:
+      group: network
+      mating_prob: 0.1
+  prob_death: 0.001
+  topology:
+    fixed:
+      directed: true
+      links: []
+      nodes:
+      - id: 1
+      - id: 0
+  model_reporters:
+    num_males: 'num_males'
+    num_females: 'num_females'
+    num_rabbits: |
+      py:lambda env: env.num_males + env.num_females
+extra:
+  visualization_params: {}

examples/random_delays/random_delays.py

@@ -0,0 +1,43 @@
+"""
+Example of setting a 
+Example of a fully programmatic simulation, without definition files.
+"""
+from soil import Simulation, agents
+from soil.time import Delta
+
+
+class MyAgent(agents.FSM):
+    """
+    An agent that first does a ping
+    """
+
+    defaults = {"pong_counts": 2}
+
+    @agents.default_state
+    @agents.state
+    def ping(self):
+        self.info("Ping")
+        return self.pong, Delta(self.random.expovariate(1 / 16))
+
+    @agents.state
+    def pong(self):
+        self.info("Pong")
+        self.pong_counts -= 1
+        self.info(str(self.pong_counts))
+        if self.pong_counts < 1:
+            return self.die()
+        return None, Delta(self.random.expovariate(1 / 16))
+
+
+s = Simulation(
+    name="Programmatic",
+    network_agents=[{"agent_class": MyAgent, "id": 0}],
+    topology={"nodes": [{"id": 0}], "links": []},
+    num_trials=1,
+    max_time=100,
+    agent_class=MyAgent,
+    dry_run=True,
+)
+
+
+envs = s.run()

examples/template.yml

@@ -0,0 +1,30 @@
+---
+sampler:
+  method: "SALib.sample.morris.sample"
+  N: 10
+template:
+  group: simple
+  num_trials: 1
+  interval: 1
+  max_steps: 2
+  seed: "CompleteSeed!"
+  dump: false
+  model_params:
+    network_params:
+      generator: complete_graph
+      n: 10
+    network_agents:
+      - agent_class: CounterModel
+        weight: "{{ x1 }}"
+        state:
+          state_id: 0
+      - agent_class: AggregatedCounter
+        weight: "{{ 1 - x1 }}"
+    name: "{{ x3 }}"
+  skip_test: true
+vars:
+  bounds:
+    x1: [0, 1]
+    x2: [1, 2]
+  fixed:
+    x3: ["a", "b", "c"]

examples/terrorism/TerroristNetworkModel.py

@@ -0,0 +1,291 @@
+import networkx as nx
+from soil.agents import Geo, NetworkAgent, FSM, state, default_state
+from soil import Environment
+
+
+class TerroristSpreadModel(FSM, Geo):
+    """
+    Settings:
+        information_spread_intensity
+
+        terrorist_additional_influence
+
+        min_vulnerability (optional else zero)
+
+        max_vulnerability
+
+        prob_interaction
+    """
+
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=model, unique_id=unique_id, state=state)
+
+        self.information_spread_intensity = model.environment_params[
+            "information_spread_intensity"
+        ]
+        self.terrorist_additional_influence = model.environment_params[
+            "terrorist_additional_influence"
+        ]
+        self.prob_interaction = model.environment_params["prob_interaction"]
+
+        if self["id"] == self.civilian.id:  # Civilian
+            self.mean_belief = self.random.uniform(0.00, 0.5)
+        elif self["id"] == self.terrorist.id:  # Terrorist
+            self.mean_belief = self.random.uniform(0.8, 1.00)
+        elif self["id"] == self.leader.id:  # Leader
+            self.mean_belief = 1.00
+        else:
+            raise Exception("Invalid state id: {}".format(self["id"]))
+
+        if "min_vulnerability" in model.environment_params:
+            self.vulnerability = self.random.uniform(
+                model.environment_params["min_vulnerability"],
+                model.environment_params["max_vulnerability"],
+            )
+        else:
+            self.vulnerability = self.random.uniform(
+                0, model.environment_params["max_vulnerability"]
+            )
+
+    @state
+    def civilian(self):
+        neighbours = list(self.get_neighboring_agents(agent_class=TerroristSpreadModel))
+        if len(neighbours) > 0:
+            # Only interact with some of the neighbors
+            interactions = list(
+                n for n in neighbours if self.random.random() <= self.prob_interaction
+            )
+            influence = sum(self.degree(i) for i in interactions)
+            mean_belief = sum(
+                i.mean_belief * self.degree(i) / influence for i in interactions
+            )
+            mean_belief = (
+                mean_belief * self.information_spread_intensity
+                + self.mean_belief * (1 - self.information_spread_intensity)
+            )
+            self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * (
+                1 - self.vulnerability
+            )
+
+        if self.mean_belief >= 0.8:
+            return self.terrorist
+
+    @state
+    def leader(self):
+        self.mean_belief = self.mean_belief ** (1 - self.terrorist_additional_influence)
+        for neighbour in self.get_neighboring_agents(
+            state_id=[self.terrorist.id, self.leader.id]
+        ):
+            if self.betweenness(neighbour) > self.betweenness(self):
+                return self.terrorist
+
+    @state
+    def terrorist(self):
+        neighbours = self.get_agents(
+            state_id=[self.terrorist.id, self.leader.id],
+            agent_class=TerroristSpreadModel,
+            limit_neighbors=True,
+        )
+        if len(neighbours) > 0:
+            influence = sum(self.degree(n) for n in neighbours)
+            mean_belief = sum(
+                n.mean_belief * self.degree(n) / influence for n in neighbours
+            )
+            mean_belief = mean_belief * self.vulnerability + self.mean_belief * (
+                1 - self.vulnerability
+            )
+            self.mean_belief = self.mean_belief ** (
+                1 - self.terrorist_additional_influence
+            )
+
+        # Check if there are any leaders in the group
+        leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
+        if not leaders:
+            # Check if this is the potential leader
+            # Stop once it's found. Otherwise, set self as leader
+            for neighbour in neighbours:
+                if self.betweenness(self) < self.betweenness(neighbour):
+                    return
+            return self.leader
+
+    def ego_search(self, steps=1, center=False, node=None, **kwargs):
+        """Get a list of nodes in the ego network of *node* of radius *steps*"""
+        node = as_node(node if node is not None else self)
+        G = self.subgraph(**kwargs)
+        return nx.ego_graph(G, node, center=center, radius=steps).nodes()
+
+    def degree(self, node, force=False):
+        node = as_node(node)
+        if (
+            force
+            or (not hasattr(self.model, "_degree"))
+            or getattr(self.model, "_last_step", 0) < self.now
+        ):
+            self.model._degree = nx.degree_centrality(self.G)
+            self.model._last_step = self.now
+        return self.model._degree[node]
+
+    def betweenness(self, node, force=False):
+        node = as_node(node)
+        if (
+            force
+            or (not hasattr(self.model, "_betweenness"))
+            or getattr(self.model, "_last_step", 0) < self.now
+        ):
+            self.model._betweenness = nx.betweenness_centrality(self.G)
+            self.model._last_step = self.now
+        return self.model._betweenness[node]
+
+
+class TrainingAreaModel(FSM, Geo):
+    """
+    Settings:
+        training_influence
+
+        min_vulnerability
+
+    Requires TerroristSpreadModel.
+    """
+
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=model, unique_id=unique_id, state=state)
+        self.training_influence = model.environment_params["training_influence"]
+        if "min_vulnerability" in model.environment_params:
+            self.min_vulnerability = model.environment_params["min_vulnerability"]
+        else:
+            self.min_vulnerability = 0
+
+    @default_state
+    @state
+    def terrorist(self):
+        for neighbour in self.get_neighboring_agents(agent_class=TerroristSpreadModel):
+            if neighbour.vulnerability > self.min_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability ** (
+                    1 - self.training_influence
+                )
+
+
+class HavenModel(FSM, Geo):
+    """
+    Settings:
+        haven_influence
+
+        min_vulnerability
+
+        max_vulnerability
+
+    Requires TerroristSpreadModel.
+    """
+
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=model, unique_id=unique_id, state=state)
+        self.haven_influence = model.environment_params["haven_influence"]
+        if "min_vulnerability" in model.environment_params:
+            self.min_vulnerability = model.environment_params["min_vulnerability"]
+        else:
+            self.min_vulnerability = 0
+        self.max_vulnerability = model.environment_params["max_vulnerability"]
+
+    def get_occupants(self, **kwargs):
+        return self.get_neighboring_agents(agent_class=TerroristSpreadModel, **kwargs)
+
+    @state
+    def civilian(self):
+        civilians = self.get_occupants(state_id=self.civilian.id)
+        if not civilians:
+            return self.terrorist
+
+        for neighbour in self.get_occupants():
+            if neighbour.vulnerability > self.min_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability * (
+                    1 - self.haven_influence
+                )
+        return self.civilian
+
+    @state
+    def terrorist(self):
+        for neighbour in self.get_occupants():
+            if neighbour.vulnerability < self.max_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability ** (
+                    1 - self.haven_influence
+                )
+        return self.terrorist
+
+
+class TerroristNetworkModel(TerroristSpreadModel):
+    """
+    Settings:
+        sphere_influence
+
+        vision_range
+
+        weight_social_distance
+
+        weight_link_distance
+    """
+
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=model, unique_id=unique_id, state=state)
+
+        self.vision_range = model.environment_params["vision_range"]
+        self.sphere_influence = model.environment_params["sphere_influence"]
+        self.weight_social_distance = model.environment_params["weight_social_distance"]
+        self.weight_link_distance = model.environment_params["weight_link_distance"]
+
+    @state
+    def terrorist(self):
+        self.update_relationships()
+        return super().terrorist()
+
+    @state
+    def leader(self):
+        self.update_relationships()
+        return super().leader()
+
+    def update_relationships(self):
+        if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
+            close_ups = set(
+                self.geo_search(
+                    radius=self.vision_range, agent_class=TerroristNetworkModel
+                )
+            )
+            step_neighbours = set(
+                self.ego_search(
+                    self.sphere_influence,
+                    agent_class=TerroristNetworkModel,
+                    center=False,
+                )
+            )
+            neighbours = set(
+                agent.id
+                for agent in self.get_neighboring_agents(
+                    agent_class=TerroristNetworkModel
+                )
+            )
+            search = (close_ups | step_neighbours) - neighbours
+            for agent in self.get_agents(search):
+                social_distance = 1 / self.shortest_path_length(agent.id)
+                spatial_proximity = 1 - self.get_distance(agent.id)
+                prob_new_interaction = (
+                    self.weight_social_distance * social_distance
+                    + self.weight_link_distance * spatial_proximity
+                )
+                if (
+                    agent["id"] == agent.civilian.id
+                    and self.random.random() < prob_new_interaction
+                ):
+                    self.add_edge(agent)
+                    break
+
+    def get_distance(self, target):
+        source_x, source_y = nx.get_node_attributes(self.G, "pos")[self.id]
+        target_x, target_y = nx.get_node_attributes(self.G, "pos")[target]
+        dx = abs(source_x - target_x)
+        dy = abs(source_y - target_y)
+        return (dx**2 + dy**2) ** (1 / 2)
+
+    def shortest_path_length(self, target):
+        try:
+            return nx.shortest_path_length(self.G, self.id, target)
+        except nx.NetworkXNoPath:
+            return float("inf")

examples/terrorism/TerroristNetworkModel.yml

@@ -0,0 +1,62 @@
+name: TerroristNetworkModel_sim
+max_steps: 150 
+num_trials: 1
+model_params:
+  network_params:
+    generator: random_geometric_graph
+    radius: 0.2
+    # generator: geographical_threshold_graph
+    # theta: 20
+    n: 100
+  network_agents:
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
+      weight: 0.8
+      state:
+        id: civilian  # Civilians
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
+      weight: 0.1
+      state:
+        id: leader    # Leaders
+    - agent_class: TerroristNetworkModel.TrainingAreaModel
+      weight: 0.05
+      state:
+        id: terrorist # Terrorism
+    - agent_class: TerroristNetworkModel.HavenModel
+      weight: 0.05
+      state:
+        id: civilian  # Civilian
+
+  # TerroristSpreadModel
+  information_spread_intensity: 0.7
+  terrorist_additional_influence: 0.035
+  max_vulnerability: 0.7
+  prob_interaction: 0.5
+
+  # TrainingAreaModel and HavenModel
+  training_influence: 0.20
+  haven_influence: 0.20
+
+  # TerroristNetworkModel
+  vision_range: 0.30
+  sphere_influence: 2
+  weight_social_distance: 0.035
+  weight_link_distance: 0.035
+
+visualization_params:
+  # Icons downloaded from https://www.iconfinder.com/
+  shape_property: agent
+  shapes:
+    TrainingAreaModel: target
+    HavenModel: home
+    TerroristNetworkModel: person
+  colors:
+    - attr_id: civilian
+      color: '#40de40'
+    - attr_id: terrorist
+      color: red
+    - attr_id: leader
+      color: '#c16a6a'
+  background_image: 'map_4800x2860.jpg'
+  background_opacity: '0.9'
+  background_filter_color: 'blue'
+skip_test: true  # This simulation takes too long for automated tests.

examples/torvalds.yml

@@ -1,14 +1,15 @@
 ---
 name: torvalds_example
-max_time: 1
+max_steps: 10
 interval: 2
-agent_type: CounterModel
-default_state:
-  skill_level: 'beginner'
-network_params:
-  path: 'torvalds.edgelist'
-states:
-  Torvalds:
-    skill_level: 'God'
-  balkian:
-    skill_level: 'developer'
+model_params:
+  agent_class: CounterModel
+  default_state:
+    skill_level: 'beginner'
+  network_params:
+    path: 'torvalds.edgelist'
+  states:
+    Torvalds:
+      skill_level: 'God'
+    balkian:
+      skill_level: 'developer'

models_org.py

@@ -1,596 +0,0 @@
-from nxsim import BaseNetworkAgent
-import numpy as np
-import random
-import settings
-
-settings.init()
-
-##############################
-# Variables initialization #
-##############################
-def init():
-    global networkStatus
-    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
-
-
-####################
-# Available models #
-####################
-
-class BaseBehaviour(BaseNetworkAgent):
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self._attrs = {}
-
-    @property
-    def attrs(self):
-        now = self.env.now
-        if now not in self._attrs:
-            self._attrs[now] = {}
-        return self._attrs[now]
-
-    @attrs.setter
-    def attrs(self, value):
-        self._attrs[self.env.now] = value
-
-    def run(self):
-        while True:
-            self.step(self.env.now)
-            yield self.env.timeout(settings.timeout)
-
-    def step(self, now):
-        networkStatus['agent_%s'% self.id] = self.to_json()
-
-    def to_json(self):
-        final = {}
-        for stamp, attrs in self._attrs.items():
-            for a in attrs:
-                if a not in final:
-                   final[a] = {}
-                final[a][stamp] = attrs[a]
-        return final
-
-class ControlModelM2(BaseBehaviour):
-    #Init infected
-    init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}
-    init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}
-
-    # Init beacons
-    init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
-    init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance)
-
-        self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance)
-
-        self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance)
-        self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance)
-
-        self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance)
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance)
-        self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance)
-
-    def step(self, now):
-
-        if self.state['id'] == 0:  #Neutral
-            self.neutral_behaviour()
-        elif self.state['id'] == 1:  #Infected
-            self.infected_behaviour()
-        elif self.state['id'] == 2:  #Cured
-            self.cured_behaviour()
-        elif self.state['id'] == 3:  #Vaccinated
-            self.vaccinated_behaviour()
-        elif self.state['id'] == 4:  #Beacon-off
-            self.beacon_off_behaviour()
-        elif self.state['id'] == 5:  #Beacon-on
-            self.beacon_on_behaviour()
-
-        self.attrs['status'] = self.state['id']
-        super().step(now)
-
-
-    def neutral_behaviour(self):
-
-        # Infected
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        if len(infected_neighbors)>0:
-            if random.random() < self.prob_neutral_making_denier:
-                self.state['id'] = 3   # Vaccinated making denier
-
-    def infected_behaviour(self):
-
-        # Neutral
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
-                neighbor.state['id'] = 1  # Infected
-
-    def cured_behaviour(self):
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-
-    def vaccinated_behaviour(self):
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Generate anti-rumor
-        infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
-
-    def beacon_off_behaviour(self):
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        if len(infected_neighbors) > 0:
-            self.state['id'] == 5  #Beacon on
-
-    def beacon_on_behaviour(self):
-
-        # Cure (M2 feature added)
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
-            neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
-            for neighbor in neutral_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
-                    neighbor.state['id'] = 3  # Vaccinated
-            infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
-            for neighbor in infected_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
-                    neighbor.state['id'] = 2  # Cured
-
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-
-class SpreadModelM2(BaseBehaviour):
-    init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
-    init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance)
-
-        self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance)
-
-        self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance)
-        self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance)
-
-        self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance)
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance)
-        self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance)
-
-    def step(self, now):
-
-        if self.state['id'] == 0:  #Neutral
-            self.neutral_behaviour()
-        elif self.state['id'] == 1:  #Infected
-            self.infected_behaviour()
-        elif self.state['id'] == 2:  #Cured
-            self.cured_behaviour()
-        elif self.state['id'] == 3:  #Vaccinated
-            self.vaccinated_behaviour()
-
-        self.attrs['status'] = self.state['id']
-        super().step(now)
-
-
-    def neutral_behaviour(self):
-
-        # Infected
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        if len(infected_neighbors)>0:
-            if random.random() < self.prob_neutral_making_denier:
-                self.state['id'] = 3   # Vaccinated making denier
-
-    def infected_behaviour(self):
-
-        # Neutral
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
-                neighbor.state['id'] = 1  # Infected
-
-    def cured_behaviour(self):
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-
-    def vaccinated_behaviour(self):
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Generate anti-rumor
-        infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
-
-
-class SISaModel(BaseBehaviour):
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.neutral_discontent_spon_prob = np.random.normal(settings.neutral_discontent_spon_prob, settings.standard_variance)
-        self.neutral_discontent_infected_prob = np.random.normal(settings.neutral_discontent_infected_prob,settings.standard_variance)
-        self.neutral_content_spon_prob = np.random.normal(settings.neutral_content_spon_prob,settings.standard_variance)
-        self.neutral_content_infected_prob = np.random.normal(settings.neutral_content_infected_prob,settings.standard_variance)
-
-        self.discontent_neutral = np.random.normal(settings.discontent_neutral,settings.standard_variance)
-        self.discontent_content = np.random.normal(settings.discontent_content,settings.variance_d_c)
-
-        self.content_discontent = np.random.normal(settings.content_discontent,settings.variance_c_d)
-        self.content_neutral = np.random.normal(settings.content_neutral,settings.standard_variance)
-
-    def step(self, now):
-
-        if self.state['id'] == 0:
-            self.neutral_behaviour()
-        if self.state['id'] == 1:
-            self.discontent_behaviour()
-        if self.state['id'] == 2:
-            self.content_behaviour()
-
-        self.attrs['status'] = self.state['id']
-        super().step(now)
-
-
-    def neutral_behaviour(self):
-
-        #Spontaneus effects
-        if random.random() < self.neutral_discontent_spon_prob:
-            self.state['id'] = 1
-        if random.random() < self.neutral_content_spon_prob:
-            self.state['id'] = 2
-
-        #Infected
-        discontent_neighbors = self.get_neighboring_agents(state_id=1)
-        if random.random() < len(discontent_neighbors)*self.neutral_discontent_infected_prob:
-            self.state['id'] = 1
-        content_neighbors = self.get_neighboring_agents(state_id=2)
-        if random.random() < len(content_neighbors)*self.neutral_content_infected_prob:
-            self.state['id'] = 2
-
-    def discontent_behaviour(self):
-
-        #Healing
-        if random.random() < self.discontent_neutral:
-            self.state['id'] = 0
-
-        #Superinfected
-        content_neighbors = self.get_neighboring_agents(state_id=2)
-        if random.random() < len(content_neighbors)*self.discontent_content:
-            self.state['id'] = 2
-
-    def content_behaviour(self):
-
-        #Healing
-        if random.random() < self.content_neutral:
-            self.state['id'] = 0
-
-        #Superinfected
-        discontent_neighbors = self.get_neighboring_agents(state_id=1)
-        if random.random() < len(discontent_neighbors)*self.content_discontent:
-            self.state['id'] = 1
-
-
-class BigMarketModel(BaseBehaviour):
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.enterprises = settings.enterprises
-        self.type = ""
-        self.number_of_enterprises = len(settings.enterprises)
-
-        if self.id < self.number_of_enterprises: #Enterprises
-            self.state['id']=self.id
-            self.type="Enterprise"
-            self.tweet_probability = settings.tweet_probability_enterprises[self.id]
-        else:                       #normal users
-            self.state['id']=self.number_of_enterprises
-            self.type="User"
-            self.tweet_probability = settings.tweet_probability_users
-            self.tweet_relevant_probability = settings.tweet_relevant_probability
-            self.tweet_probability_about = settings.tweet_probability_about #List
-            self.sentiment_about = settings.sentiment_about #List
-
-    def step(self, now):
-
-        if(self.id < self.number_of_enterprises): # Ennterprise
-            self.enterpriseBehaviour()
-        else:  # Usuario
-            self.userBehaviour()
-            for i in range(self.number_of_enterprises):       # So that it never is set to 0 if there are not changes (logs)
-                self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
-
-        super().step(now)
-
-    def enterpriseBehaviour(self):
-
-        if random.random()< self.tweet_probability: #Tweets
-            aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbour users
-            for x in aware_neighbors:
-                if random.uniform(0,10) < 5:
-                    x.sentiment_about[self.id] += 0.1 #Increments for enterprise
-                else:
-                    x.sentiment_about[self.id] -= 0.1 #Decrements for enterprise
-
-                # Establecemos limites
-                if x.sentiment_about[self.id] > 1:
-                    x.sentiment_about[self.id] = 1
-                if x.sentiment_about[self.id]< -1:
-                    x.sentiment_about[self.id] = -1
-
-                x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
-
-
-    def userBehaviour(self):
-
-        if random.random() < self.tweet_probability: #Tweets
-            if random.random() < self.tweet_relevant_probability: #Tweets something relevant
-                #Tweet probability per enterprise
-                for i in range(self.number_of_enterprises):
-                    random_num = random.random()
-                    if random_num < self.tweet_probability_about[i]:
-                        #The condition is fulfilled, sentiments are evaluated towards that enterprise
-                        if self.sentiment_about[i] < 0:
-                            #NEGATIVO
-                            self.userTweets("negative",i)
-                        elif self.sentiment_about[i] == 0:
-                            #NEUTRO
-                            pass
-                        else:
-                            #POSITIVO
-                            self.userTweets("positive",i)
-
-    def userTweets(self,sentiment,enterprise):
-        aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbours users
-        for x in aware_neighbors:
-            if sentiment == "positive":
-                x.sentiment_about[enterprise] +=0.003
-            elif sentiment == "negative":
-                x.sentiment_about[enterprise] -=0.003
-            else:
-                pass
-
-            # Establecemos limites
-            if x.sentiment_about[enterprise] > 1:
-                x.sentiment_about[enterprise] = 1
-            if x.sentiment_about[enterprise] < -1:
-                x.sentiment_about[enterprise] = -1
-
-            x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]
-
-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 = settings.outside_effects_prob
-        self.anger_prob = settings.anger_prob
-        self.joy_prob = settings.joy_prob
-        self.sadness_prob = settings.sadness_prob
-        self.disgust_prob = settings.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()
-        super().step(now)
-
-    def behaviour(self):
-
-        angry_neighbors_1_time_step=[]
-        joyful_neighbors_1_time_step=[]
-        sad_neighbors_1_time_step=[]
-        disgusted_neighbors_1_time_step=[]
-
-
-        angry_neighbors = self.get_neighboring_agents(state_id=1)
-        for x in angry_neighbors:
-            if x.time_awareness[0] > (self.env.now-500):
-                angry_neighbors_1_time_step.append(x)
-        num_neighbors_angry = len(angry_neighbors_1_time_step)
-
-
-        joyful_neighbors = self.get_neighboring_agents(state_id=2)
-        for x in joyful_neighbors:
-            if x.time_awareness[1] > (self.env.now-500):
-                joyful_neighbors_1_time_step.append(x)
-        num_neighbors_joyful = len(joyful_neighbors_1_time_step)
-
-
-        sad_neighbors = self.get_neighboring_agents(state_id=3)
-        for x in sad_neighbors:
-            if x.time_awareness[2] > (self.env.now-500):
-                sad_neighbors_1_time_step.append(x)
-        num_neighbors_sad = len(sad_neighbors_1_time_step)
-
-
-        disgusted_neighbors = self.get_neighboring_agents(state_id=4)
-        for x in disgusted_neighbors:
-            if x.time_awareness[3] > (self.env.now-500):
-                disgusted_neighbors_1_time_step.append(x)
-        num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
-
-
-        anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
-        joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
-        sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
-        disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
-        outside_effects_prob= settings.outside_effects_prob
-
-
-        num = random.random()
-
-
-        if(num<outside_effects_prob):
-            self.state['id'] = random.randint(1,4)
-
-            sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] #It is stored when it has been infected for the dynamic network
-            self.time_awareness[self.state['id']-1] = self.env.now
-            self.attrs['sentiment'] = self.state['id']
-
-
-
-        if(num<anger_prob):
-
-            self.state['id'] = 1
-            sentimentCorrelationNodeArray[self.id][self.env.now]=1
-            self.time_awareness[self.state['id']-1] = self.env.now
-        elif (num<joy_prob+anger_prob and num>anger_prob):
-
-            self.state['id'] = 2
-            sentimentCorrelationNodeArray[self.id][self.env.now]=2
-            self.time_awareness[self.state['id']-1] = self.env.now
-        elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
-
-
-            self.state['id'] = 3
-            sentimentCorrelationNodeArray[self.id][self.env.now]=3
-            self.time_awareness[self.state['id']-1] = self.env.now
-        elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
-
-
-            self.state['id'] = 4
-            sentimentCorrelationNodeArray[self.id][self.env.now]=4
-            self.time_awareness[self.state['id']-1] = self.env.now
-
-        self.attrs['sentiment'] = self.state['id']
-
-
-class BassModel(BaseBehaviour):
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.innovation_prob = settings.innovation_prob
-        self.imitation_prob = settings.imitation_prob
-        sentimentCorrelationNodeArray[self.id][self.env.now]=0
-
-    def step(self, now):
-        self.behaviour()
-        super().step(now)
-
-    def behaviour(self):
-        #Outside effects
-        if random.random() < settings.innovation_prob:
-            if self.state['id'] == 0:
-                self.state['id'] = 1
-                sentimentCorrelationNodeArray[self.id][self.env.now]=1
-            else:
-                pass
-
-            self.attrs['status'] = self.state['id']
-            return
-
-        #Imitation effects
-        if self.state['id'] == 0:
-            aware_neighbors = self.get_neighboring_agents(state_id=1)
-            num_neighbors_aware = len(aware_neighbors)
-            if random.random() < (settings.imitation_prob*num_neighbors_aware):
-                self.state['id'] = 1
-                sentimentCorrelationNodeArray[self.id][self.env.now]=1
-
-            else:
-                pass
-            self.attrs['status'] = self.state['id']
-
-
-class IndependentCascadeModel(BaseBehaviour):
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.innovation_prob = settings.innovation_prob
-        self.imitation_prob = settings.imitation_prob
-        self.time_awareness = 0
-        sentimentCorrelationNodeArray[self.id][self.env.now]=0
-
-    def step(self,now):
-        self.behaviour()
-        super().step(now)
-
-    def behaviour(self):
-        aware_neighbors_1_time_step=[]
-            #Outside effects
-        if random.random() < settings.innovation_prob:
-            if self.state['id'] == 0:
-                self.state['id'] = 1
-                sentimentCorrelationNodeArray[self.id][self.env.now]=1
-                self.time_awareness = self.env.now #To know when they have been infected
-            else:
-                pass
-
-            self.attrs['status'] = self.state['id']
-            return
-
-        #Imitation effects
-        if self.state['id'] == 0:
-            aware_neighbors = self.get_neighboring_agents(state_id=1)
-            for x in aware_neighbors:
-                if x.time_awareness == (self.env.now-1):
-                    aware_neighbors_1_time_step.append(x)
-            num_neighbors_aware = len(aware_neighbors_1_time_step)
-            if random.random() < (settings.imitation_prob*num_neighbors_aware):
-                self.state['id'] = 1
-                sentimentCorrelationNodeArray[self.id][self.env.now]=1
-            else:
-                pass
-
-            self.attrs['status'] = self.state['id']
-            return

requirements.txt

@@ -1,6 +1,10 @@
-nxsim
-simpy
-networkx
+networkx>=2.5
 numpy
 matplotlib
-pyyaml
+pyyaml>=5.1
+pandas>=1
+SALib>=1.3
+Jinja2
+Mesa>=1.1
+pydantic>=1.9
+sqlalchemy>=1.4

setup.cfg

@@ -0,0 +1,4 @@
+[aliases]
+test=pytest
+[tool:pytest]
+addopts = --verbose

setup.py

@@ -1,20 +1,27 @@
-import pip
+import os
 from setuptools import setup
-# parse_requirements() returns generator of pip.req.InstallRequirement objects
-from pip.req import parse_requirements
-from soil import __version__
 
-try:
-    install_reqs = parse_requirements(
-        "requirements.txt", session=pip.download.PipSession())
-    test_reqs = parse_requirements(
-        "test-requirements.txt", session=pip.download.PipSession())
-except AttributeError:
-    install_reqs = parse_requirements("requirements.txt")
-    test_reqs = parse_requirements("test-requirements.txt")
 
-install_reqs = [str(ir.req) for ir in install_reqs]
-test_reqs = [str(ir.req) for ir in test_reqs]
+with open(os.path.join('soil', 'VERSION')) as f:
+    __version__ = f.readlines()[0].strip()
+    assert __version__
+
+
+def parse_requirements(filename):
+    """ load requirements from a pip requirements file """
+    with open(filename, 'r') as f:
+        lineiter = list(line.strip() for line in f)
+    return [line for line in lineiter if line and not line.startswith("#")]
+
+
+install_reqs = parse_requirements("requirements.txt")
+test_reqs = parse_requirements("test-requirements.txt")
+extras_require={
+    'mesa': ['mesa>=0.8.9'],
+    'geo': ['scipy>=1.3'],
+    'web': ['tornado']
+}
+extras_require['all'] = [dep for package in extras_require.values() for dep in package]
 
 
 setup(
@@ -28,12 +35,24 @@ setup(
     download_url='https://github.com/gsi-upm/soil/archive/{}.tar.gz'.format(
         __version__),
     keywords=['agent', 'social', 'simulator'],
-    classifiers=[],
+    classifiers=[
+        'Development Status :: 5 - Production/Stable',
+        'Environment :: Console',
+        'Intended Audience :: End Users/Desktop',
+        'Intended Audience :: Developers',
+        'License :: OSI Approved :: Apache Software License',
+        'Operating System :: MacOS :: MacOS X',
+        'Operating System :: Microsoft :: Windows',
+        'Operating System :: POSIX',
+        'Programming Language :: Python :: 3'],
     install_requires=install_reqs,
+    extras_require=extras_require,
     tests_require=test_reqs,
     setup_requires=['pytest-runner', ],
+    pytest_plugins = ['pytest_profiling'],
     include_package_data=True,
     entry_points={
         'console_scripts':
-        ['soil = soil.__init__:main']
+        ['soil = soil.__main__:main',
+        'soil-web = soil.web.__init__:main']
     })