Update tutorial and fix plotting bug

* Improve text
* Move to docs
* Autogenerate with sphinx
* Fix naming issue `environment.run` (double name)
* Add to tests

.dockerignore

@@ -0,0 +1,7 @@
+**/soil_output
+.*
+**/.*
+**/__pycache__
+__pycache__
+*.pyc
+**/backup

.gitignore

@@ -8,3 +8,5 @@ soil_output
 docs/_build*
 build/*
 dist/*
+prof
+backup

.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:3.8
+  stage: test
+  script:
+    - pip install -r requirements.txt -r test-requirements.txt
+    - python setup.py test
+
+push_pypi:
+  only:
+    - tags
+  tags:
+    - docker
+  image: python:3.8
+  stage: publish
+  script:
+    - echo $CI_COMMIT_TAG > soil/VERSION
+    - pip install twine
+    - python setup.py sdist bdist_wheel
+    - TWINE_PASSWORD=$PYPI_PASSWORD TWINE_USERNAME=$PYPI_USERNAME python -m twine upload dist/*
+
+check_pypi:
+  only:
+    - tags
+  tags:
+    - docker
+  image: python:3.8
+  stage: check_published
+  script:
+    - pip install soil==$CI_COMMIT_TAG
+  # Allow PYPI to update its index before we try to install 
+  when: delayed
+  start_in: 2 minutes

CHANGELOG.md

@@ -0,0 +1,196 @@
+# 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).
+
+## [1.0 UNRELEASED]
+
+Version 1.0 introduced multiple changes, especially on the `Simulation` class and anything related to how configuration is handled.
+For an explanation of the general changes in version 1.0, please refer to the file `docs/notes_v1.0.rst`.
+
+### Added
+* 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).
+* Environments now have a class method to make them easier to use without a simulation`.run`. Notice that this is different from `run_model`, which is an instance method.
+* Ability to run simulations using mesa models
+* The `soil.exporters` module to export the results of datacollectors (`model.datacollector`) into files at the end of trials/simulations
+* Agents can now have generators as a step function or a state. They work similar to normal functions, with one caveat in the case of `FSM`: only `time` values (or None) 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.
+* Simulations can now specify a `matrix` with possible values for every simulation parameter. The final parameters will be calculated based on the `parameters` used and a cartesian product (i.e., all possible combinations) of each parameter.
+* 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>`
+### Changed
+* Configuration schema (`Simulation`) is very simplified. All simulations should be checked
+* Model / environment variables are expected (but not enforced) to be a single value. This is done to more closely align with mesa
+* `Exporter.iteration_end` now takes two parameters: `env` (same as before) and `params` (specific parameters for this environment). We considered including a `parameters` attribute in the environment, but this would not be compatible with mesa.
+* `num_trials` renamed to `iterations`
+* General renaming of `trial` to `iteration`, to work better with `mesa`
+* `model_parameters` renamed to `parameters` in simulation
+* Simulation results for every iteration of a simulation with the same name are stored in a single `sqlite` database
+
+### 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.
+
+## [0.20.8]
+### Changed
+* Tsih bumped to version 0.1.8
+### Fixed
+* Mentions to `id` in docs. It should be `state_id` now.
+* Fixed bug: environment agents were not being added to the simulation
+
+## [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

@@ -0,0 +1,7 @@
+include requirements.txt
+include test-requirements.txt
+include README.rst
+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

@@ -1,12 +1,91 @@
-#[Soil](https://github.com/gsi-upm/soil)
-
-The purpose of Soil (SOcial network sImuLator) is provding an Agent-based Social Simulator written in Python for Social Networks.
+# [SOIL](https://github.com/gsi-upm/soil)
 
 
-In order to see quickly how to use Soil, you can follow the following [tutorial](https://github.com/gsi-upm/soil/blob/master/soil_tutorial.ipynb).
+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](docs/tutorial/soil_tutorial.ipynb) to develop your own agent models.
+
+> **Warning**
+> Soil 1.0 introduced many fundamental changes. Check the [documention on how to update your simulations to work with newer versions](docs/notes_v1.0.rst)
+
+## Features
+
+* Integration with (social) networks (through `networkx`)
+* Convenience functions and methods to easily assign agents to your model (and optionally to its network):
+  * Following a given distribution (e.g., 2 agents of type `Foo`, 10% of the network should be agents of type `Bar`)
+  * Based on the topology of the network
+* **Several types of abstractions for agents**:
+  * Finite state machine, where methods can be turned into a state
+  * Network agents, which have convenience methods to access the model's topology
+  * Generator-based agents, whose state is paused though a `yield` and resumed on the next step
+* **Reporting and data collection**:
+  * Soil models include data collection and record some data by default (# of agents, state of each agent, etc.)
+  * All data collected are exported by default to a SQLite database and a description file
+  * Options to export to other formats, such as CSV, or defining your own exporters
+  * A summary of the data collected is shown in the command line, for easy inspection
+* **An event-based scheduler**
+  * Agents can be explicit about when their next time/step should be, and not all agents run in every step. This avoids unnecessary computation.
+  * Time intervals between each step are flexible.
+  * There are primitives to specify when the next execution of an agent should be (or conditions)
+* **Actor-inspired** message-passing
+* A simulation runner (`soil.Simulation`) that can:
+  * Run models in parallel
+  * Save results to different formats
+* Simulation configuration files 
+* A command line interface (`soil`), to quickly run simulations with different parameters
+* An integrated debugger (`soil --debug`) with custom functions to print agent states and break at specific states
+
+## Mesa compatibility
+
+SOIL has been redesigned to integrate well with [Mesa](https://github.com/projectmesa/mesa).
+For instance, it should be possible to run a `mesa.Model` models using a `soil.Simulation` and the `soil` CLI, or to integrate the `soil.TimedActivation` scheduler on a `mesa.Model`.
+
+Note that some combinations of `mesa` and `soil` components, while technically possible, are much less useful or might yield surprising results.
+For instance, you may add any `soil.agent` agent on a regular `mesa.Model` with a vanilla scheduler from `mesa.time`.
+But in that case the agents will not get any of the advanced event-based scheduling, and most agent behaviors that depend on that may not work. 
+
+
+## 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.
 
 
 
+## Citation 
 
 
-@Copyright GSI - Universidad Politécnica de Madrid 2017
+If you use Soil in your research, don't forget to cite this paper:
+
+```bibtex
+@inbook{soil-gsi-conference-2017,
+    author = "S{\'a}nchez, Jes{\'u}s M. and Iglesias, Carlos A. and S{\'a}nchez-Rada, J. Fernando",
+    booktitle = "Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection",
+    doi = "10.1007/978-3-319-59930-4_19",
+    editor = "Demazeau Y., Davidsson P., Bajo J., Vale Z.",
+    isbn = "978-3-319-59929-8",
+    keywords = "soil;social networks;agent based social simulation;python",
+    month = "June",
+    organization = "PAAMS 2017",
+    pages = "234-245",
+    publisher = "Springer Verlag",
+    series = "LNAI",
+    title = "{S}oil: {A}n {A}gent-{B}ased {S}ocial {S}imulator in {P}ython for {M}odelling and {S}imulation of {S}ocial {N}etworks",
+    url = "https://link.springer.com/chapter/10.1007/978-3-319-59930-4_19",
+    volume = "10349",
+    year = "2017",
+}
+
+```
+
+@Copyright GSI - Universidad Politécnica de Madrid 2017-2021
+
+[![SOIL](logo_gsi.png)](https://www.gsi.upm.es)

debug.py

@@ -0,0 +1,3 @@
+import soil
+soil.main()
+import pdb

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 +31,10 @@
 # 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",
+    "nbsphinx",
+]
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
@@ -64,12 +67,12 @@ release = '0.1'
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # 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'
@@ -152,6 +155,3 @@ texinfo_documents = [
      author, 'Soil', 'One line description of project.',
      'Miscellaneous'),
 ]
-
-
-

docs/example.yml

@@ -0,0 +1,40 @@
+---
+name: MyExampleSimulation
+max_time: 50
+num_trials: 3
+interval: 2
+model_params:
+  topology:
+    params:
+      generator: barabasi_albert_graph
+      n: 100
+      m: 2
+  agents:
+    distribution:
+    - agent_class: SISaModel
+      topology: True
+      ratio: 0.1
+      state:
+        state_id: content
+    - agent_class: SISaModel
+      topology: True
+      ratio: .1
+      state:
+        state_id: discontent
+    - agent_class: SISaModel
+      topology: True
+      ratio: 0.8
+      state:
+        state_id: neutral
+  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

@@ -1,21 +1,56 @@
-.. Soil documentation master file, created by
-   sphinx-quickstart on Tue Apr 25 12:48:56 2017.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
 Welcome to Soil's documentation!
 ================================
 
-Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks.
+Soil is an opinionated Agent-based Social Simulator in Python focused on Social Networks.
+To get started developing your own simulations and agent behaviors, check out our :doc:`Tutorial <soil_tutorial>` and the `examples on GitHub <https://github.com/gsi-upm/soil/tree/master/examples>`.
+
+Soil can be installed through pip (see more details in the :doc:`installation` page):.
+
+.. image:: soil.png
+  :width: 80%
+  :align: center
+
+
+.. code:: bash
+
+    pip install soil
+
+
+
+If you use Soil in your research, do not forget to cite this paper:
+
+.. code:: bibtex
+
+    @inbook{soil-gsi-conference-2017,
+        author = "S{\'a}nchez, Jes{\'u}s M. and Iglesias, Carlos A. and S{\'a}nchez-Rada, J. Fernando",
+        booktitle = "Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection",
+        doi = "10.1007/978-3-319-59930-4_19",
+        editor = "Demazeau Y., Davidsson P., Bajo J., Vale Z.",
+        isbn = "978-3-319-59929-8",
+        keywords = "soil;social networks;agent based social simulation;python",
+        month = "June",
+        organization = "PAAMS 2017",
+        pages = "234-245",
+        publisher = "Springer Verlag",
+        series = "LNAI",
+        title = "{S}oil: {A}n {A}gent-{B}ased {S}ocial {S}imulator in {P}ython for {M}odelling and {S}imulation of {S}ocial {N}etworks",
+        url = "https://link.springer.com/chapter/10.1007/978-3-319-59930-4_19",
+        volume = "10349",
+        year = "2017",
+    }
+
+
+
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 0
    :caption: Learn more about soil:
 
    installation
-   usage
-   models
+   Tutorial <tutorial/soil_tutorial>
+   notes_v1.0
 
+..
 
 
 .. Indices and tables

docs/installation.rst

@@ -1,7 +1,65 @@
 Installation
 ------------
-The latest version can be installed through GitLab.
+
+Through pip
+===========
+
+The easiest way to install Soil is through pip, with Python >= 3.8:
 
 .. code:: bash
 
-   git clone https://lab.cluster.gsi.dit.upm.es/soil/soil.git
+   pip install soil
+
+
+Now test that it worked by running the command line tool
+
+.. code:: bash
+
+   soil --help
+
+   #or
+
+   python -m soil --help
+
+Or, if you're using using soil programmatically:
+
+.. code:: python
+
+   import soil
+   print(soil.__version__)
+
+
+
+Web UI
+======
+
+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
+
+
+Development
+===========
+
+The latest version can be downloaded from `GitHub <https://github.com/gsi-upm/soil>`_ and installed manually:
+
+.. code:: bash
+
+   git clone https://github.com/gsi-upm/soil
+   cd soil
+   python -m venv .venv
+   source .venv/bin/activate
+   pip install --editable .

docs/make.bat

@@ -12,7 +12,7 @@ set BUILDDIR=_build
 set SPHINXPROJ=Soil
 
 if "%1" == "" goto help
-
+eE
 %SPHINXBUILD% >NUL 2>NUL
 if errorlevel 9009 (
 	echo.

docs/mesa.rst

@@ -0,0 +1,22 @@
+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

docs/models.rst

@@ -1,112 +0,0 @@
-Developing new models
----------------------
-This document describes how to develop a new analysis model.
-
-What is a model?
-================
-
-A model defines the behaviour of the agents with a view to assessing their effects on the system as a whole.
-In practice, a model consists of at least two parts:
-
-* Python module: the actual code that describes the behaviour.
-* Setting up the variables in the Settings JSON file.
-
-This separation allows us to run the simulation with different agents.
-
-Models Code
-===========
-
-All the models are imported to the main file. The initialization look like this:
-
-.. code:: python
-
-    import settings
-
-    networkStatus = {}  # Dict that will contain the status of every agent in the network
-
-    sentimentCorrelationNodeArray = []
-    for x in range(0, settings.network_params["number_of_nodes"]):
-        sentimentCorrelationNodeArray.append({'id': x})
-    # Initialize agent states. Let's assume everyone is normal.
-    init_states = [{'id': 0, } for _ in range(settings.network_params["number_of_nodes"])]
-        # add keys as as necessary, but "id" must always refer to that state category
-
-A new model have to inherit the BaseBehaviour class which is in the same module.
-There are two basics methods:
-
-* __init__
-* step: used to define the behaviour over time.
-
-Variable Initialization
-=======================
-
-The different parameters of the model have to be initialize in the Simulation Settings JSON file which will be
-passed as a parameter to the simulation.
-
-.. code:: json
-
-    {
-        "agent": ["SISaModel","ControlModelM2"],
-
-        "neutral_discontent_spon_prob": 0.04,
-        "neutral_discontent_infected_prob": 0.04,
-        "neutral_content_spon_prob": 0.18,
-        "neutral_content_infected_prob": 0.02,
-
-        "discontent_neutral": 0.13,
-        "discontent_content": 0.07,
-        "variance_d_c": 0.02,
-
-        "content_discontent": 0.009,
-        "variance_c_d": 0.003,
-        "content_neutral": 0.088,
-
-        "standard_variance": 0.055,
-
-
-        "prob_neutral_making_denier": 0.035,
-
-        "prob_infect": 0.075,
-
-        "prob_cured_healing_infected": 0.035,
-        "prob_cured_vaccinate_neutral": 0.035,
-
-        "prob_vaccinated_healing_infected": 0.035,
-        "prob_vaccinated_vaccinate_neutral": 0.035,
-        "prob_generate_anti_rumor": 0.035
-    }
-
-In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
-The simulation returns one result for each model, executing each model separately. For the usage, see :doc:`usage`.
-
-Example Model
-=============
-
-In this section, we will implement a Sentiment Correlation Model.
-
-The class would look like this:
-
-.. code:: python
-
-    from ..BaseBehaviour import *
-    from .. import sentimentCorrelationNodeArray
-
-    class SentimentCorrelationModel(BaseBehaviour):
-
-        def __init__(self, environment=None, agent_id=0, state=()):
-            super().__init__(environment=environment, agent_id=agent_id, state=state)
-            self.outside_effects_prob = environment.environment_params['outside_effects_prob']
-            self.anger_prob = environment.environment_params['anger_prob']
-            self.joy_prob = environment.environment_params['joy_prob']
-            self.sadness_prob = environment.environment_params['sadness_prob']
-            self.disgust_prob = environment.environment_params['disgust_prob']
-            self.time_awareness = []
-            for i in range(4):  # In this model we have 4 sentiments
-                self.time_awareness.append(0)  # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
-            sentimentCorrelationNodeArray[self.id][self.env.now] = 0
-
-        def step(self, now):
-            self.behaviour()  # Method which define the behaviour
-            super().step(now)
-
-The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file.

docs/notes_v1.0.rst

@@ -0,0 +1,38 @@
+Upgrading to Soil 1.0
+---------------------
+
+What are the main changes in version 1.0?
+#########################################
+
+Version 1.0 is a major rewrite of the Soil system, focused on simplifying the API, aligning it with Mesa, and making it easier to use.
+Unfortunately, this comes at the cost of backwards compatibility.
+
+We drew several lessons from the previous version of Soil, and tried to address them in this version.
+Mainly:
+
+- The split between simulation configuration and simulation code was overly complicated for most use cases. As a result, most users ended up reusing configuration.
+- Storing **all** the simulation data in a database is costly and unnecessary for most use cases. For most use cases, only a handful of variables need to be stored. This fits nicely with Mesa's data collection system.
+- The API was too complex, and it was difficult to understand how to use it.
+- Most parts of the API were not aligned with Mesa, which made it difficult to use Mesa's features or to integrate Soil modules with Mesa code, especially for newcomers.
+- Many parts of the API were tightly coupled, which made it difficult to find bugs, test the system and add new features.
+
+The 0.30 rewrite should provide a middle ground between Soil's opinionated approach and Mesa's flexibility.
+The new Soil is less configuration-centric.
+It aims to provide more modular and convenient functions, most of which can be used in vanilla Mesa.
+
+How are agents assigned to nodes in the network
+###############################################
+
+The constructor of the `NetworkAgent` class has two arguments: `node_id` and `topology`.
+If `topology` is not provided, it will default to `self.model.topology`.
+This assignment might err if the model does not have a `topology` attribute, but most Soil environments derive from `NetworkEnvironment`, so they include a topology by default.
+If `node_id` is not provided, a random node will be selected from the topology, until a node with no agent is found.
+Then, the `node_id` of that node is assigned to the agent.
+If no node with no agent is found, a new node is automatically added to the topology.
+
+
+Can Soil environments include more than one network / topology?
+###############################################################
+
+Yes, but each network has to be included manually.
+Somewhere between 0.20 and 0.30 we included the ability to include multiple networks, but it was deemed too complex and was removed.

docs/requirements.txt

@@ -0,0 +1,2 @@
+ipython>=7.31.1
+nbsphinx==0.9.1

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

docs/usage.rst

@@ -1,99 +0,0 @@
-Usage
------
-
-First of all, you need to install the package. See :doc:`installation` for installation instructions.
-
-Simulation Settings
-===================
-
-Once installed, before running a simulation, you need to configure it.
-
-* In the Settings JSON file you will find the configuration of the network.
-
-  .. code:: python
-
-    {
-        "network_type": 1,
-        "number_of_nodes": 1000,
-        "max_time": 50,
-        "num_trials": 1,
-        "timeout": 2
-    }
-
-* In the Settings JSON file, you will also find the configuration of the models.
-
-Network Types
-=============
-
-There are three types of network implemented, but you could add more.
-
-.. code:: python
-
-    if settings.network_type == 0:
-        G = nx.complete_graph(settings.number_of_nodes)
-    if settings.network_type == 1:
-        G = nx.barabasi_albert_graph(settings.number_of_nodes, 10)
-    if settings.network_type == 2:
-        G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
-    # More types of networks can be added here
-
-Models Settings
-===============
-
-After having configured the simulation, the next step is setting up the variables of the models.
-For this, you will need to modify the Settings JSON file again.
-
-.. code:: json
-
-    {
-        "agent": ["SISaModel","ControlModelM2"],
-
-        "neutral_discontent_spon_prob": 0.04,
-        "neutral_discontent_infected_prob": 0.04,
-        "neutral_content_spon_prob": 0.18,
-        "neutral_content_infected_prob": 0.02,
-
-        "discontent_neutral": 0.13,
-        "discontent_content": 0.07,
-        "variance_d_c": 0.02,
-
-        "content_discontent": 0.009,
-        "variance_c_d": 0.003,
-        "content_neutral": 0.088,
-
-        "standard_variance": 0.055,
-
-
-        "prob_neutral_making_denier": 0.035,
-
-        "prob_infect": 0.075,
-
-        "prob_cured_healing_infected": 0.035,
-        "prob_cured_vaccinate_neutral": 0.035,
-
-        "prob_vaccinated_healing_infected": 0.035,
-        "prob_vaccinated_vaccinate_neutral": 0.035,
-        "prob_generate_anti_rumor": 0.035
-    }
-
-In this file you will define the different models you are going to simulate. You can simulate as many models
-as you want. Each model will be simulated separately.
-
-After setting up the models, you have to initialize the parameters of each one. You will find the parameters needed
-in the documentation of each model.
-
-Parameter validation will fail if a required parameter without a default has not been provided.
-
-Running the Simulation
-======================
-
-After setting all the configuration, you will be able to run the simulation. All you need to do is execute:
-
-.. code:: bash
-
-    python3 soil.py
-
-The simulation will return a dynamic graph .gexf file which could be visualized with
-`Gephi <https://gephi.org/users/download/>`__.
-
-It will also return one .png picture for each model simulated.

examples/custom_generator/generator_sim.py

@@ -0,0 +1,39 @@
+from networkx import Graph
+import random
+import networkx as nx
+from soil import Simulation, Environment, CounterModel, parameters
+
+
+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
+
+
+class GeneratorEnv(Environment):
+    """Using a custom generator for the network"""
+
+    generator: parameters.function = staticmethod(mygenerator)
+
+    def init(self):
+        self.create_network(generator=self.generator, n=10, n_edges=5)
+        self.add_agents(CounterModel)
+
+
+sim = Simulation(model=GeneratorEnv, max_steps=10, interval=1)
+
+if __name__ == '__main__':
+    sim.run(dump=False)

examples/custom_timeouts/custom_timeouts_sim.py

@@ -0,0 +1,41 @@
+from soil.agents import FSM, state, default_state
+from soil.time import Delta
+
+
+class Fibonacci(FSM):
+    """Agent that only executes in t_steps that are Fibonacci numbers"""
+    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, Delta(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, Delta(1 + self.now % 2)
+
+
+from soil import Environment, Simulation
+from networkx import complete_graph
+
+
+class TimeoutsEnv(Environment):
+    def init(self):
+        self.create_network(generator=complete_graph, n=2)
+        self.add_agent(agent_class=Fibonacci, node_id=0)
+        self.add_agent(agent_class=Odds, node_id=1)
+
+
+sim = Simulation(model=TimeoutsEnv, max_steps=10, interval=1)
+
+if __name__ == "__main__":
+    sim.run(dump=False)

examples/events_and_messages/README.md

@@ -0,0 +1,9 @@
+This example can be run like with command-line options, like this:
+
+```bash
+python cars.py --level DEBUG  -e summary --csv
+#or
+soil cars.py -e summary
+```
+
+This will set the `CSV` (save the agent and model data to a CSV) and `summary` (print the a summary of the data to stdout) exporters, and set the log level to DEBUG.

examples/events_and_messages/cars_sim.py

@@ -0,0 +1,231 @@
+"""
+This is an example of a simplified city, where there are Passengers and Drivers that can take those passengers
+from their location to their desired location.
+
+An example scenario could play like the following:
+
+- Drivers start in the `wandering` state, where they wander around the city until they have been assigned a journey
+- Passenger(1) tells every driver that it wants to request a Journey.
+- Each driver receives the request.
+  If Driver(2) is interested in providing the Journey, it asks Passenger(1) to confirm that it accepts Driver(2)'s request
+- When Passenger(1) accepts the request, two things happen:
+    - Passenger(1) changes its state to `driving_home`
+    - Driver(2) starts moving towards the origin of the Journey
+- Once Driver(2) reaches the origin, it starts moving itself and Passenger(1) to the destination of the Journey
+- When Driver(2) reaches the destination (carrying Passenger(1) along):
+    - Driver(2) starts wondering again
+    - Passenger(1) dies, and is removed from the simulation
+- If there are no more passengers available in the simulation, Drivers die
+"""
+from __future__ import annotations
+from typing import Optional
+from soil import *
+from soil import events
+from mesa.space import MultiGrid
+
+
+# More complex scenarios may use more than one type of message  between objects.
+# A common pattern is to use `enum.Enum` to represent state changes in a request.
+@dataclass
+class Journey:
+    """
+    This represents a request for a journey. Passengers and drivers exchange this object.
+
+    A journey may have a driver assigned or not. If the driver has not been assigned, this
+    object is considered a "request for a journey".
+    """
+
+    origin: (int, int)
+    destination: (int, int)
+    tip: float
+
+    passenger: Passenger
+    driver: Optional[Driver] = None
+
+
+class City(EventedEnvironment):
+    """
+    An environment with a grid where drivers and passengers will be placed.
+
+    The number of drivers and riders is configurable through its parameters:
+
+    :param str n_cars: The total number of drivers to add
+    :param str n_passengers: The number of passengers in the simulation
+    :param list agents: Specific agents to use in the simulation. It overrides the `n_passengers`
+    and `n_cars` params.
+    :param int height: Height of the internal grid
+    :param int width: Width of the internal grid
+    """
+    n_cars = 1
+    n_passengers = 10
+    height = 100
+    width = 100
+
+    def init(self):
+        self.grid = MultiGrid(width=self.width, height=self.height, torus=False)
+        if not self.agents:
+            self.add_agents(Driver, k=self.n_cars)
+            self.add_agents(Passenger, k=self.n_passengers)
+
+        for agent in self.agents:
+            self.grid.place_agent(agent, (0, 0))
+            self.grid.move_to_empty(agent)
+        
+        self.total_earnings = 0
+        self.add_model_reporter("total_earnings")
+
+    @report
+    @property
+    def number_passengers(self):
+        return self.count_agents(agent_class=Passenger)
+
+
+class Driver(Evented, FSM):
+    pos = None
+    journey = None
+    earnings = 0
+
+    def on_receive(self, msg, sender):
+        """This is not a state. It will run (and block) every time check_messages is invoked"""
+        if self.journey is None and isinstance(msg, Journey) and msg.driver is None:
+            msg.driver = self
+            self.journey = msg
+
+    def check_passengers(self):
+        """If there are no more passengers, stop forever"""
+        c = self.count_agents(agent_class=Passenger)
+        self.debug(f"Passengers left {c}")
+        if not c:
+            self.die("No more passengers")
+
+    @default_state
+    @state
+    def wandering(self):
+        """Move around the city until a journey is accepted"""
+        target = None
+        self.check_passengers()
+        self.journey = None
+        while self.journey is None:  # No potential journeys detected (see on_receive)
+            if target is None or not self.move_towards(target):
+                target = self.random.choice(
+                    self.model.grid.get_neighborhood(self.pos, moore=False)
+                )
+
+            self.check_passengers()
+            # This will call on_receive behind the scenes, and the agent's status will be updated
+            self.check_messages()
+            yield Delta(30)  # Wait at least 30 seconds before checking again
+
+        try:
+            # Re-send the journey to the passenger, to confirm that we have been selected
+            self.journey = yield self.journey.passenger.ask(self.journey, timeout=60)
+        except events.TimedOut:
+            # No journey has been accepted. Try again
+            self.journey = None
+            return
+
+        return self.driving
+
+    @state
+    def driving(self):
+        """The journey has been accepted. Pick them up and take them to their destination"""
+        self.info(f"Driving towards Passenger {self.journey.passenger.unique_id}")
+        while self.move_towards(self.journey.origin):
+            yield
+        self.info(f"Driving {self.journey.passenger.unique_id} from {self.journey.origin} to {self.journey.destination}")
+        while self.move_towards(self.journey.destination, with_passenger=True):
+            yield
+        self.info("Arrived at destination")
+        self.earnings += self.journey.tip
+        self.model.total_earnings += self.journey.tip
+        self.check_passengers()
+        return self.wandering
+
+    def move_towards(self, target, with_passenger=False):
+        """Move one cell at a time towards a target"""
+        self.debug(f"Moving { self.pos } -> { target }")
+        if target[0] == self.pos[0] and target[1] == self.pos[1]:
+            return False
+
+        next_pos = [self.pos[0], self.pos[1]]
+        for idx in [0, 1]:
+            if self.pos[idx] < target[idx]:
+                next_pos[idx] += 1
+                break
+            if self.pos[idx] > target[idx]:
+                next_pos[idx] -= 1
+                break
+        self.model.grid.move_agent(self, tuple(next_pos))
+        if with_passenger:
+            self.journey.passenger.pos = (
+                self.pos
+            )  # This could be communicated through messages
+        return True
+
+
+class Passenger(Evented, FSM):
+    pos = None
+
+    def on_receive(self, msg, sender):
+        """This is not a state. It will be run synchronously every time `check_messages` is run"""
+
+        if isinstance(msg, Journey):
+            self.journey = msg
+            return msg
+
+    @default_state
+    @state
+    def asking(self):
+        destination = (
+            self.random.randint(0, self.model.grid.height-1),
+            self.random.randint(0, self.model.grid.width-1),
+        )
+        self.journey = None
+        journey = Journey(
+            origin=self.pos,
+            destination=destination,
+            tip=self.random.randint(10, 100),
+            passenger=self,
+        )
+
+        timeout = 60
+        expiration = self.now + timeout
+        self.info(f"Asking for journey at: { self.pos }")
+        self.model.broadcast(journey, ttl=timeout, sender=self, agent_class=Driver)
+        while not self.journey:
+            self.debug(f"Waiting for responses at: { self.pos }")
+            try:
+                # This will call check_messages behind the scenes, and the agent's status will be updated
+                # If you want to avoid that, you can call it with: check=False
+                yield self.received(expiration=expiration)
+            except events.TimedOut:
+                self.info(f"Still no response. Waiting at: { self.pos }")
+                self.model.broadcast(
+                    journey, ttl=timeout, sender=self, agent_class=Driver
+                )
+                expiration = self.now + timeout
+        self.info(f"Got a response! Waiting for driver")
+        return self.driving_home
+
+    @state
+    def driving_home(self):
+        while (
+            self.pos[0] != self.journey.destination[0]
+            or self.pos[1] != self.journey.destination[1]
+        ):
+            try:
+                yield self.received(timeout=60)
+            except events.TimedOut:
+                pass
+
+        self.die("Got home safe!")
+
+
+simulation = Simulation(name="RideHailing",
+                        model=City,
+                        seed="carsSeed",
+                        max_time=1000,
+                        parameters=dict(n_passengers=2))
+
+if __name__ == "__main__":
+    easy(simulation)

examples/mesa/mesa_sim.py

@@ -0,0 +1,7 @@
+from soil import Simulation
+from social_wealth import MoneyEnv, graph_generator
+
+sim = Simulation(name="mesa_sim", dump=False, max_steps=10, interval=2, model=MoneyEnv, parameters=dict(generator=graph_generator, N=10, width=50, height=50))
+
+if __name__ == "__main__":
+    sim.run()

examples/mesa/server.py

@@ -0,0 +1,111 @@
+from mesa.visualization.ModularVisualization import ModularServer
+from mesa.visualization.UserParam import Slider, Choice
+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"
+)
+
+parameters = {
+    "N": Slider(
+        "N",
+        5,
+        1,
+        10,
+        1,
+        description="Choose how many agents to include in the model",
+    ),
+    "height": Slider(
+        "height",
+        5,
+        5,
+        10,
+        1,
+        description="Grid height",
+    ),
+    "width": Slider(
+        "width",
+        5,
+        5,
+        10,
+        1,
+        description="Grid width",
+    ),
+    "agent_class": Choice(
+        "Agent class",
+        value="MoneyAgent",
+        choices=["MoneyAgent", "SocialMoneyAgent"],
+    ),
+    "generator": graph_generator,
+}
+
+
+canvas_element = CanvasGrid(
+    gridPortrayal, parameters["width"].value, parameters["height"].value, 500, 500
+)
+
+
+server = ModularServer(
+    MoneyEnv, [grid, chart, canvas_element], "Money Model", parameters
+)
+server.port = 8521
+
+if __name__ == '__main__':
+    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, **kwargs):
+        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(MoneyAgent, NetworkAgent):
+    wealth = 1
+
+    def give_money(self):
+        cellmates = set(self.model.grid.get_cell_list_contents([self.pos]))
+        friends = set(self.get_neighbors())
+        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_sim.py

@@ -0,0 +1,134 @@
+from soil.agents import FSM, NetworkAgent, state, default_state, prob
+from soil.parameters import *
+import logging
+
+from soil.environment import Environment
+
+
+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.
+    """
+
+    has_been_infected: bool = False
+    has_tv: bool = False
+
+    @default_state
+    @state
+    def neutral(self):
+        if self.has_tv:
+            if self.prob(self.get("prob_tv_spread")):
+                return self.infected
+        if self.has_been_infected:
+            return self.infected
+
+    @state
+    def infected(self):
+        for neighbor in self.get_neighbors(state_id=self.neutral.id):
+            if self.prob(self.get("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.has_been_infected = True
+
+
+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_neighbors(state_id=self.infected.id)
+        total = self.count_neighbors()
+        prob_infect = self.get("prob_neighbor_spread") * infected / total
+        self.debug("prob_infect", prob_infect)
+        if self.prob(prob_infect):
+            self.has_been_infected = True
+
+
+class WiseViewer(HerdViewer):
+    """
+    A viewer that can change its mind.
+    """
+
+    @state
+    def cured(self):
+        prob_cure = self.get("prob_neighbor_cure")
+        for neighbor in self.get_neighbors(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.has_been_cured = True
+
+    @state
+    def infected(self):
+        if self.has_been_cured:
+            return self.cured
+        cured = max(self.count_neighbors(self.cured.id), 1.0)
+        infected = max(self.count_neighbors(self.infected.id), 1.0)
+        prob_cure = self.get("prob_neighbor_cure") * (cured / infected)
+        if self.prob(prob_cure):
+            return self.cured
+
+
+class NewsSpread(Environment):
+    ratio_dumb: probability = 1,
+    ratio_herd: probability = 0,
+    ratio_wise: probability = 0,
+    prob_tv_spread: probability = 0.1,
+    prob_neighbor_spread: probability = 0.1,
+    prob_neighbor_cure: probability = 0.05,
+
+    def init(self):
+        self.populate_network([DumbViewer, HerdViewer, WiseViewer],
+                              [self.ratio_dumb, self.ratio_herd, self.ratio_wise])
+
+
+from itertools import product
+from soil import Simulation
+
+
+# We want to investigate the effect of different agent distributions on the spread of news.
+# To do that, we will run different simulations, with a varying ratio of DumbViewers, HerdViewers, and WiseViewers
+# Because the effect of these agents might also depend on the network structure, we will run our simulations on two different networks:
+# one with a small-world structure and one with a connected structure.
+
+counter = 0
+for [r1, r2] in product([0, 0.5, 1.0], repeat=2):
+    for (generator, netparams) in {
+        "barabasi_albert_graph": {"m": 5},
+        "erdos_renyi_graph": {"p": 0.1},
+    }.items():
+        print(r1, r2, 1-r1-r2, generator)
+        # Create new simulation
+        netparams["n"] = 500
+        Simulation(
+            name='newspread_sim',
+            model=NewsSpread,
+            parameters=dict(
+                ratio_dumb=r1,
+                ratio_herd=r2,
+                ratio_wise=1-r1-r2,
+                network_generator=generator,
+                network_params=netparams,
+                prob_neighbor_spread=0,
+            ),
+            iterations=5,
+            max_steps=300,
+            dump=False,
+        ).run()
+        counter += 1
+        # Run all the necessary instances
+ 
+print(f"A total of {counter} simulations were run.")

examples/programmatic/.gitignore

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

examples/programmatic/programmatic_sim.py

@@ -0,0 +1,53 @@
+"""
+Example of a fully programmatic simulation, without definition files.
+"""
+from soil import Simulation, Environment, agents
+from networkx import Graph
+import logging
+
+
+def mygenerator():
+    # Add only a node
+    G = Graph()
+    G.add_node(1)
+    G.add_node(2)
+    return G
+
+
+class MyAgent(agents.NetworkAgent, agents.FSM):
+    times_run = 0
+    @agents.default_state
+    @agents.state
+    def neutral(self):
+        self.debug("I am running")
+        if self.prob(0.2):
+            self.times_run += 1
+            self.info("This runs 2/10 times on average")
+
+
+class ProgrammaticEnv(Environment):
+
+    def init(self):
+        self.create_network(generator=mygenerator)
+        assert len(self.G)
+        self.populate_network(agent_class=MyAgent)
+        self.add_agent_reporter('times_run')
+
+
+simulation = Simulation(
+    name="Programmatic",
+    model=ProgrammaticEnv,
+    seed='Program',
+    iterations=1,
+    max_time=100,
+    dump=False,
+)
+
+if __name__ == "__main__":
+    # 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 = simulation.run()
+
+    for agent in envs[0].agents:
+        print(agent.times_run)

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_sim.py

@@ -0,0 +1,195 @@
+from soil.agents import FSM, NetworkAgent, state, default_state
+from soil import Environment, Simulation, parameters
+from itertools import islice
+import networkx as nx
+import logging
+
+
+class CityPubs(Environment):
+    """Environment with Pubs"""
+
+    level = logging.INFO
+    number_of_pubs: parameters.Integer = 3
+    ratio_extroverted: parameters.probability = 0.1
+    pub_capacity: parameters.Integer = 10
+    
+    def init(self):
+        self.pubs = {}
+        for i in range(self.number_of_pubs):
+            newpub = {
+                "name": "The awesome pub #{}".format(i),
+                "open": True,
+                "capacity": self.pub_capacity,
+                "occupancy": 0,
+            }
+            self.pubs[newpub["name"]] = newpub
+        self.add_agent(agent_class=Police)
+        self.populate_network([Patron.w(openness=0.1), Patron.w(openness=1)],
+                              [self.ratio_extroverted, 1-self.ratio_extroverted])
+        assert all(["agent" in node and isinstance(node["agent"], Patron) for (_, node) in self.G.nodes(data=True)])
+
+    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_neighbors())
+        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.unique_id))
+                befriended = True
+            else:
+                self.debug("{} does not want to be friends".format(friend.unique_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.unique_id))
+            drunk.kick_out()
+        else:
+            self.info("No trash to take out. Too bad.")
+
+
+sim = Simulation(
+    model=CityPubs,
+    name="pubcrawl",
+    iterations=3,
+    max_steps=10,
+    dump=False,
+    parameters=dict(
+        network_generator=nx.empty_graph,
+        network_params={"n": 30},
+        model=CityPubs,
+        altercations=0,
+        number_of_pubs=3,
+    )
+)
+
+
+if __name__ == "__main__":
+    sim.run(parallel=False)

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/rabbit_improved_sim.py

@@ -0,0 +1,153 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment, Simulation
+from soil.time import Delta
+from enum import Enum
+from collections import Counter
+import logging
+import math
+
+from rabbits_basic_sim import RabbitEnv
+
+
+class RabbitsImprovedEnv(RabbitEnv):
+    def init(self):
+        """Initialize the environment with the new versions of the agents"""
+        a1 = self.add_node(Male)
+        a2 = self.add_node(Female)
+        a1.add_edge(a2)
+        self.add_agent(RandomAccident)
+
+
+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))
+
+
+sim = Simulation(model=RabbitsImprovedEnv, max_time=100, seed="MySeed", iterations=1)
+
+if __name__ == "__main__":
+    sim.run()

examples/rabbits/rabbits_basic_sim.py

@@ -0,0 +1,161 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment, Simulation, report, parameters as params
+from collections import Counter
+import logging
+import math
+
+
+class RabbitEnv(Environment):
+    prob_death: params.probability = 1e-100
+
+    def init(self):
+        a1 = self.add_node(Male)
+        a2 = self.add_node(Female)
+        a1.add_edge(a2)
+        self.add_agent(RandomAccident)
+
+    @report
+    @property
+    def num_rabbits(self):
+        return self.count_agents(agent_class=Rabbit)
+
+    @report
+    @property
+    def num_males(self):
+        return self.count_agents(agent_class=Male)
+
+    @report
+    @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.prob_death * math.floor(
+            math.log10(max(1, rabbits_alive))
+        )
+        self.debug("Killing some rabbits with prob={}!".format(prob_death))
+        for i in self.get_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))
+
+
+
+sim = Simulation(model=RabbitEnv, max_time=100, seed="MySeed", iterations=1)
+
+if __name__ == "__main__":
+    sim.run()

examples/random_delays/random_delays_sim.py

@@ -0,0 +1,47 @@
+"""
+Example of setting a 
+Example of a fully programmatic simulation, without definition files.
+"""
+from soil import Simulation, agents, Environment
+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))
+
+
+class RandomEnv(Environment):
+
+    def init(self):
+        self.add_agent(agent_class=MyAgent)
+
+
+s = Simulation(
+    name="Programmatic",
+    model=RandomEnv,
+    iterations=1,
+    max_time=100,
+    dump=False,
+)
+
+
+envs = s.run()

examples/terrorism/TerroristNetworkModel_sim.py

@@ -0,0 +1,341 @@
+import networkx as nx
+from soil.agents import Geo, NetworkAgent, FSM, custom, state, default_state
+from soil import Environment, Simulation
+from soil.parameters import *
+from soil.utils import int_seed
+
+
+class TerroristEnvironment(Environment):
+    n: Integer = 100
+    radius: Float = 0.2
+
+    information_spread_intensity: probability = 0.7
+    terrorist_additional_influence: probability = 0.03
+    terrorist_additional_influence: probability = 0.035
+    max_vulnerability: probability = 0.7
+    prob_interaction: probability = 0.5
+
+    # TrainingAreaModel and HavenModel
+    training_influence: probability = 0.20
+    haven_influence: probability = 0.20
+
+    # TerroristNetworkModel
+    vision_range: Float = 0.30
+    sphere_influence: Integer = 2
+    weight_social_distance: Float = 0.035
+    weight_link_distance: Float = 0.035
+
+    ratio_civil: probability = 0.8
+    ratio_leader: probability = 0.1
+    ratio_training: probability = 0.05
+    ratio_haven: probability = 0.05
+
+    def init(self):
+        self.create_network(generator=self.generator, n=self.n, radius=self.radius)
+        self.populate_network([
+            TerroristNetworkModel.w(state_id='civilian'),
+            TerroristNetworkModel.w(state_id='leader'),
+            TrainingAreaModel,
+            HavenModel
+        ], [self.ratio_civil, self.ratio_leader, self.ratio_training, self.ratio_haven])
+
+    def generator(self, *args, **kwargs):
+        return nx.random_geometric_graph(*args, **kwargs, seed=int_seed(self._seed))
+
+class TerroristSpreadModel(FSM, Geo):
+    """
+    Settings:
+        information_spread_intensity
+
+        terrorist_additional_influence
+
+        min_vulnerability (optional else zero)
+
+        max_vulnerability
+    """
+
+    information_spread_intensity = 0.1
+    terrorist_additional_influence = 0.1
+    min_vulnerability = 0
+    max_vulnerability = 1
+
+    def init(self):
+        if self.state_id == self.civilian.id:  # Civilian
+            self.mean_belief = self.model.random.uniform(0.00, 0.5)
+        elif self.state_id == self.terrorist.id:  # Terrorist
+            self.mean_belief = self.random.uniform(0.8, 1.00)
+        elif self.state_id == self.leader.id:  # Leader
+            self.mean_belief = 1.00
+        else:
+            raise Exception("Invalid state id: {}".format(self["id"]))
+
+        self.vulnerability = self.random.uniform(
+            self.get("min_vulnerability", 0), self.get("max_vulnerability", 1)
+        )
+
+    @default_state
+    @state
+    def civilian(self):
+        neighbours = list(self.get_neighbors(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.model.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_neighbors(
+            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, agent=None, **kwargs):
+        """Get a list of nodes in the ego network of *node* of radius *steps*"""
+        node = agent.node_id if agent else self.node_id
+        G = self.subgraph(**kwargs)
+        return nx.ego_graph(G, node, center=center, radius=steps).nodes()
+
+    def degree(self, agent, force=False):
+        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[agent.node_id]
+
+    def betweenness(self, agent, force=False):
+        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[agent.node_id]
+
+
+class TrainingAreaModel(FSM, Geo):
+    """
+    Settings:
+        training_influence
+
+        min_vulnerability
+
+    Requires TerroristSpreadModel.
+    """
+
+    training_influence = 0.1
+    min_vulnerability = 0
+
+    def init(self):
+        self.mean_believe = 1
+        self.vulnerability = 0
+
+    @default_state
+    @state
+    def terrorist(self):
+        for neighbour in self.get_neighbors(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.
+    """
+
+    min_vulnerability = 0
+    haven_influence = 0.1
+    max_vulnerability = 0.5
+
+    def init(self):
+        self.mean_believe = 0
+        self.vulnerability = 0
+
+    def get_occupants(self, **kwargs):
+        return self.get_neighbors(agent_class=TerroristSpreadModel,
+                                  **kwargs)
+
+    @default_state
+    @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
+    """
+
+    sphere_influence: float = 1
+    vision_range: float = 1
+    weight_social_distance: float = 0.5
+    weight_link_distance: float = 0.2
+
+    @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_neighbors(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.unique_id
+                for agent in self.get_neighbors(agent_class=TerroristNetworkModel)
+            )
+            search = (close_ups | step_neighbours) - neighbours
+            for agent in self.get_agents(search):
+                social_distance = 1 / self.shortest_path_length(agent.unique_id)
+                spatial_proximity = 1 - self.get_distance(agent.unique_id)
+                prob_new_interaction = (
+                    self.weight_social_distance * social_distance
+                    + self.weight_link_distance * spatial_proximity
+                )
+                if (
+                    agent.state_id == "civilian"
+                    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.unique_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.unique_id, target)
+        except nx.NetworkXNoPath:
+            return float("inf")
+
+
+sim = Simulation(
+    model=TerroristEnvironment,
+    iterations=1,
+    name="TerroristNetworkModel_sim",
+    max_steps=150,
+    seed="default2",
+    skip_test=False,
+    dump=False,
+)
+
+# TODO: integrate visualization
+# 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'

examples/torvalds.edgelist

@@ -0,0 +1,2 @@
+balkian Torvalds {}
+anonymous Torvalds {}

examples/torvalds_sim.py

@@ -0,0 +1,25 @@
+from soil import Environment, Simulation, CounterModel, report
+
+
+# Get directory path for current file
+import os, sys, inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+
+class TorvaldsEnv(Environment):
+
+  def init(self):
+    self.create_network(path=os.path.join(currentdir, 'torvalds.edgelist'))
+    self.populate_network(CounterModel, skill_level='beginner')
+    self.agent(node_id="Torvalds").skill_level = 'God'
+    self.agent(node_id="balkian").skill_level = 'developer'
+    self.add_agent_reporter("times")
+
+  @report
+  def god_developers(self):
+    return self.count_agents(skill_level='God')
+  
+
+sim = Simulation(name='torvalds_example',
+                 max_steps=10,
+                 interval=2,
+                 model=TorvaldsEnv)

models/BaseBehaviour/BaseBehaviour.py

@@ -1,38 +0,0 @@
-import settings
-from nxsim import BaseNetworkAgent
-from .. import networkStatus
-
-
-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.network_params["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

models/BaseBehaviour/__init__.py

@@ -1 +0,0 @@
-from .BaseBehaviour import BaseBehaviour

models/TerroristModel/TerroristModel.py

@@ -1,367 +0,0 @@
-import random
-import numpy as np
-from ..BaseBehaviour import *
-import settings
-import networkx as nx
-
-
-
-POPULATION = 0
-LEADERS = 1
-HAVEN = 2
-TRAININGENV = 3
-
-NON_RADICAL = 0
-NEUTRAL = 1
-RADICAL = 2
-
-POPNON =0
-POPNE=1
-POPRAD=2
-
-HAVNON=3
-HAVNE=4
-HAVRAD=5
-
-LEADER=6
-
-TRAINING = 7
-
-
-class TerroristModel(BaseBehaviour):
-    num_agents = 0
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.population = settings.network_params["number_of_nodes"] * settings.environment_params['initial_population']
-        self.havens = settings.network_params["number_of_nodes"] * settings.environment_params['initial_havens']
-        self.training_enviroments = settings.network_params["number_of_nodes"] * settings.environment_params['initial_training_enviroments']
-
-        self.initial_radicalism = settings.environment_params['initial_radicalism']
-        self.information_spread_intensity = settings.environment_params['information_spread_intensity']
-        self.influence = settings.environment_params['influence']
-        self.relative_inequality = settings.environment_params['relative_inequality']
-        self.additional_influence = settings.environment_params['additional_influence']
-
-        if TerroristModel.num_agents < self.population:
-            self.state['type'] = POPULATION
-            TerroristModel.num_agents = TerroristModel.num_agents + 1
-            random1 = random.random()
-            if random1 < 0.7:
-                self.state['id'] = NON_RADICAL
-                self.state['fstatus'] = POPNON
-            elif random1 >= 0.7 and random1 < 0.9:
-                self.state['id'] = NEUTRAL
-                self.state['fstatus'] = POPNE
-            elif random1 >= 0.9:
-                self.state['id'] = RADICAL
-                self.state['fstatus'] = POPRAD
-
-        elif TerroristModel.num_agents < self.havens + self.population:
-            self.state['type'] = HAVEN
-            TerroristModel.num_agents = TerroristModel.num_agents + 1
-            random2 = random.random()
-            random1 = random2 + self.initial_radicalism
-            if random1 < 1.2:
-                self.state['id'] = NON_RADICAL
-                self.state['fstatus'] = HAVNON
-            elif random1 >= 1.2 and random1 < 1.6:
-                self.state['id'] = NEUTRAL
-                self.state['fstatus'] = HAVNE
-            elif random1 >= 1.6:
-                self.state['id'] = RADICAL
-                self.state['fstatus'] = HAVRAD
-
-        elif TerroristModel.num_agents < self.training_enviroments + self.havens + self.population:
-            self.state['type'] = TRAININGENV
-            self.state['fstatus'] = TRAINING
-            TerroristModel.num_agents = TerroristModel.num_agents + 1
-
-    def step(self, now):
-        if self.state['type'] == POPULATION:
-            self.population_and_leader_conduct()
-        if self.state['type'] == LEADERS:
-            self.population_and_leader_conduct()
-        if self.state['type'] == HAVEN:
-            self.haven_conduct()
-        if self.state['type'] == TRAININGENV:
-            self.training_enviroment_conduct()
-
-        self.attrs['status'] = self.state['id']
-        self.attrs['type'] = self.state['type']
-        self.attrs['radicalism'] = self.state['rad']
-        self.attrs['fstatus'] = self.state['fstatus']
-        super().step(now)
-
-    def population_and_leader_conduct(self):
-        if self.state['id'] == NON_RADICAL:
-            if self.state['rad'] == 0.000:
-                self.state['rad'] = self.set_radicalism()
-            self.non_radical_behaviour()
-        if self.state['id'] == NEUTRAL:
-            if self.state['rad'] == 0.000:
-                self.state['rad'] = self.set_radicalism()
-            while self.state['id'] == RADICAL:
-                self.radical_behaviour()
-                break
-            self.neutral_behaviour()
-        if self.state['id'] == RADICAL:
-            if self.state['rad'] == 0.000:
-                self.state['rad'] = self.set_radicalism()
-            self.radical_behaviour()
-
-    def haven_conduct(self):
-        non_radical_neighbors = self.get_neighboring_agents(state_id=NON_RADICAL)
-        neutral_neighbors = self.get_neighboring_agents(state_id=NEUTRAL)
-        radical_neighbors = self.get_neighboring_agents(state_id=RADICAL)
-
-        neighbors_of_non_radical = len(neutral_neighbors) + len(radical_neighbors)
-        neighbors_of_neutral = len(non_radical_neighbors) + len(radical_neighbors)
-        neighbors_of_radical = len(non_radical_neighbors) + len(neutral_neighbors)
-        threshold = 8
-        if (len(non_radical_neighbors) > neighbors_of_non_radical) and len(non_radical_neighbors) >= threshold:
-            self.state['id'] = NON_RADICAL
-        elif (len(neutral_neighbors) > neighbors_of_neutral) and len(neutral_neighbors) >= threshold:
-            self.state['id'] = NEUTRAL
-        elif (len(radical_neighbors) > neighbors_of_radical) and len(radical_neighbors) >= threshold:
-            self.state['id'] = RADICAL
-
-        if self.state['id'] == NEUTRAL:
-            for neighbor in non_radical_neighbors:
-                neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
-                if neighbor.state['rad'] >= 0.3 and neighbor.state['rad'] <= 0.59:
-                    neighbor.state['id'] = NEUTRAL
-                    if neighbor.state['type'] == POPULATION:
-                        neighbor.state['fstatus'] = POPNE
-                    elif neighbor.state['type'] == HAVEN:
-                        neighbor.state['fstatus'] = HAVNE
-                elif neighbor.state['rad'] > 0.59:
-                    neighbor.state['rad'] = 0.59
-                    neighbor.state['id'] = NEUTRAL
-                    if neighbor.state['type'] == POPULATION:
-                        neighbor.state['fstatus'] = POPNE
-                    elif neighbor.state['type'] == HAVEN:
-                        neighbor.state['fstatus'] = HAVNE
-
-        if self.state['id'] == RADICAL:
-
-            for neighbor in non_radical_neighbors:
-                neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
-                if neighbor.state['rad'] >= 0.3 and neighbor.state['rad'] <= 0.59:
-                    neighbor.state['id'] = NEUTRAL
-                    if neighbor.state['type'] == POPULATION:
-                        neighbor.state['fstatus'] = POPNE
-                    elif neighbor.state['type'] == HAVEN:
-                        neighbor.state['fstatus'] = HAVNE
-                elif neighbor.state['rad'] > 0.59:
-                    neighbor.state['rad'] = 0.59
-                    neighbor.state['id'] = NEUTRAL
-                    if neighbor.state['type'] == POPULATION:
-                        neighbor.state['fstatus'] = POPNE
-                    elif neighbor.state['type'] == HAVEN:
-                        neighbor.state['fstatus'] = HAVNE
-
-            for neighbor in neutral_neighbors:
-                neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
-                if neighbor.state['rad'] >= 0.6:
-                    neighbor.state['id'] = RADICAL
-                    if neighbor.state['type'] != HAVEN and neighbor.state['type']!=TRAININGENV:
-                        if neighbor.state['rad'] >= 0.62:
-                            if create_leader(neighbor):
-                                neighbor.state['type'] = LEADERS
-                                neighbor.state['fstatus'] = LEADER
-                            # elif neighbor.state['type'] == LEADERS:
-                            #     neighbor.state['type'] = POPULATION
-                            #     neighbor.state['fstatus'] = POPRAD
-                            elif neighbor.state['type'] == POPULATION:
-                                neighbor.state['fstatus'] = POPRAD
-                    elif neighbor.state['type'] == HAVEN:
-                        neighbor.state['fstatus'] = HAVRAD
-
-    def training_enviroment_conduct(self):
-        self.state['id'] = RADICAL
-        self.state['rad'] = 1
-        neighbors = self.get_neighboring_agents()
-        for neighbor in neighbors:
-            if neighbor.state['id'] == NON_RADICAL:
-                neighbor.state['rad'] = neighbor.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
-                if neighbor.state['rad'] >= 0.3 and self.state['rad'] <= 0.59:
-                    neighbor.state['id'] = NEUTRAL
-                    if neighbor.state['type'] == POPULATION:
-                        neighbor.state['fstatus'] = POPNE
-                    elif neighbor.state['type'] == HAVEN:
-                        neighbor.state['fstatus'] = HAVNE
-                elif neighbor.state['rad'] > 0.59:
-                    neighbor.state['rad'] = 0.59
-                    neighbor.state['id'] = NEUTRAL
-                    if neighbor.state['type'] == POPULATION:
-                        neighbor.state['fstatus'] = POPNE
-                    elif neighbor.state['type'] == HAVEN:
-                        neighbor.state['fstatus'] = HAVNE
-
-
-            neighbor.state['rad'] = neighbor.state['rad'] + (neighbor.influence + neighbor.additional_influence) * neighbor.information_spread_intensity
-            if neighbor.state['rad'] >= 0.3 and neighbor.state['rad'] <= 0.59:
-                neighbor.state['id'] = NEUTRAL
-                if neighbor.state['type'] == POPULATION:
-                    neighbor.state['fstatus'] = POPNE
-                elif neighbor.state['type'] == HAVEN:
-                    neighbor.state['fstatus'] = HAVNE
-            elif neighbor.state['rad'] >= 0.6:
-                neighbor.state['id'] = RADICAL
-                if neighbor.state['type'] != HAVEN and neighbor.state['type'] != TRAININGENV:
-                    if neighbor.state['rad'] >= 0.62:
-                        if create_leader(neighbor):
-                            neighbor.state['type'] = LEADERS
-                            neighbor.state['fstatus'] = LEADER
-                        # elif neighbor.state['type'] == LEADERS:
-                        #     neighbor.state['type'] = POPULATION
-                        #     neighbor.state['fstatus'] = POPRAD
-                        elif neighbor.state['type'] == POPULATION:
-                            neighbor.state['fstatus'] = POPRAD
-                elif neighbor.state['type'] == HAVEN:
-                    neighbor.state['fstatus'] = HAVRAD
-
-    def non_radical_behaviour(self):
-        neighbors = self.get_neighboring_agents()
-
-        for neighbor in neighbors:
-            if neighbor.state['type'] == POPULATION:
-                if neighbor.state['id'] == NEUTRAL or neighbor.state['id'] == RADICAL:
-                    self.state['rad'] = self.state['rad'] + self.influence * self.information_spread_intensity
-                    if self.state['rad'] >= 0.3 and self.state['rad'] <= 0.59:
-                        self.state['id'] = NEUTRAL
-
-                        if self.state['type']==POPULATION:
-                            self.state['fstatus'] = POPNE
-                        elif self.state['type'] == HAVEN:
-                            self.state['fstatus'] = HAVNE
-                    elif self.state['rad'] > 0.59:
-                        self.state['rad'] = 0.59
-                        self.state['id'] = NEUTRAL
-                        if self.state['type']==POPULATION:
-                            self.state['fstatus'] = POPNE
-                        elif self.state['type'] == HAVEN:
-                            self.state['fstatus'] = HAVNE
-
-            elif neighbor.state['type'] == LEADERS:
-
-                if neighbor.state['id'] == NEUTRAL or neighbor.state['id'] == RADICAL:
-                    self.state['rad'] = self.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
-                    if self.state['rad'] >= 0.3 and self.state['rad'] <= 0.59:
-                        self.state['id'] = NEUTRAL
-                        if self.state['type']==POPULATION:
-                            self.state['fstatus'] = POPNE
-                        elif self.state['type'] == HAVEN:
-                            self.state['fstatus'] = HAVNE
-                    elif self.state['rad'] > 0.59:
-                        self.state['rad'] = 0.59
-                        self.state['id'] = NEUTRAL
-                        if self.state['type']==POPULATION:
-                            self.state['fstatus'] = POPNE
-                        elif self.state['type'] == HAVEN:
-                            self.state['fstatus'] = HAVNE
-
-
-    def neutral_behaviour(self):
-        neighbors = self.get_neighboring_agents()
-        for neighbor in neighbors:
-            if neighbor.state['type'] == POPULATION:
-                if neighbor.state['id'] == RADICAL:
-                    self.state['rad'] = self.state['rad'] + self.influence * self.information_spread_intensity
-                    if self.state['rad'] >= 0.6:
-                        self.state['id'] = RADICAL
-                        if self.state['type'] != HAVEN:
-                            if self.state['rad'] >= 0.62:
-                                if create_leader(self):
-                                    self.state['type'] = LEADERS
-
-                                    self.state['fstatus'] = LEADER
-                                # elif self.state['type'] == LEADERS:
-                                #     self.state['type'] = POPULATION
-                                #     self.state['fstatus'] = POPRAD
-                                elif neighbor.state['type'] == POPULATION:
-                                    self.state['fstatus'] = POPRAD
-                        elif self.state['type'] == HAVEN:
-                            self.state['fstatus'] = HAVRAD
-
-
-            elif neighbor.state['type'] == LEADERS:
-                if neighbor.state['id'] == RADICAL:
-                    self.state['rad'] = self.state['rad'] + (self.influence + self.additional_influence) * self.information_spread_intensity
-                    if self.state['rad'] >= 0.6:
-                        self.state['id'] = RADICAL
-                        if self.state['type'] != HAVEN:
-                            if self.state['rad'] >= 0.62:
-                                if create_leader(self):
-                                    self.state['type'] = LEADERS
-                                    self.state['fstatus'] = LEADER
-                                # elif self.state['type'] == LEADERS:
-                                #     self.state['type'] = POPULATION
-                                #     self.state['fstatus'] = POPRAD
-                                elif neighbor.state['type'] == POPULATION:
-                                    self.state['fstatus'] = POPRAD
-                        elif self.state['type'] == HAVEN:
-                            self.state['fstatus'] = HAVRAD
-
-
-
-
-
-    def radical_behaviour(self):
-        neighbors = self.get_neighboring_agents(state_id=RADICAL)
-
-        for neighbor in neighbors:
-            if self.state['rad']< neighbor.state['rad'] and self.state['type']== LEADERS and neighbor.state['type']==LEADERS:
-                self.state['type'] = POPULATION
-                self.state['fstatus'] = POPRAD
-
-
-    def set_radicalism(self):
-        if self.state['id'] == NON_RADICAL:
-            radicalism = random.uniform(0.0, 0.29) * self.relative_inequality
-            return radicalism
-        elif self.state['id'] == NEUTRAL:
-            radicalism = 0.3 + random.uniform(0.3, 0.59) * self.relative_inequality
-            if radicalism >= 0.6:
-                self.state['id'] = RADICAL
-            return radicalism
-        elif self.state['id'] == RADICAL:
-            radicalism = 0.6 + random.uniform(0.6, 1.0) * self.relative_inequality
-            return radicalism
-
-def get_partition(agent):
-    return settings.partition_param[agent.id]
-
-def get_centrality(agent):
-    return settings.centrality_param[agent.id]
-def get_centrality_given_id(id):
-    return settings.centrality_param[id]
-
-def get_leader(partition):
-    if not bool(settings.leaders) or partition not in settings.leaders.keys():
-        return None
-    return settings.leaders[partition]
-
-def set_leader(partition, agent):
-    settings.leaders[partition] = agent.id
-
-def create_leader(agent):
-    my_partition = get_partition(agent)
-    old_leader = get_leader(my_partition)
-
-    if old_leader == None:
-        set_leader(my_partition, agent)
-        return True
-    else:
-        my_centrality = get_centrality(agent)
-        old_leader_centrality = get_centrality_given_id(old_leader)
-        if my_centrality > old_leader_centrality:
-            set_leader(my_partition, agent)
-            return True
-        return False
-
-
-

models/TerroristModel/__init__.py

@@ -1 +0,0 @@
-from .TerroristModel import TerroristModel

models/__init__.py

@@ -1,3 +0,0 @@
-from .models import *
-from .BaseBehaviour import *
-from .TerroristModel import *

models/models.py

@@ -1,7 +0,0 @@
-import settings
-
-networkStatus = {}  # Dict that will contain the status of every agent in the network
-
-# Initialize agent states. Let's assume everyone is normal and all types are population.
-init_states = [{'id': 0, 'type': 0, 'rad': 0, 'fstatus':0,  } for _ in range(settings.network_params["number_of_nodes"])]
-

requirements.txt

@@ -1,5 +1,13 @@
-nxsim
-simpy
-networkx
+networkx>=2.5
 numpy
-matplotlib
+matplotlib
+pyyaml>=5.1
+pandas>=1
+SALib>=1.3
+Jinja2
+Mesa>=1.2
+pydantic>=1.9
+sqlalchemy>=1.4
+typing-extensions>=4.4
+annotated-types>=0.4
+tqdm>=4.64

settings.json

@@ -1,23 +0,0 @@
-[
-  {
-    "network_type": 0,
-    "number_of_nodes": 80,
-    "max_time": 50,
-    "num_trials": 1,
-    "timeout": 2
-  },
-
-  {
-    "agent": ["TerroristModel"],
-
-    "initial_population": 0.85,
-    "initial_havens": 0.1,
-    "initial_training_enviroments": 0.05,
-
-    "initial_radicalism": 0.12,
-    "relative_inequality": 0.33,
-    "information_spread_intensity": 0.1,
-    "influence": 0.4,
-    "additional_influence": 0.1
-  }
-]

settings.py

@@ -1,13 +0,0 @@
-# General configuration
-import json
-
-with open('settings.json', 'r') as f:
-    settings = json.load(f)
-
-network_params = settings[0]
-environment_params = settings[1]
-
-centrality_param = {}
-partition_param={}
-leaders={}
-

setup.cfg

@@ -0,0 +1,8 @@
+[metadata]
+long_description = file: README.md
+long_description_content_type = text/markdown
+
+[aliases]
+test=pytest
+[tool:pytest]
+addopts = --verbose

setup.py

@@ -0,0 +1,63 @@
+import os
+from setuptools import setup
+
+
+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={
+    'geo': ['scipy>=1.3'],
+    'web': ['tornado'],
+    'ipython': ['ipython==8.12', 'nbformat==5.8'],
+}
+extras_require['all'] = [dep for package in extras_require.values() for dep in package]
+
+
+setup(
+    name='soil',
+    packages=['soil'],  # this must be the same as the name above
+    version=__version__,
+    description=('An Agent-Based Social Simulator for Social Networks'),
+    author='J. Fernando Sanchez',
+    author_email='jf.sanchez@upm.es',
+    url='https://github.com/gsi-upm/soil',  # use the URL to the github repo
+    download_url='https://github.com/gsi-upm/soil/archive/{}.tar.gz'.format(
+        __version__),
+    keywords=['agent', 'social', 'simulator'],
+    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 :: Only",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        ],
+    install_requires=install_reqs,
+    extras_require=extras_require,
+    tests_require=test_reqs,
+    setup_requires=['pytest-runner', ],
+    pytest_plugins = ['pytest_profiling'],
+    include_package_data=True,
+    python_requires=">=3.8",
+    entry_points={
+        'console_scripts':
+        ['soil = soil.__main__:main',
+        'soil-web = soil.web.__init__:main']
+    })

soil.py

@@ -1,215 +0,0 @@
-from models import *
-from nxsim import NetworkSimulation
-# import numpy
-from matplotlib import pyplot as plt
-import networkx as nx
-import settings
-import models
-import math
-import json
-import operator
-import community
-
-
-
-POPULATION = 0
-LEADERS = 1
-HAVEN = 2
-TRAINING = 3
-
-NON_RADICAL = 0
-NEUTRAL = 1
-RADICAL = 2
-#################
-# Visualization #
-#################
-
-def visualization(graph_name):
-
-    for x in range(0, settings.network_params["number_of_nodes"]):
-        attributes = {}
-        spells = []
-        for attribute in models.networkStatus["agent_%s" % x]:
-            if attribute == 'visible':
-                lastvisible = False
-                laststep = 0
-                for t_step in models.networkStatus["agent_%s" % x][attribute]:
-                    nowvisible = models.networkStatus["agent_%s" % x][attribute][t_step]
-                    if nowvisible and not lastvisible:
-                        laststep = t_step
-                    if not nowvisible and lastvisible:
-                        spells.append((laststep, t_step))
-
-                    lastvisible = nowvisible
-                if lastvisible:
-                    spells.append((laststep, None))
-            else:
-                emotionStatusAux = []
-                for t_step in models.networkStatus["agent_%s" % x][attribute]:
-                    prec = 2
-                    output = math.floor(models.networkStatus["agent_%s" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec)  # 2 decimals
-                    emotionStatusAux.append((output, t_step, t_step + settings.network_params["timeout"]))
-                attributes[attribute] = emotionStatusAux
-        if spells:
-            G.add_node(x, attributes, spells=spells)
-        else:
-            G.add_node(x, attributes)
-
-    print("Done!")
-
-
-    with open('data.txt', 'w') as outfile:
-        json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
-
-    for node in range(settings.network_params["number_of_nodes"]):
-        G.node[node]['x'] = G.node[node]['pos'][0]
-        G.node[node]['y'] = G.node[node]['pos'][1]
-        G.node[node]['viz'] = {"position": {"x": G.node[node]['pos'][0], "y": G.node[node]['pos'][1], "z": 0.0}}
-        del (G.node[node]['pos'])
-
-    nx.write_gexf(G, graph_name+".gexf", version="1.2draft")
-
-###########
-# Results #
-###########
-
-def results(model_name):
-    x_values = []
-    neutral_values = []
-    non_radical_values = []
-    radical_values = []
-
-    attribute_plot = 'status'
-    for time in range(0, settings.network_params["max_time"]):
-        value_neutral = 0
-        value_non_radical = 0
-        value_radical = 0
-        real_time = time * settings.network_params["timeout"]
-        activity = False
-        for x in range(0, settings.network_params["number_of_nodes"]):
-            if attribute_plot in models.networkStatus["agent_%s" % x]:
-                if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
-                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == NON_RADICAL:  
-                        value_non_radical += 1
-                        activity = True
-                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == NEUTRAL:  
-                        value_neutral += 1
-                        activity = True
-                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == RADICAL:  
-                        value_radical += 1
-                        activity = True
-
-
-        if activity:
-            x_values.append(real_time)
-            neutral_values.append(value_neutral)
-            non_radical_values.append(value_non_radical)
-            radical_values.append(value_radical)
-            activity = False
-
-    fig1 = plt.figure()
-    ax1 = fig1.add_subplot(111)
-
-    non_radical_line = ax1.plot(x_values, non_radical_values, label='Non radical')
-    neutral_line = ax1.plot(x_values, neutral_values, label='Neutral')
-    radical_line = ax1.plot(x_values, radical_values, label='Radical')
-    ax1.legend()
-    fig1.savefig(model_name+'.png')
-    plt.show()
-
-###########
-# Results #
-###########
-
-def resultadosTipo(model_name):
-    x_values = []
-    population_values = []
-    leaders_values = []
-    havens_values = []
-    training_enviroments_values = []
-
-    attribute_plot = 'type'
-    for time in range(0, settings.network_params["max_time"]):
-        value_population = 0
-        value_leaders = 0
-        value_havens = 0
-        value_training_enviroments = 0
-        real_time = time * settings.network_params["timeout"]
-        activity = False
-        for x in range(0, settings.network_params["number_of_nodes"]):
-            if attribute_plot in models.networkStatus["agent_%s" % x]:
-                if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
-                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == POPULATION:
-                        value_population += 1
-                        activity = True
-                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == LEADERS:
-                        value_leaders += 1
-                        activity = True
-                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == HAVEN:
-                        value_havens += 1
-                        activity = True
-                    if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == TRAINING:
-                        value_training_enviroments += 1
-                        activity = True
-        if activity:
-            x_values.append(real_time)
-            population_values.append(value_population)
-            leaders_values.append(value_leaders)
-            havens_values.append(value_havens)
-            training_enviroments_values.append(value_training_enviroments)
-            activity = False
-
-    fig2 = plt.figure()
-    ax2 = fig2.add_subplot(111)
-
-    population_line = ax2.plot(x_values, population_values, label='Population')
-    leaders_line = ax2.plot(x_values, leaders_values, label='Leader')
-    havens_line = ax2.plot(x_values, havens_values, label='Havens')
-    training_enviroments_line = ax2.plot(x_values, training_enviroments_values, label='Training Enviroments')
-    ax2.legend()
-    fig2.savefig(model_name+'_type'+'.png')
-    plt.show()
-
-####################
-# Network creation #
-####################
-
-#  nx.degree_centrality(G);
-
-if settings.network_params["network_type"] == 0:
-    G = nx.random_geometric_graph(settings.network_params["number_of_nodes"], 0.2)
-
-    settings.partition_param = community.best_partition(G)
-    settings.centrality_param = nx.betweenness_centrality(G).copy()
-
-
-    # print(settings.centrality_param)
-    # print(settings.partition_param)
-# More types of networks can be added here
-
-##############
-# Simulation #
-##############
-
-agents = settings.environment_params['agent']
-
-print("Using Agent(s): {agents}".format(agents=agents))
-
-if len(agents) > 1:
-    for agent in agents:
-        sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params["max_time"],
-                                num_trials=settings.network_params["num_trials"], logging_interval=1.0, **settings.environment_params)
-        sim.run_simulation()
-        print(str(agent))
-        results(str(agent))
-        resultadosTipo(str(agent))
-        visualization(str(agent))
-else:
-    agent = agents[0]
-    sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.network_params["max_time"],
-                            num_trials=settings.network_params["num_trials"], logging_interval=1.0, **settings.environment_params)
-    sim.run_simulation()
-    results(str(agent))
-    resultadosTipo(str(agent))
-
-    visualization(str(agent))

soil.py~

@@ -1,394 +0,0 @@
-from nxsim import NetworkSimulation
-from nxsim import BaseNetworkAgent
-from nxsim import BaseLoggingAgent
-from random import randint
-from matplotlib import pyplot as plt
-import random
-import numpy as np
-import networkx as nx
-import settings
-
-
-settings.init()
-
-if settings.network_type == 0:
-    G = nx.complete_graph(settings.number_of_nodes)
-if settings.network_type == 1:
-    G = nx.barabasi_albert_graph(settings.number_of_nodes,3)
-if settings.network_type == 2:
-    G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
-
-
-myList=[]
-networkStatus=[]
-for x in range(0, settings.number_of_nodes):
-    networkStatus.append({'id':x})
-
-
-
-# # Just like subclassing a process in SimPy
-# class MyAgent(BaseNetworkAgent):
-#     def __init__(self, environment=None, agent_id=0, state=()):  # Make sure to have these three keyword arguments
-#         super().__init__(environment=environment, agent_id=agent_id, state=state)
-#         # Add your own attributes here
-
-#     def run(self):
-#         # Add your behaviors here
-
-
-
-
-class SentimentCorrelationModel(BaseNetworkAgent):
-    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):
-            self.time_awareness.append(0)     #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
-        networkStatus[self.id][self.env.now]=0
-
-
-    def run(self):
-        while True:
-            if self.env.now > 10:
-                G.add_node(205)
-                G.add_edge(205,0)
-            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)
-
-            # #Outside effects. Asignamos un estado aleatorio
-            # if random.random() < settings.outside_effects_prob:
-            #     if self.state['id'] == 0:
-            #         self.state['id'] = random.randint(1,4)
-            #         myList.append(self.id)
-            #         networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
-            #         self.time_awareness = self.env.now #Para saber cuando se han contagiado
-            #         yield self.env.timeout(settings.timeout)
-            #     else:
-            #         yield self.env.timeout(settings.timeout)
-
-
-            # #Imitation effects-Joy
-
-            # if random.random() < (settings.joy_prob*(num_neighbors_joyful)/10):
-            #     myList.append(self.id)
-            #     self.state['id'] = 2
-            #     networkStatus[self.id][self.env.now]=2
-            #     yield self.env.timeout(settings.timeout)
-
-
-            # #Imitation effects-Sadness
-
-            # if random.random() < (settings.sadness_prob*(num_neighbors_sad)/10):
-            #     myList.append(self.id)
-            #     self.state['id'] = 3
-            #     networkStatus[self.id][self.env.now]=3
-            #     yield self.env.timeout(settings.timeout)
-
-
-            # #Imitation effects-Disgust
-
-            # if random.random() < (settings.disgust_prob*(num_neighbors_disgusted)/10):
-            #     myList.append(self.id)
-            #     self.state['id'] = 4
-            #     networkStatus[self.id][self.env.now]=4
-            #     yield self.env.timeout(settings.timeout)
-
-            # #Imitation effects-Anger
-
-            # if random.random() < (settings.anger_prob*(num_neighbors_angry)/10):
-            #     myList.append(self.id)
-            #     self.state['id'] = 1
-            #     networkStatus[self.id][self.env.now]=1
-            #     yield self.env.timeout(settings.timeout)
-
-            # yield self.env.timeout(settings.timeout)
-
-###########################################
-
-
-            anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
-            print("anger_prob " + str(anger_prob))
-            joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
-            print("joy_prob " + str(joy_prob))
-            sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
-            print("sadness_prob "+ str(sadness_prob))
-            disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
-            print("disgust_prob " + str(disgust_prob))
-            outside_effects_prob= settings.outside_effects_prob
-            print("outside_effects_prob " + str(outside_effects_prob))
-
-
-            num = random.random()
-
-
-            if(num<outside_effects_prob):
-                self.state['id'] = random.randint(1,4)
-                myList.append(self.id)
-                networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
-                self.time_awareness[self.state['id']-1] = self.env.now
-                yield self.env.timeout(settings.timeout)
-
-
-            if(num<anger_prob):
-
-                myList.append(self.id)
-                self.state['id'] = 1
-                networkStatus[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):
-
-                myList.append(self.id)
-                self.state['id'] = 2
-                networkStatus[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):
-
-                myList.append(self.id)
-                self.state['id'] = 3
-                networkStatus[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):
-
-                myList.append(self.id)
-                self.state['id'] = 4
-                networkStatus[self.id][self.env.now]=4
-                self.time_awareness[self.state['id']-1] = self.env.now
-
-            yield self.env.timeout(settings.timeout)
-
-
-            # anger_propagation = settings.anger_prob*num_neighbors_angry/10
-            # joy_propagation = anger_propagation + (settings.joy_prob*num_neighbors_joyful/10)
-            # sadness_propagation = joy_propagation + (settings.sadness_prob*num_neighbors_sad/10)
-            # disgust_propagation = sadness_propagation + (settings.disgust_prob*num_neighbors_disgusted/10)
-            # outside_effects_propagation = disgust_propagation + settings.outside_effects_prob
-
-            # if (num<anger_propagation):
-            #     if(self.state['id'] !=0):
-            #         myList.append(self.id)
-            #         self.state['id'] = 1
-            #         networkStatus[self.id][self.env.now]=1
-            #         yield self.env.timeout(settings.timeout)
-            # if (num<joy_propagation):
-            #     if(self.state['id'] !=0):
-            #         myList.append(self.id)
-            #         self.state['id'] = 2
-            #         networkStatus[self.id][self.env.now]=2
-            #         yield self.env.timeout(settings.timeout)
-            # if(num<sadness_propagation):
-            #     if(self.state['id'] !=0):
-            #         myList.append(self.id)
-            #         self.state['id'] = 3
-            #         networkStatus[self.id][self.env.now]=3
-            #         yield self.env.timeout(settings.timeout)
-            # # if(num<disgust_propagation):
-            # #     if(self.state['id'] !=0):
-            # #         myList.append(self.id)
-            # #         self.state['id'] = 4
-            # #         networkStatus[self.id][self.env.now]=4
-            # #         yield self.env.timeout(settings.timeout)
-            # if(num <outside_effects_propagation):
-            #     if self.state['id'] == 0:
-            #         self.state['id'] = random.randint(1,4)
-            #         myList.append(self.id)
-            #         networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
-            #         self.time_awareness = self.env.now #Para saber cuando se han contagiado
-            #         yield self.env.timeout(settings.timeout)
-            #     else:
-            #         yield self.env.timeout(settings.timeout)
-            # else:
-            #     yield self.env.timeout(settings.timeout)
-
-
-
-
-
-class BassModel(BaseNetworkAgent):
-    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
-        networkStatus[self.id][self.env.now]=0
-
-    def run(self):
-        while True:
-
-
-            #Outside effects
-            if random.random() < settings.innovation_prob:
-                if self.state['id'] == 0:
-                    self.state['id'] = 1
-                    myList.append(self.id)
-                    networkStatus[self.id][self.env.now]=1
-                    yield self.env.timeout(settings.timeout)
-                else:
-                    yield self.env.timeout(settings.timeout)
-
-            #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):
-                    myList.append(self.id)
-                    self.state['id'] = 1
-                    networkStatus[self.id][self.env.now]=1
-                    yield self.env.timeout(settings.timeout)
-                else:
-                    yield self.env.timeout(settings.timeout)
-
-class IndependentCascadeModel(BaseNetworkAgent):
-    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
-        networkStatus[self.id][self.env.now]=0
-
-    def run(self):
-        while True:
-            aware_neighbors_1_time_step=[]
-            #Outside effects
-            if random.random() < settings.innovation_prob:
-                if self.state['id'] == 0:
-                    self.state['id'] = 1
-                    myList.append(self.id)
-                    networkStatus[self.id][self.env.now]=1
-                    self.time_awareness = self.env.now #Para saber cuando se han contagiado
-                    yield self.env.timeout(settings.timeout)
-                else:
-                    yield self.env.timeout(settings.timeout)
-
-            #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):
-                    myList.append(self.id)
-                    self.state['id'] = 1
-                    networkStatus[self.id][self.env.now]=1
-                    yield self.env.timeout(settings.timeout)
-                else:
-                    yield self.env.timeout(settings.timeout)
-
-
-class ZombieOutbreak(BaseNetworkAgent):
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.bite_prob = settings.bite_prob
-        networkStatus[self.id][self.env.now]=0
-
-
-    def run(self):
-        while True:
-            if random.random() < settings.heal_prob:
-                if self.state['id'] == 1:
-                    self.zombify()
-                    yield self.env.timeout(settings.timeout)
-                else:
-                    yield self.env.timeout(settings.timeout)
-            else:
-                if self.state['id'] == 1:
-                    print("Soy el zombie " + str(self.id) + " y me voy a curar porque el num aleatorio ha sido " + str(num))
-                    networkStatus[self.id][self.env.now]=0
-                    if self.id in myList:
-                        myList.remove(self.id)
-                    self.state['id'] = 0
-                    yield self.env.timeout(settings.timeout)
-                else:
-                    yield self.env.timeout(settings.timeout)
-
-
-    def zombify(self):
-        normal_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in normal_neighbors:
-            if random.random() < self.bite_prob:
-                print("Soy el zombie " + str(self.id) + " y voy a contagiar a " + str(neighbor.id))
-                neighbor.state['id'] = 1 # zombie
-                myList.append(neighbor.id)
-                networkStatus[self.id][self.env.now]=1
-                networkStatus[neighbor.id][self.env.now]=1
-                print(self.env.now, "Soy el zombie: "+ str(self.id), "Mi vecino es: "+ str(neighbor.id), sep='\t')
-                break
-
-
-# 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
-
-# Seed a zombie
-#init_states[5] = {'id': 1}
-#init_states[3] = {'id': 1}
-
-sim = NetworkSimulation(topology=G, states=init_states, agent_type=SentimentCorrelationModel,
-                        max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
-
-
-sim.run_simulation()
-
-myList = sorted(myList, key=int)
-#print("Los zombies son: " + str(myList))
-
-trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
-zombie_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
-
-#for x in range(len(myList)):
-#    G.node[myList[x]]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
-
-#G.node[1]['viz'] = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}
-
-#lista = nx.nodes(G)
-#print('Nodos: ' + str(lista))
-for x in range(0, settings.number_of_nodes):
-    networkStatusAux=[]
-    for tiempo in networkStatus[x]:
-        if tiempo != 'id':
-	        networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
-    G.add_node(x, zombie= networkStatusAux)
-#print(networkStatus)
-
-
-nx.write_gexf(G,"test.gexf", version="1.2draft")
-plt.plot(zombie_census)
-plt.draw()  # pyplot draw()
-plt.savefig("zombie.png")
-#print(networkStatus)
-#nx.draw(G)
-#plt.show()
-#plt.savefig("path.png")

soil/VERSION

@@ -0,0 +1 @@
+1.0.0rc2

soil/__init__.py

@@ -0,0 +1,287 @@
+from __future__ import annotations
+
+import importlib
+from importlib.resources import path
+import sys
+import os
+import logging
+import traceback
+from contextlib import contextmanager
+
+from .version import __version__
+
+try:
+    basestring
+except NameError:
+    basestring = str
+
+from pathlib import Path
+from .analysis import *
+from .agents import *
+from . import agents
+from .simulation import *
+from .environment import Environment, EventedEnvironment
+from .datacollection import SoilCollector
+from . import serialization
+from .utils import logger
+from .time import *
+from .decorators import *
+
+
+def main(
+    cfg="simulation.yml",
+    exporters=None,
+    num_processes=1,
+    output="soil_output",
+    *,
+    debug=False,
+    pdb=False,
+    **kwargs,
+):
+
+    sim = None
+    if isinstance(cfg, Simulation):
+        sim = cfg
+
+    import argparse
+    from . import simulation
+
+    logger.info("Running SOIL version: {}".format(__version__))
+
+    parser = argparse.ArgumentParser(description="Run a SOIL simulation")
+    parser.add_argument(
+        "file",
+        type=str,
+        nargs="?",
+        default=cfg if sim is None else "",
+        help="Configuration file for the simulation (e.g., YAML or JSON)",
+    )
+    parser.add_argument(
+        "--version", action="store_true", help="Show version info and exit"
+    )
+    parser.add_argument(
+        "--module",
+        "-m",
+        type=str,
+        help="file containing the code of any custom agents.",
+    )
+    parser.add_argument(
+        "--dry-run",
+        "--dry",
+        action="store_true",
+        help="Do not run the simulation",
+    )
+    parser.add_argument(
+        "--no-dump",
+        action="store_true",
+        help="Do not store the results of the simulation to disk, show in terminal instead.",
+    )
+    parser.add_argument(
+        "--pdb", action="store_true", help="Use a pdb console in case of exception."
+    )
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="Run a customized version of a pdb console to debug a simulation.",
+    )
+    parser.add_argument(
+        "--graph",
+        "-g",
+        action="store_true",
+        help="Dump each iteration's network topology as a GEXF graph. Defaults to false.",
+    )
+    parser.add_argument(
+        "--csv",
+        action="store_true",
+        help="Dump all data collected in CSV format. Defaults to false.",
+    )
+    parser.add_argument("--level", type=str, help="Logging level")
+    parser.add_argument(
+        "--output",
+        "-o",
+        type=str,
+        default=output or "soil_output",
+        help="folder to write results to. It defaults to the current directory.",
+    )
+    parser.add_argument(
+        "--num-processes",
+        default=num_processes,
+        help="Number of processes to use for parallel execution. Defaults to 1.",
+    )
+
+    parser.add_argument(
+        "-e",
+        "--exporter",
+        action="append",
+        default=[],
+        help="Export environment and/or simulations using this exporter",
+    )
+    parser.add_argument(
+        "--max_time",
+        default="-1",
+        help="Set maximum time for the simulation to run. ",
+    )
+
+    parser.add_argument(
+        "--max_steps",
+        default="-1",
+        help="Set maximum number of steps for the simulation to run.",
+    )
+
+    parser.add_argument(
+        "--iterations",
+        default="",
+        help="Set maximum number of iterations (runs) for the simulation.",
+    )
+
+    parser.add_argument(
+        "--seed",
+        default=None,
+        help="Manually set a seed for the simulation.",
+    )
+
+    parser.add_argument(
+        "--only-convert",
+        "--convert",
+        action="store_true",
+        help="Do not run the simulation, only convert the configuration file(s) and output them.",
+    )
+
+    parser.add_argument(
+        "--set",
+        metavar="KEY=VALUE",
+        action="append",
+        help="Set a number of parameters that will be passed to the simulation."
+        "(do not put spaces before or after the = sign). "
+        "If a value contains spaces, you should define "
+        "it with double quotes: "
+        'foo="this is a sentence". Note that '
+        "values are always treated as strings.",
+    )
+
+    args = parser.parse_args()
+    level = getattr(logging, (args.level or "INFO").upper())
+    logger.setLevel(level)
+
+    if args.version:
+        return
+
+    exporters = exporters or [
+        "default",
+    ]
+    for exp in args.exporter:
+        if exp not in exporters:
+            exporters.append(exp)
+    if args.csv:
+        exporters.append("csv")
+    if args.graph:
+        exporters.append("gexf")
+
+    if os.getcwd() not in sys.path:
+        sys.path.append(os.getcwd())
+    if args.module:
+        importlib.import_module(args.module)
+    if output is None:
+        output = args.output
+
+    debug = debug or args.debug
+
+    if args.pdb or debug:
+        args.synchronous = True
+        os.environ["SOIL_POSTMORTEM"] = "true"
+
+    res = []
+    try:
+        exp_params = {}
+        opts = dict(
+                    dry_run=args.dry_run,
+                    dump=not args.no_dump,
+                    debug=debug,
+                    exporters=exporters,
+                    num_processes=args.num_processes,
+                    level=level,
+                    outdir=output,
+                    exporter_params=exp_params,
+                    **kwargs)
+        if args.seed is not None:
+            opts["seed"] = args.seed
+        if args.iterations:
+            opts["iterations"] =int(args.iterations)
+
+        if sim:
+            logger.info("Loading simulation instance")
+            for (k, v) in opts.items():
+                setattr(sim, k, v)
+            sims = [sim]
+        else:
+            logger.info("Loading config file: {}".format(args.file))
+            if not os.path.exists(args.file):
+                logger.error("Please, input a valid file")
+                return
+
+            assert opts["debug"] == debug
+            sims = list(
+                simulation.iter_from_file(
+                    args.file,
+                    **opts,
+                )
+            )
+
+        for sim in sims:
+            assert sim.debug == debug
+
+            if args.set:
+                for s in args.set:
+                    k, v = s.split("=", 1)[:2]
+                    v = eval(v)
+                    tail, *head = k.rsplit(".", 1)[::-1]
+                    target = sim.parameters
+                    if head:
+                        for part in head[0].split("."):
+                            try:
+                                target = getattr(target, part)
+                            except AttributeError:
+                                target = target[part]
+                    try:
+                        setattr(target, tail, v)
+                    except AttributeError:
+                        target[tail] = v
+
+            if args.only_convert:
+                print(sim.to_yaml())
+                continue
+            max_time = float(args.max_time) if args.max_time != "-1" else None
+            max_steps = float(args.max_steps) if args.max_steps != "-1" else None
+            res.append(sim.run(max_time=max_time, max_steps=max_steps))
+
+    except Exception as ex:
+        if args.pdb:
+            from .debugging import post_mortem
+
+            print(traceback.format_exc())
+            post_mortem()
+        else:
+            raise
+    if debug:
+        from .debugging import set_trace
+
+        os.environ["SOIL_DEBUG"] = "true"
+        set_trace()
+    return res
+
+
+@contextmanager
+def easy(cfg, pdb=False, debug=False, **kwargs):
+    try:
+        return main(cfg, debug=debug, pdb=pdb, **kwargs)[0]
+    except Exception as e:
+        if os.environ.get("SOIL_POSTMORTEM"):
+            from .debugging import post_mortem
+
+            print(traceback.format_exc())
+            post_mortem()
+        raise
+
+
+if __name__ == "__main__":
+    main()

soil/__main__.py

@@ -0,0 +1,9 @@
+from . import main as init_main
+
+
+def main():
+    init_main()
+
+
+if __name__ == "__main__":
+    init_main()

soil/agents/BassModel.py

@@ -0,0 +1,31 @@
+from . import FSM, state, default_state
+
+
+class BassModel(FSM):
+    """
+    Settings:
+        innovation_prob
+        imitation_prob
+    """
+
+    sentimentCorrelation = 0
+
+    def step(self):
+        self.behaviour()
+
+    @default_state
+    @state
+    def innovation(self):
+        if self.prob(self.innovation_prob):
+            self.sentimentCorrelation = 1
+            return self.aware
+        else:
+            aware_neighbors = self.get_neighbors(state_id=self.aware.id)
+            num_neighbors_aware = len(aware_neighbors)
+            if self.prob((self.imitation_prob * num_neighbors_aware)):
+                self.sentimentCorrelation = 1
+                return self.aware
+
+    @state
+    def aware(self):
+        self.die()

soil/agents/CounterModel.py

@@ -0,0 +1,46 @@
+from . import BaseAgent, NetworkAgent
+
+
+class Ticker(BaseAgent):
+    times = 0
+
+    def step(self):
+        self.times += 1
+
+class CounterModel(NetworkAgent):
+    """
+    Dummy behaviour. It counts the number of nodes in the network and neighbors
+    in each step and adds it to its state.
+    """
+
+    times = 0
+    neighbors = 0
+    total = 0
+
+    def step(self):
+        # Outside effects
+        total = len(list(self.model.schedule._agents))
+        neighbors = len(list(self.get_neighbors()))
+        self["times"] = self.get("times", 0) + 1
+        self["neighbors"] = neighbors
+        self["total"] = total
+
+
+class AggregatedCounter(NetworkAgent):
+    """
+    Dummy behaviour. It counts the number of nodes in the network and neighbors
+    in each step and adds it to its state.
+    """
+
+    times = 0
+    neighbors = 0
+    total = 0
+
+    def step(self):
+        # Outside effects
+        self["times"] += 1
+        neighbors = len(list(self.get_neighbors()))
+        self["neighbors"] += neighbors
+        total = len(list(self.model.schedule.agents))
+        self["total"] += total
+        self.debug("Running for step: {}. Total: {}".format(self.now, total))

soil/agents/Geo.py

@@ -0,0 +1,21 @@
+from scipy.spatial import cKDTree as KDTree
+import networkx as nx
+from . import NetworkAgent
+
+
+class Geo(NetworkAgent):
+    """In this type of network, nodes have a "pos" attribute."""
+
+    def geo_search(self, radius, center=False, **kwargs):
+        """Get a list of nodes whose coordinates are closer than *radius* to *node*."""
+        node = self.node_id
+
+        G = self.subgraph(**kwargs)
+
+        pos = nx.get_node_attributes(G, "pos")
+        if not pos:
+            return []
+        nodes, coords = list(zip(*pos.items()))
+        kdtree = KDTree(coords)  # Cannot provide generator.
+        indices = kdtree.query_ball_point(pos[node], radius)
+        return [nodes[i] for i in indices if center or (nodes[i] != node)]

soil/agents/IndependentCascadeModel.py

@@ -0,0 +1,30 @@
+from . import Agent, state, default_state
+
+
+class IndependentCascadeModel(Agent):
+    """
+    Settings:
+        innovation_prob
+
+        imitation_prob
+    """
+
+    time_awareness = 0
+    sentimentCorrelation = 0
+
+    # Outside effects
+    @default_state
+    @state
+    def outside(self):
+        if self.prob(self.model.innovation_prob):
+            self.sentimentCorrelation = 1
+            self.time_awareness = self.model.now # To know when they have been infected
+            return self.imitate
+
+    @state
+    def imitate(self):
+        aware_neighbors = self.get_neighbors(state_id=1, time_awareness=self.now-1)
+
+        if self.prob(self.model.imitation_prob * len(aware_neighbors)):
+            self.sentimentCorrelation = 1
+            return self.outside

soil/agents/SISaModel.py

@@ -0,0 +1,110 @@
+import numpy as np
+from hashlib import sha512
+from . import Agent, state, default_state
+
+
+class SISaModel(Agent):
+    """
+    Settings:
+        neutral_discontent_spon_prob
+
+        neutral_discontent_infected_prob
+
+        neutral_content_spon_prob
+
+        neutral_content_infected_prob
+
+        discontent_neutral
+
+        discontent_content
+
+        variance_d_c
+
+        content_discontent
+
+        variance_c_d
+
+        content_neutral
+
+        standard_variance
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        seed = self.model._seed
+        if isinstance(seed, (str, bytes, bytearray)):
+            if isinstance(seed, str):
+                seed = seed.encode()
+            seed = int.from_bytes(seed + sha512(seed).digest(), 'big')
+
+        random = np.random.default_rng(seed=seed)
+
+        self.neutral_discontent_spon_prob = random.normal(
+            self.model.neutral_discontent_spon_prob, self.model.standard_variance
+        )
+        self.neutral_discontent_infected_prob = random.normal(
+            self.model.neutral_discontent_infected_prob, self.model.standard_variance
+        )
+        self.neutral_content_spon_prob = random.normal(
+            self.model.neutral_content_spon_prob, self.model.standard_variance
+        )
+        self.neutral_content_infected_prob = random.normal(
+            self.model.neutral_content_infected_prob, self.model.standard_variance
+        )
+
+        self.discontent_neutral = random.normal(
+            self.model.discontent_neutral, self.model.standard_variance
+        )
+        self.discontent_content = random.normal(
+            self.model.discontent_content, self.model.variance_d_c
+        )
+
+        self.content_discontent = random.normal(
+            self.model.content_discontent, self.model.variance_c_d
+        )
+        self.content_neutral = random.normal(
+            self.model.discontent_neutral, self.model.standard_variance
+        )
+
+    @default_state
+    @state
+    def neutral(self):
+        # Spontaneous effects
+        if self.prob(self.neutral_discontent_spon_prob):
+            return self.discontent
+        if self.prob(self.neutral_content_spon_prob):
+            return self.content
+
+        # Infected
+        discontent_neighbors = self.count_neighbors(state_id=self.discontent)
+        if self.prob(discontent_neighbors * self.neutral_discontent_infected_prob):
+            return self.discontent
+        content_neighbors = self.count_neighbors(state_id=self.content.id)
+        if self.prob(content_neighbors * self.neutral_content_infected_prob):
+            return self.content
+        return self.neutral
+
+    @state
+    def discontent(self):
+        # Healing
+        if self.prob(self.discontent_neutral):
+            return self.neutral
+
+        # Superinfected
+        content_neighbors = self.count_neighbors(state_id=self.content.id)
+        if self.prob(content_neighbors * self.discontent_content):
+            return self.content
+        return self.discontent
+
+    @state
+    def content(self):
+        # Healing
+        if self.prob(self.content_neutral):
+            return self.neutral
+
+        # Superinfected
+        discontent_neighbors = self.count_neighbors(state_id=self.discontent.id)
+        if self.prob(discontent_neighbors * self.content_discontent):
+            self.discontent
+        return self.content

soil/agents/__init__.py

@@ -0,0 +1,672 @@
+from __future__ import annotations
+
+import logging
+from collections import OrderedDict, defaultdict
+from collections.abc import MutableMapping, Mapping, Set
+from abc import ABCMeta
+from copy import deepcopy, copy
+from functools import partial, wraps
+from itertools import islice, chain
+import inspect
+import types
+import textwrap
+import networkx as nx
+import warnings
+import sys
+
+from typing import Any
+
+from mesa import Agent as MesaAgent, Model
+from typing import Dict, List
+
+from .. import serialization, network, utils, time, config
+
+
+IGNORED_FIELDS = ("model", "logger")
+
+
+class MetaAgent(ABCMeta):
+    def __new__(mcls, name, bases, namespace):
+        defaults = {}
+
+        # Re-use defaults from inherited classes
+        for i in bases:
+            if isinstance(i, MetaAgent):
+                defaults.update(i._defaults)
+
+        new_nmspc = {
+            "_defaults": defaults,
+            "_last_return": None,
+            "_last_except": None,
+        }
+
+        for attr, func in namespace.items():
+            if attr == "step" and inspect.isgeneratorfunction(func):
+                orig_func = func
+                new_nmspc["_coroutine"] = None
+
+                @wraps(func)
+                def func(self):
+                    while True:
+                        if not self._coroutine:
+                            self._coroutine = orig_func(self)
+                        try:
+                            if self._last_except:
+                                return self._coroutine.throw(self._last_except)
+                            else:
+                                return self._coroutine.send(self._last_return)
+                        except StopIteration as ex:
+                            self._coroutine = None
+                            return ex.value
+                        finally:
+                            self._last_return = None
+                            self._last_except = None
+
+                func.id = name or func.__name__
+                func.is_default = False
+                new_nmspc[attr] = func
+            elif (
+                isinstance(func, types.FunctionType)
+                or isinstance(func, property)
+                or isinstance(func, classmethod)
+                or attr[0] == "_"
+            ):
+                new_nmspc[attr] = func
+            elif attr == "defaults":
+                defaults.update(func)
+            else:
+                defaults[attr] = copy(func)
+
+        return super().__new__(mcls, name, bases, new_nmspc)
+
+
+class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
+    """
+    A special type of Mesa Agent that:
+
+    * Can be used as a dictionary to access its state.
+    * Has logging built-in
+    * Can be given default arguments through a defaults class attribute,
+    which will be used on construction to initialize each agent's state
+
+    Any attribute that is not preceded by an underscore (`_`) will also be added to its state.
+    """
+
+    def __init__(self, unique_id, model, name=None, init=True, interval=None, **kwargs):
+        assert isinstance(unique_id, int)
+        super().__init__(unique_id=unique_id, model=model)
+
+        self.name = (
+            str(name) if name else "{}[{}]".format(type(self).__name__, self.unique_id)
+        )
+
+        self.alive = True
+
+        self.interval = interval or self.get("interval", 1)
+        logger = utils.logger.getChild(getattr(self.model, "id", self.model)).getChild(
+            self.name
+        )
+        self.logger = logging.LoggerAdapter(logger, {"agent_name": self.name})
+
+        if hasattr(self, "level"):
+            self.logger.setLevel(self.level)
+
+        for (k, v) in self._defaults.items():
+            if not hasattr(self, k) or getattr(self, k) is None:
+                setattr(self, k, deepcopy(v))
+
+        for (k, v) in kwargs.items():
+
+            setattr(self, k, v)
+        if init:
+            self.init()
+
+    def init(self):
+        pass
+
+    def __hash__(self):
+        return hash(self.unique_id)
+
+    def prob(self, probability):
+        return prob(probability, self.model.random)
+
+    @classmethod
+    def w(cls, **kwargs):
+        return custom(cls, **kwargs)
+
+    # TODO: refactor to clean up mesa compatibility
+    @property
+    def id(self):
+        msg = "This attribute is deprecated. Use `unique_id` instead"
+        warnings.warn(msg, DeprecationWarning)
+        print(msg, file=sys.stderr)
+        return self.unique_id
+
+    @classmethod
+    def from_dict(cls, model, attrs, warn_extra=True):
+        ignored = {}
+        args = {}
+        for k, v in attrs.items():
+            if k in inspect.signature(cls).parameters:
+                args[k] = v
+            else:
+                ignored[k] = v
+        if ignored and warn_extra:
+            utils.logger.info(
+                f"Ignoring the following arguments for agent class { agent_class.__name__ }: { ignored }"
+            )
+        return cls(model=model, **args)
+
+    def __getitem__(self, key):
+        try:
+            return getattr(self, key)
+        except AttributeError:
+            raise KeyError(f"key {key}  not found in agent")
+
+    def __delitem__(self, key):
+        return delattr(self, key)
+
+    def __contains__(self, key):
+        return hasattr(self, key)
+
+    def __setitem__(self, key, value):
+        setattr(self, key, value)
+
+    def __len__(self):
+        return sum(1 for n in self.keys())
+
+    def __iter__(self):
+        return self.items()
+
+    def keys(self):
+        return (k for k in self.__dict__ if k[0] != "_" and k not in IGNORED_FIELDS)
+
+    def items(self, keys=None, skip=None):
+        keys = keys if keys is not None else self.keys()
+        it = ((k, self.get(k, None)) for k in keys)
+        if skip:
+            return filter(lambda x: x[0] not in skip, it)
+        return it
+
+    def get(self, key, default=None):
+        if key in self:
+            return self[key]
+        elif key in self.model:
+            return self.model[key]
+        return default
+
+    @property
+    def now(self):
+        try:
+            return self.model.now
+        except AttributeError:
+            # No environment
+            return None
+
+    def die(self, msg=None):
+        if msg:
+            self.info("Agent dying:", msg)
+        self.debug(f"agent dying")
+        self.alive = False
+        try:
+            self.model.schedule.remove(self)
+        except KeyError:
+            pass
+        return time.NEVER
+
+    def step(self):
+        raise NotImplementedError("Agent must implement step method")
+    
+    def _check_alive(self):
+        if not self.alive:
+            raise time.DeadAgent(self.unique_id)
+
+    def log(self, *message, level=logging.INFO, **kwargs):
+        if not self.logger.isEnabledFor(level):
+            return
+        message = " ".join(str(i) for i in message)
+        message = "[@{:>4}]\t{:>10}: {}".format(self.now, repr(self), message)
+        for k, v in kwargs:
+            message += " {k}={v} ".format(k, v)
+        extra = {}
+        extra["now"] = self.now
+        extra["unique_id"] = self.unique_id
+        extra["agent_name"] = self.name
+        return self.logger.log(level, message, extra=extra)
+
+    def debug(self, *args, **kwargs):
+        return self.log(*args, level=logging.DEBUG, **kwargs)
+
+    def info(self, *args, **kwargs):
+        return self.log(*args, level=logging.INFO, **kwargs)
+
+    def count_agents(self, **kwargs):
+        return len(list(self.get_agents(**kwargs)))
+
+    def get_agents(self, *args, **kwargs):
+        it = self.iter_agents(*args, **kwargs)
+        return list(it)
+
+    def iter_agents(self, *args, **kwargs):
+        yield from filter_agents(self.model.schedule._agents, *args, **kwargs)
+
+    def __str__(self):
+        return self.to_str()
+
+    def to_str(self, keys=None, skip=None, pretty=False):
+        content = dict(self.items(keys=keys))
+        if pretty and content:
+            d = content
+            content = "\n"
+            for k, v in d.items():
+                content += f"- {k}: {v}\n"
+            content = textwrap.indent(content, "    ")
+        return f"{repr(self)}{content}"
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self.unique_id})"
+
+
+def prob(prob, random):
+    """
+    A true/False uniform distribution with a given probability.
+    To be used like this:
+
+    .. code-block:: python
+
+          if prob(0.3):
+              do_something()
+
+    """
+    r = random.random()
+    return r < prob
+
+
+def calculate_distribution(network_agents=None, agent_class=None):
+    """
+    Calculate the threshold values (thresholds for a uniform distribution)
+    of an agent distribution given the weights of each agent type.
+
+    The input has this form: ::
+
+            [
+            {'agent_class': 'agent_class_1',
+                'weight': 0.2,
+                'state': {
+                    'id': 0
+                }
+            },
+            {'agent_class': 'agent_class_2',
+                'weight': 0.8,
+                'state': {
+                    'id': 1
+                }
+            }
+            ]
+
+    In this example, 20% of the nodes will be marked as type
+    'agent_class_1'.
+    """
+    if network_agents:
+        network_agents = [
+            deepcopy(agent) for agent in network_agents if not hasattr(agent, "id")
+        ]
+    elif agent_class:
+        network_agents = [{"agent_class": agent_class}]
+    else:
+        raise ValueError("Specify a distribution or a default agent type")
+
+    # Fix missing weights and incompatible types
+    for x in network_agents:
+        x["weight"] = float(x.get("weight", 1))
+
+    # Calculate the thresholds
+    total = sum(x["weight"] for x in network_agents)
+    acc = 0
+    for v in network_agents:
+        if "ids" in v:
+            continue
+        upper = acc + (v["weight"] / total)
+        v["threshold"] = [acc, upper]
+        acc = upper
+    return network_agents
+
+
+def _serialize_type(agent_class, known_modules=[], **kwargs):
+    if isinstance(agent_class, str):
+        return agent_class
+    known_modules += ["soil.agents"]
+    return serialization.serialize(agent_class, known_modules=known_modules, **kwargs)[
+        1
+    ]  # Get the name of the class
+
+
+def _deserialize_type(agent_class, known_modules=[]):
+    if not isinstance(agent_class, str):
+        return agent_class
+    known = known_modules + ["soil.agents", "soil.agents.custom"]
+    agent_class = serialization.deserializer(agent_class, known_modules=known)
+    return agent_class
+
+
+class AgentView(Mapping, Set):
+    """A lazy-loaded list of agents."""
+
+    __slots__ = ("_agents",)
+
+    def __init__(self, agents):
+        self._agents = agents
+
+    def __getstate__(self):
+        return {"_agents": self._agents}
+
+    def __setstate__(self, state):
+        self._agents = state["_agents"]
+
+    # Mapping methods
+    def __len__(self):
+        return len(self._agents)
+
+    def __iter__(self):
+        yield from self._agents.values()
+
+    def __getitem__(self, agent_id):
+        if isinstance(agent_id, slice):
+            raise ValueError(f"Slicing is not supported")
+        if agent_id in self._agents:
+            return self._agents[agent_id]
+        raise ValueError(f"Agent {agent_id} not found")
+
+    def filter(self, *args, **kwargs):
+        yield from filter_agents(self._agents, *args, **kwargs)
+
+    def one(self, *args, **kwargs):
+        return next(filter_agents(self._agents, *args, **kwargs))
+
+    def __call__(self, *args, **kwargs):
+        return list(self.filter(*args, **kwargs))
+
+    def __contains__(self, agent_id):
+        return agent_id in self._agents
+
+    def __str__(self):
+        return str(list(unique_id for unique_id in self.keys()))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self})"
+
+
+def filter_agents(
+    agents: dict,
+    *id_args,
+    unique_id=None,
+    state_id=None,
+    agent_class=None,
+    ignore=None,
+    state=None,
+    limit=None,
+    **kwargs,
+):
+    """
+    Filter agents given as a dict, by the criteria given as arguments (e.g., certain type or state id).
+    """
+    assert isinstance(agents, dict)
+
+    ids = []
+
+    if unique_id is not None:
+        if isinstance(unique_id, list):
+            ids += unique_id
+        else:
+            ids.append(unique_id)
+
+    if id_args:
+        ids += id_args
+
+    if ids:
+        f = (agents[aid] for aid in ids if aid in agents)
+    else:
+        f = agents.values()
+
+    if state_id is not None and not isinstance(state_id, (tuple, list)):
+        state_id = tuple([state_id])
+
+    if agent_class is not None:
+        agent_class = _deserialize_type(agent_class)
+        try:
+            agent_class = tuple(agent_class)
+        except TypeError:
+            agent_class = tuple([agent_class])
+
+    if ignore:
+        f = filter(lambda x: x not in ignore, f)
+
+    if state_id is not None:
+        f = filter(lambda agent: agent.get("state_id", None) in state_id, f)
+
+    if agent_class is not None:
+        f = filter(lambda agent: isinstance(agent, agent_class), f)
+
+    state = state or dict()
+    state.update(kwargs)
+
+    for k, v in state.items():
+        f = filter(lambda agent: getattr(agent, k, None) == v, f)
+
+    if limit is not None:
+        f = islice(f, limit)
+
+    yield from f
+
+
+def from_config(
+    cfg: config.AgentConfig, random, topology: nx.Graph = None
+) -> List[Dict[str, Any]]:
+    """
+    This function turns an agentconfig into a list of individual "agent specifications", which are just a dictionary
+    with the parameters that the environment will use to construct each agent.
+
+    This function does NOT return a list of agents, mostly because some attributes to the agent are not known at the
+    time of calling this function, such as `unique_id`.
+    """
+    default = cfg or config.AgentConfig()
+    if not isinstance(cfg, config.AgentConfig):
+        cfg = config.AgentConfig(**cfg)
+
+    agents = []
+
+    assigned_total = 0
+    assigned_network = 0
+
+    if cfg.fixed is not None:
+        agents, assigned_total, assigned_network = _from_fixed(
+            cfg.fixed, topology=cfg.topology, default=cfg
+        )
+
+    n = cfg.n
+
+    if cfg.distribution:
+        topo_size = len(topology) if topology else 0
+
+        networked = []
+        total = []
+
+        for d in cfg.distribution:
+            if d.strategy == config.Strategy.topology:
+                topo = d.topology if ("topology" in d.__fields_set__) else cfg.topology
+                if not topo:
+                    raise ValueError(
+                        'The "topology" strategy only works if the topology parameter is set to True'
+                    )
+                if not topo_size:
+                    raise ValueError(
+                        f"Topology does not have enough free nodes to assign one to the agent"
+                    )
+
+                networked.append(d)
+
+            if d.strategy == config.Strategy.total:
+                if not cfg.n:
+                    raise ValueError(
+                        'Cannot use the "total" strategy without providing the total number of agents'
+                    )
+                total.append(d)
+
+        if networked:
+            new_agents = _from_distro(
+                networked,
+                n=topo_size - assigned_network,
+                topology=topo,
+                default=cfg,
+                random=random,
+            )
+            assigned_total += len(new_agents)
+            assigned_network += len(new_agents)
+            agents += new_agents
+
+        if total:
+            remaining = n - assigned_total
+            agents += _from_distro(total, n=remaining, default=cfg, random=random)
+
+        if assigned_network < topo_size:
+            utils.logger.warn(
+                f"The total number of agents does not match the total number of nodes in "
+                "every topology. This may be due to a definition error: assigned: "
+                f"{ assigned } total size: { topo_size }"
+            )
+
+    return agents
+
+
+def _from_fixed(
+    lst: List[config.FixedAgentConfig],
+    topology: bool,
+    default: config.SingleAgentConfig,
+) -> List[Dict[str, Any]]:
+    agents = []
+
+    counts_total = 0
+    counts_network = 0
+
+    for fixed in lst:
+        agent = {}
+        if default:
+            agent = default.state.copy()
+        agent.update(fixed.state)
+        cls = serialization.deserialize(
+            fixed.agent_class or (default and default.agent_class)
+        )
+        agent["agent_class"] = cls
+        topo = (
+            fixed.topology
+            if ("topology" in fixed.__fields_set__)
+            else topology or default.topology
+        )
+
+        if topo:
+            agent["topology"] = True
+            counts_network += 1
+        if not fixed.hidden:
+            counts_total += 1
+        agents.append(agent)
+
+    return agents, counts_total, counts_network
+
+
+def _from_distro(
+    distro: List[config.AgentDistro],
+    n: int,
+    default: config.SingleAgentConfig,
+    random,
+    topology: str = None
+) -> List[Dict[str, Any]]:
+
+    agents = []
+
+    if n is None:
+        if any(lambda dist: dist.n is None, distro):
+            raise ValueError(
+                "You must provide a total number of agents, or the number of each type"
+            )
+        n = sum(dist.n for dist in distro)
+
+    weights = list(dist.weight if dist.weight is not None else 1 for dist in distro)
+    minw = min(weights)
+    norm = list(weight / minw for weight in weights)
+    total = sum(norm)
+    chunk = n // total
+
+    # random.choices would be enough to get a weighted distribution. But it can vary a lot for smaller k
+    # So instead we calculate our own distribution to make sure the actual ratios are close to what we would expect
+
+    # Calculate how many times each has to appear
+    indices = list(
+        chain.from_iterable([idx] * int(n * chunk) for (idx, n) in enumerate(norm))
+    )
+
+    # Complete with random agents following the original weight distribution
+    if len(indices) < n:
+        indices += random.choices(
+            list(range(len(distro))),
+            weights=[d.weight for d in distro],
+            k=n - len(indices),
+        )
+
+    # Deserialize classes for efficiency
+    classes = list(
+        serialization.deserialize(i.agent_class or default.agent_class) for i in distro
+    )
+
+    # Add them in random order
+    random.shuffle(indices)
+
+    for idx in indices:
+        d = distro[idx]
+        agent = d.state.copy()
+        cls = classes[idx]
+        agent["agent_class"] = cls
+        if default:
+            agent.update(default.state)
+        topology = (
+            d.topology
+            if ("topology" in d.__fields_set__)
+            else topology or default.topology
+        )
+        if topology:
+            agent["topology"] = topology
+        agents.append(agent)
+
+    return agents
+
+
+from .network_agents import *
+from .fsm import *
+from .evented import *
+from typing import Optional
+
+
+class Agent(NetworkAgent, FSM, EventedAgent):
+    """Default agent class, has both network and event capabilities"""
+
+
+from ..environment import NetworkEnvironment
+
+
+from .BassModel import *
+from .IndependentCascadeModel import *
+from .SISaModel import *
+from .CounterModel import *
+
+
+try:
+    import scipy
+    from .Geo import Geo
+except ImportError:
+    import sys
+
+    print("Could not load the Geo Agent, scipy is not installed", file=sys.stderr)
+
+
+def custom(cls, **kwargs):
+    """Create a new class from a template class and keyword arguments"""
+    return type(cls.__name__, (cls,), kwargs)

soil/agents/evented.py

@@ -0,0 +1,77 @@
+from . import BaseAgent
+from ..events import Message, Tell, Ask, TimedOut
+from ..time import BaseCond
+from functools import partial
+from collections import deque
+
+
+class ReceivedOrTimeout(BaseCond):
+    def __init__(
+        self, agent, expiration=None, timeout=None, check=True, ignore=False, **kwargs
+    ):
+        if expiration is None:
+            if timeout is not None:
+                expiration = agent.now + timeout
+        self.expiration = expiration
+        self.ignore = ignore
+        self.check = check
+        super().__init__(**kwargs)
+
+    def expired(self, time):
+        return self.expiration and self.expiration < time
+
+    def ready(self, agent, time):
+        return len(agent._inbox) or self.expired(time)
+
+    def return_value(self, agent):
+        if not self.ignore and self.expired(agent.now):
+            raise TimedOut("No messages received")
+        if self.check:
+            agent.check_messages()
+        return None
+
+    def schedule_next(self, time, delta, first=False):
+        if self._delta is not None:
+            delta = self._delta
+        return (time + delta, self)
+
+    def __repr__(self):
+        return f"ReceivedOrTimeout(expires={self.expiration})"
+
+
+class EventedAgent(BaseAgent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._inbox = deque()
+        self._processed = 0
+
+    def on_receive(self, *args, **kwargs):
+        pass
+
+    def received(self, *args, **kwargs):
+        return ReceivedOrTimeout(self, *args, **kwargs)
+
+    def tell(self, msg, sender=None):
+        self._inbox.append(Tell(timestamp=self.now, payload=msg, sender=sender))
+
+    def ask(self, msg, timeout=None, **kwargs):
+        ask = Ask(timestamp=self.now, payload=msg, sender=self)
+        self._inbox.append(ask)
+        expiration = float("inf") if timeout is None else self.now + timeout
+        return ask.replied(expiration=expiration, **kwargs)
+
+    def check_messages(self):
+        changed = False
+        while self._inbox:
+            msg = self._inbox.popleft()
+            self._processed += 1
+            if msg.expired(self.now):
+                continue
+            changed = True
+            reply = self.on_receive(msg.payload, sender=msg.sender)
+            if isinstance(msg, Ask):
+                msg.reply = reply
+        return changed
+
+
+Evented = EventedAgent

soil/agents/fsm.py

@@ -0,0 +1,148 @@
+from . import MetaAgent, BaseAgent
+from ..time import Delta
+
+from functools import partial, wraps
+import inspect
+
+
+def state(name=None, default=False):
+    def decorator(func, name=None):
+        """
+        A state function should return either a state id, or a tuple (state_id, when)
+        The default value for state_id is the current state id.
+        The default value for when is the interval defined in the environment.
+        """
+        if inspect.isgeneratorfunction(func):
+            orig_func = func
+
+            @wraps(func)
+            def func(self):
+                while True:
+                    if not self._coroutine:
+                        self._coroutine = orig_func(self)
+
+                    try:
+                        if self._last_except:
+                            n = self._coroutine.throw(self._last_except)
+                        else:
+                            n = self._coroutine.send(self._last_return)
+                        if n:
+                            return None, n
+                        return n
+                    except StopIteration as ex:
+                        self._coroutine = None
+                        next_state = ex.value
+                        if next_state is not None:
+                            self._set_state(next_state)
+                        return next_state
+                    finally:
+                        self._last_return = None
+                        self._last_except = None
+
+        func.id = name or func.__name__
+        func.is_default = default
+        return func
+
+    if callable(name):
+        return decorator(name)
+    else:
+        return partial(decorator, name=name)
+
+
+def default_state(func):
+    func.is_default = True
+    return func
+
+
+class MetaFSM(MetaAgent):
+    def __new__(mcls, name, bases, namespace):
+        states = {}
+        # Re-use states from inherited classes
+        default_state = None
+        for i in bases:
+            if isinstance(i, MetaFSM):
+                for state_id, state in i._states.items():
+                    if state.is_default:
+                        default_state = state
+                    states[state_id] = state
+
+        # Add new states
+        for attr, func in namespace.items():
+            if hasattr(func, "id"):
+                if func.is_default:
+                    default_state = func
+                states[func.id] = func
+
+        namespace.update(
+            {
+                "_default_state": default_state,
+                "_states": states,
+            }
+        )
+
+        return super(MetaFSM, mcls).__new__(
+            mcls=mcls, name=name, bases=bases, namespace=namespace
+        )
+
+
+class FSM(BaseAgent, metaclass=MetaFSM):
+    def __init__(self, init=True, **kwargs):
+        super().__init__(**kwargs, init=False)
+        if not hasattr(self, "state_id"):
+            if not self._default_state:
+                raise ValueError(
+                    "No default state specified for {}".format(self.unique_id)
+                )
+            self.state_id = self._default_state.id
+
+        self._coroutine = None
+        self.default_interval = Delta(self.model.interval)
+        self._set_state(self.state_id)
+        if init:
+            self.init()
+
+    @classmethod
+    def states(cls):
+        return list(cls._states.keys())
+
+    def step(self):
+        self.debug(f"Agent {self.unique_id} @ state {self.state_id}")
+
+        self._check_alive()
+        next_state = self._states[self.state_id](self)
+
+        when = None
+        try:
+            next_state, *when = next_state
+            if not when:
+                when = None
+            elif len(when) == 1:
+                when = when[0]
+            else:
+                raise ValueError(
+                    "Too many values returned. Only state (and time) allowed"
+                )
+        except TypeError:
+            pass
+
+        if next_state is not None:
+            self._set_state(next_state)
+
+        return when or self.default_interval
+
+    def _set_state(self, state, when=None):
+        if hasattr(state, "id"):
+            state = state.id
+        if state not in self._states:
+            raise ValueError("{} is not a valid state".format(state))
+        self.state_id = state
+        if when is not None:
+            self.model.schedule.add(self, when=when)
+        return state
+
+    def die(self, *args, **kwargs):
+        return self.dead, super().die(*args, **kwargs)
+
+    @state
+    def dead(self):
+        return self.die()

soil/agents/network_agents.py

@@ -0,0 +1,100 @@
+from . import BaseAgent
+
+
+class NetworkAgent(BaseAgent):
+    def __init__(self, *args, topology=None, init=True, node_id=None, **kwargs):
+        super().__init__(*args, init=False, **kwargs)
+
+        self.G = topology or self.model.G
+        assert self.G
+        if node_id is None:
+            nodes = self.random.choices(list(self.G.nodes), k=len(self.G))
+            for n_id in nodes:
+                if "agent" not in self.G.nodes[n_id] or self.G.nodes[n_id]["agent"] is None:
+                    node_id = n_id
+                    break
+            else:
+                node_id = len(self.G)
+                self.info(f"All nodes ({len(self.G)}) have an agent assigned, adding a new node to the graph for agent {self.unique_id}")
+                self.G.add_node(node_id)
+        assert node_id is not None
+        self.G.nodes[node_id]["agent"] = self
+        self.node_id = node_id
+        if init:
+            self.init()
+
+    def count_neighbors(self, state_id=None, **kwargs):
+        return len(self.get_neighbors(state_id=state_id, **kwargs))
+        if init:
+            self.init()
+
+    def iter_neighbors(self, **kwargs):
+        return self.iter_agents(limit_neighbors=True, **kwargs)
+
+    def get_neighbors(self, **kwargs):
+        return list(self.iter_neighbors(**kwargs))
+
+    @property
+    def node(self):
+        return self.G.nodes[self.node_id]
+
+    def iter_agents(self, unique_id=None, *, limit_neighbors=False, **kwargs):
+        unique_ids = None
+        if unique_ids is not None:
+            try:
+                unique_ids = set(unique_id)
+            except TypeError:
+                unique_ids = set([unique_id])
+
+        if limit_neighbors:
+            neighbor_ids = set()
+            for node_id in self.G.neighbors(self.node_id):
+                agent = self.G.nodes[node_id].get("agent")
+                if agent is not None:
+                    neighbor_ids.add(agent.unique_id)
+            if unique_ids:
+                unique_ids = unique_ids & neighbor_ids
+            else:
+                unique_ids = neighbor_ids
+            if not unique_ids:
+                return
+            unique_ids = list(unique_ids)
+        yield from super().iter_agents(unique_id=unique_ids, **kwargs)
+
+    def subgraph(self, center=True, **kwargs):
+        include = [self] if center else []
+        G = self.G.subgraph(
+            n.node_id for n in list(self.get_agents(**kwargs) + include)
+        )
+        return G
+
+    def remove_node(self):
+        self.debug(f"Removing node for {self.unique_id}: {self.node_id}")
+        self.G.remove_node(self.node_id)
+        self.node_id = None
+
+    def add_edge(self, other, edge_attr_dict=None, *edge_attrs):
+        if self.node_id not in self.G.nodes(data=False):
+            raise ValueError(
+                "{} not in list of existing agents in the network".format(
+                    self.unique_id
+                )
+            )
+        if other.node_id not in self.G.nodes(data=False):
+            raise ValueError(
+                "{} not in list of existing agents in the network".format(other)
+            )
+
+        self.G.add_edge(
+            self.node_id, other.node_id, edge_attr_dict=edge_attr_dict, *edge_attrs
+        )
+
+    def die(self, remove=True):
+        if not self.alive:
+            return None
+        if remove:
+            self.remove_node()
+        return super().die()
+
+
+NetAgent = NetworkAgent

soil/analysis.py

@@ -0,0 +1,56 @@
+import os
+import sys
+import sqlalchemy
+import pandas as pd
+from collections import namedtuple
+
+def plot(env, agent_df=None, model_df=None, steps=False, ignore=["agent_count", ]):
+    """Plot the model dataframe and agent dataframe together."""
+    if model_df is None:
+        model_df = env.model_df()
+    ignore = list(ignore)
+    if not steps:
+        ignore.append("step")
+    else:
+        ignore.append("time")
+
+    ax = model_df.drop(ignore, axis='columns').plot();
+    if agent_df is None:
+        try:
+            agent_df = env.agent_df()
+        except UserWarning:
+            print("No agent dataframe provided and no agent reporters found. Skipping agent plot.", file=sys.stderr)
+            return
+    if not agent_df.empty:
+        agent_df.unstack().apply(lambda x: x.value_counts(),
+                                 axis=1).fillna(0).plot(ax=ax,
+                                                        secondary_y=True)
+
+
+Results = namedtuple("Results", ["config", "parameters", "env", "agents"])
+#TODO implement reading from CSV and SQLITE
+def read_sql(fpath=None, name=None, include_agents=False):
+    if not (fpath is None) ^ (name is None):
+        raise ValueError("Specify either a path or a simulation name")
+    if name:
+        fpath = os.path.join("soil_output", name, f"{name}.sqlite")
+    fpath = os.path.abspath(fpath)
+    # TODO: improve url parsing. This is a hacky way to check we weren't given a URL
+    if "://" not in fpath:
+        fpath = f"sqlite:///{fpath}"
+    engine = sqlalchemy.create_engine(fpath)
+    with engine.connect() as conn:
+        env = pd.read_sql_table("env", con=conn,
+                                index_col="step").reset_index().set_index([
+                                    "simulation_id", "params_id",
+                                    "iteration_id", "step"
+                                ])
+        agents = pd.read_sql_table("agents", con=conn, index_col=["simulation_id", "params_id", "iteration_id", "step", "agent_id"])
+        config = pd.read_sql_table("configuration", con=conn, index_col="simulation_id")
+        parameters = pd.read_sql_table("parameters", con=conn, index_col=["iteration_id", "params_id", "simulation_id"])
+        try:
+            parameters = parameters.pivot(columns="key", values="value")
+        except Exception as e:
+            print(f"warning: coult not pivot parameters: {e}")
+
+        return Results(config, parameters, env, agents)

soil/config.py

@@ -0,0 +1,2 @@
+def load_config(cfg):
+    return cfg

soil/datacollection.py

@@ -0,0 +1,19 @@
+from mesa import DataCollector as MDC
+
+
+class SoilCollector(MDC):
+    def __init__(self, model_reporters=None, agent_reporters=None, tables=None, **kwargs):
+        model_reporters = model_reporters or {}
+        agent_reporters = agent_reporters or {}
+        tables = tables or {}
+        if 'agent_count' not in model_reporters:
+            model_reporters['agent_count'] = lambda m: m.schedule.get_agent_count()
+        if 'time' not in model_reporters:
+            model_reporters['time'] = lambda m: m.now
+        # if 'state_id' not in agent_reporters:
+        #     agent_reporters['state_id'] = lambda agent: getattr(agent, 'state_id', None)
+
+        super().__init__(model_reporters=model_reporters,
+                         agent_reporters=agent_reporters,
+                         tables=tables,
+                         **kwargs)

soil/debugging.py

@@ -0,0 +1,243 @@
+from __future__ import annotations
+
+import pdb
+import sys
+import os
+
+from textwrap import indent
+from functools import wraps
+
+from .agents import FSM, MetaFSM
+from mesa import Model, Agent
+
+
+def wrapcmd(func):
+    @wraps(func)
+    def wrapper(self, arg: str, temporary=False):
+        sys.settrace(self.trace_dispatch)
+
+        lastself = self
+        known = globals()
+        known.update(self.curframe.f_globals)
+        known.update(self.curframe.f_locals)
+        known["attrs"] = arg.strip().split()
+
+        this = known.get("self", None)
+
+        if isinstance(this, Model):
+            known["model"] = this
+        elif isinstance(this, Agent):
+            known["agent"] = this
+            known["model"] = this.model
+
+        known["self"] = lastself
+        return exec(func.__code__, known, known)
+
+    return wrapper
+
+
+class Debug(pdb.Pdb):
+    def __init__(self, *args, skip_soil=False, **kwargs):
+        skip = kwargs.get("skip", [])
+        if skip_soil:
+            skip.append("soil")
+            skip.append("contextlib")
+            skip.append("soil.*")
+            skip.append("mesa.*")
+        super(Debug, self).__init__(*args, skip=skip, **kwargs)
+        self.prompt = "[soil-pdb] "
+
+    @staticmethod
+    def _soil_agents(model, attrs=None, pretty=True, **kwargs):
+        for agent in model.agents(**kwargs):
+            d = agent
+            print(" - " + indent(agent.to_str(keys=attrs, pretty=pretty), "  "))
+
+    @wrapcmd
+    def do_soil_agents():
+        return Debug._soil_agents(model, attrs=attrs or None)
+
+    do_sa = do_soil_agents
+
+    @wrapcmd
+    def do_soil_list():
+        return Debug._soil_agents(model, attrs=["state_id"], pretty=False)
+
+    do_sl = do_soil_list
+
+    def do_continue_state(self, arg):
+        """Continue until next time this state is reached"""
+        self.do_break_state(arg, temporary=True)
+        return self.do_continue("")
+
+    do_cs = do_continue_state
+
+    @wrapcmd
+    def do_soil_agent():
+        if not agent:
+            print("No agent available")
+            return
+
+        keys = None
+        if attrs:
+            keys = []
+            for k in attrs:
+                for key in agent.keys():
+                    if key.startswith(k):
+                        keys.append(key)
+
+        print(agent.to_str(pretty=True, keys=keys))
+
+    do_aa = do_soil_agent
+
+    def do_break_step(self, arg: str):
+        """
+        Break before the next step.
+        """
+        try:
+            known = globals()
+            known.update(self.curframe.f_globals)
+            known.update(self.curframe.f_locals)
+            func = getattr(known["model"], "step")
+        except AttributeError as ex:
+            self.error(f"The model does not have a step function: {ex}")
+            return
+        if hasattr(func, "__func__"):
+            func = func.__func__
+
+        code = func.__code__
+        # use co_name to identify the bkpt (function names
+        # could be aliased, but co_name is invariant)
+        funcname = code.co_name
+        lineno = code.co_firstlineno
+        filename = code.co_filename
+
+        # Check for reasonable breakpoint
+        line = self.checkline(filename, lineno)
+        if not line:
+            raise ValueError("no line found")
+            # now set the break point
+
+        existing = self.get_breaks(filename, line)
+        if existing:
+            self.message("Breakpoint already exists at %s:%d" % (filename, line))
+            return
+        cond = f"self.schedule.steps > {model.schedule.steps}"
+        err = self.set_break(filename, line, True, cond, funcname)
+        if err:
+            self.error(err)
+        else:
+            bp = self.get_breaks(filename, line)[-1]
+            self.message("Breakpoint %d at %s:%d" % (bp.number, bp.file, bp.line))
+            return self.do_continue("")
+    
+    do_bstep = do_break_step
+
+    def do_break_state(self, arg: str, instances=None, temporary=False):
+        """
+        Break before a specified state is stepped into.
+        """
+
+        klass = None
+        state = arg
+        if not state:
+            self.error("Specify at least a state name")
+            return
+
+        state, *tokens = state.lstrip().split()
+        if tokens:
+            instances = list(eval(token) for token in tokens)
+
+        colon = state.find(":")
+
+        if colon > 0:
+            klass = state[:colon].rstrip()
+            state = state[colon + 1 :].strip()
+
+            print(klass, state, tokens)
+            klass = eval(klass, self.curframe.f_globals, self.curframe_locals)
+
+        if klass:
+            klasses = [klass]
+        else:
+            klasses = [
+                k
+                for k in self.curframe.f_globals.values()
+                if isinstance(k, type) and issubclass(k, FSM)
+            ]
+
+        if not klasses:
+            self.error("No agent classes found")
+
+        for klass in klasses:
+            try:
+                func = getattr(klass, state)
+            except AttributeError:
+                self.error(f"State {state} not found in class {klass}")
+                continue
+            if hasattr(func, "__func__"):
+                func = func.__func__
+
+            code = func.__code__
+            # use co_name to identify the bkpt (function names
+            # could be aliased, but co_name is invariant)
+            funcname = code.co_name
+            lineno = code.co_firstlineno
+            filename = code.co_filename
+
+            # Check for reasonable breakpoint
+            line = self.checkline(filename, lineno)
+            if not line:
+                raise ValueError("no line found")
+                # now set the break point
+            cond = None
+            if instances:
+                cond = f"self.unique_id in { repr(instances) }"
+
+            existing = self.get_breaks(filename, line)
+            if existing:
+                self.message("Breakpoint already exists at %s:%d" % (filename, line))
+                continue
+            err = self.set_break(filename, line, temporary, cond, funcname)
+            if err:
+                self.error(err)
+            else:
+                bp = self.get_breaks(filename, line)[-1]
+                self.message("Breakpoint %d at %s:%d" % (bp.number, bp.file, bp.line))
+
+    do_bs = do_break_state
+
+    def do_break_state_self(self, arg: str, temporary=False):
+        """
+        Break before a specified state is stepped into, for the current agent
+        """
+        agent = self.curframe.f_locals.get("self")
+        if not agent:
+            self.error("No current agent.")
+            self.error("Try this again when the debugger is stopped inside an agent")
+            return
+
+        arg = f"{agent.__class__.__name__}:{ arg } {agent.unique_id}"
+        return self.do_break_state(arg)
+
+    do_bss = do_break_state_self
+
+
+debugger = None
+
+
+def set_trace(frame=None, **kwargs):
+    global debugger
+    if debugger is None:
+        debugger = Debug(**kwargs)
+    frame = frame or sys._getframe().f_back
+    debugger.set_trace(frame)
+
+
+def post_mortem(traceback=None, **kwargs):
+    global debugger
+    if debugger is None:
+        debugger = Debug(**kwargs)
+    t = sys.exc_info()[2]
+    debugger.reset()
+    debugger.interaction(None, t)

soil/decorators.py

@@ -0,0 +1,6 @@
+def report(f: property):
+    if isinstance(f, property):
+        setattr(f.fget, "add_to_report", True)
+    else:
+        setattr(f, "add_to_report", True)
+    return f

soil/environment.py

@@ -0,0 +1,431 @@
+from __future__ import annotations
+
+import os
+import sqlite3
+import math
+import logging
+import inspect
+
+from typing import Any, Callable, Dict, Optional, Union, List, Type
+from collections import namedtuple
+from time import time as current_time
+from copy import deepcopy
+
+
+import networkx as nx
+
+from mesa import Model, Agent
+
+from . import agents as agentmod, datacollection, serialization, utils, time, network, events
+
+
+# TODO: maybe add metaclass to read attributes of a model
+
+class BaseEnvironment(Model):
+    """
+    The environment is key in a simulation. It controls how agents interact,
+    and what information is available to them.
+
+    This is an opinionated version of `mesa.Model` class, which adds many
+    convenience methods and abstractions.
+
+    The environment parameters and the state of every agent can be accessed
+    both by using the environment as a dictionary and with the environment's
+    :meth:`soil.environment.Environment.get` method.
+    """
+
+    collector_class = datacollection.SoilCollector
+
+    def __new__(cls,
+                *args: Any,
+                seed="default",
+                dir_path=None,
+                collector_class: type = None,
+                agent_reporters: Optional[Any] = None,
+                model_reporters: Optional[Any] = None,
+                tables: Optional[Any] = None,
+                **kwargs: Any) -> Any:
+        """Create a new model with a default seed value"""
+        self = super().__new__(cls, *args, seed=seed, **kwargs)
+        self.dir_path = dir_path or os.getcwd()
+        collector_class = collector_class or cls.collector_class
+        collector_class = serialization.deserialize(collector_class)
+        self.datacollector = collector_class(
+            model_reporters=model_reporters,
+            agent_reporters=agent_reporters,
+            tables=tables,
+        )
+        for k in dir(cls):
+            v = getattr(cls, k)
+            if isinstance(v, property):
+                v = v.fget
+            if getattr(v, "add_to_report", False):
+                self.add_model_reporter(k, v)
+
+        return self
+
+    def __init__(
+        self,
+        *,
+        id="unnamed_env",
+        seed="default",
+        dir_path=None,
+        schedule_class=time.TimedActivation,
+        interval=1,
+        logger = None,
+        agents: Optional[Dict] = None,
+        collector_class: type = datacollection.SoilCollector,
+        agent_reporters: Optional[Any] = None,
+        model_reporters: Optional[Any] = None,
+        tables: Optional[Any] = None,
+        init: bool = True,
+        **env_params,
+    ):
+
+        super().__init__()
+
+
+        self.current_id = -1
+
+        self.id = id
+
+        if logger:
+            self.logger = logger
+        else:
+            self.logger = utils.logger.getChild(self.id)
+
+        if schedule_class is None:
+            schedule_class = time.TimedActivation
+        else:
+            schedule_class = serialization.deserialize(schedule_class)
+
+        self.interval = interval
+        self.schedule = schedule_class(self)
+
+        for (k, v) in env_params.items():
+            self[k] = v
+
+        if agents:
+            self.add_agents(**agents)
+        if init:
+            self.init()
+            self.datacollector.collect(self)
+
+    def init(self):
+        pass
+
+    @property
+    def agents(self):
+        return agentmod.AgentView(self.schedule._agents)
+
+    def agent(self, *args, **kwargs):
+        return agentmod.AgentView(self.schedule._agents).one(*args, **kwargs)
+
+    def count_agents(self, *args, **kwargs):
+        return sum(1 for i in self.agents(*args, **kwargs))
+    
+    def agent_df(self, steps=False):
+        df = self.datacollector.get_agent_vars_dataframe()
+        if steps:
+            df.index.rename(["step", "agent_id"], inplace=True)
+            return df
+        model_df = self.datacollector.get_model_vars_dataframe()
+        df.index = df.index.set_levels(model_df.time, level=0).rename(["time", "agent_id"])
+        return df
+
+    def model_df(self, steps=False):
+        df = self.datacollector.get_model_vars_dataframe()
+        if steps:
+            return df
+        df.index.rename("step", inplace=True)
+        return df.reset_index().set_index("time")
+
+    @property
+    def now(self):
+        if self.schedule:
+            return self.schedule.time
+        raise Exception(
+            "The environment has not been scheduled, so it has no sense of time"
+        )
+    def init_agents(self):
+        pass
+
+    def add_agent(self, agent_class, unique_id=None, **agent):
+        if unique_id is None:
+            unique_id = self.next_id()
+
+        agent["unique_id"] = unique_id
+
+        agent = dict(**agent)
+        unique_id = agent.pop("unique_id", None)
+        if unique_id is None:
+            unique_id = self.next_id()
+
+        a = serialization.deserialize(agent_class)(unique_id=unique_id, model=self, **agent)
+
+        self.schedule.add(a)
+        return a
+
+    def add_agents(self, agent_classes: List[type], k, weights: Optional[List[float]] = None, **kwargs):
+        if isinstance(agent_classes, type):
+            agent_classes = [agent_classes]
+        if weights is None:
+            weights = [1] * len(agent_classes)
+
+        for cls in self.random.choices(agent_classes, weights=weights, k=k):
+            self.add_agent(agent_class=cls, **kwargs)
+
+    def log(self, message, *args, level=logging.INFO, **kwargs):
+        if not self.logger.isEnabledFor(level):
+            return
+        message = message + " ".join(str(i) for i in args)
+        message = " @{:>3}: {}".format(self.now, message)
+        for k, v in kwargs:
+            message += " {k}={v} ".format(k, v)
+        extra = {}
+        extra["now"] = self.now
+        extra["id"] = self.id
+        return self.logger.log(level, message, extra=extra)
+
+    def step(self):
+        """
+        Advance one step in the simulation, and update the data collection and scheduler appropriately
+        """
+        super().step()
+        self.schedule.step()
+        self.datacollector.collect(self)
+
+        if self.logger.isEnabledFor(logging.DEBUG):
+            msg = "Model data:\n"
+            max_width = max(len(k) for k in self.datacollector.model_vars.keys())
+            for (k, v) in self.datacollector.model_vars.items():
+                msg += f"\t{k:<{max_width}}: {v[-1]:>6}\n"
+            self.logger.debug(f"--- Steps: {self.schedule.steps:^5} - Time: {self.now:^5} --- " + msg)
+
+    def add_model_reporter(self, name, func=None):
+        if not func:
+            func = lambda env: getattr(env, name)
+        self.datacollector._new_model_reporter(name, func)
+
+    def add_agent_reporter(self, name, agent_type=None):
+        if agent_type:
+            reporter = lambda a: getattr(a, name) if isinstance(a, agent_type) else None
+        else:
+            reporter = lambda a: getattr(a, name, None)
+        self.datacollector._new_agent_reporter(name, reporter)
+
+    @classmethod
+    def run(cls, *,
+            name=None,
+            iterations=1,
+            num_processes=1, **kwargs):
+        from .simulation import Simulation
+        return Simulation(name=name or cls.__name__,
+                          model=cls, iterations=iterations,
+                          num_processes=num_processes, **kwargs).run()
+
+    def __getitem__(self, key):
+        try:
+            return getattr(self, key)
+        except AttributeError:
+            raise KeyError(f"key {key}  not found in environment")
+
+    def __delitem__(self, key):
+        return delattr(self, key)
+
+    def __contains__(self, key):
+        return hasattr(self, key)
+
+    def __setitem__(self, key, value):
+        setattr(self, key, value)
+
+    def __str__(self):
+        return str(dict(self))
+
+    def __len__(self):
+        return sum(1 for n in self.keys())
+
+    def __iter__(self):
+        return iter(self.agents())
+
+    def get(self, key, default=None):
+        return self[key] if key in self else default
+
+    def keys(self):
+        return (k for k in self.__dict__ if k[0] != "_")
+
+class NetworkEnvironment(BaseEnvironment):
+    """
+    The NetworkEnvironment is an environment that includes one or more networkx.Graph intances
+    and methods to associate agents to nodes and vice versa.
+    """
+
+    def __init__(self,
+                 *args,
+                 topology: Optional[Union[nx.Graph, str]] = None,
+                 agent_class: Optional[Type[agentmod.Agent]] = None,
+                 network_generator: Optional[Callable] = None,
+                 network_params: Optional[Dict] = {},
+                 init=True,
+                 **kwargs):
+        self.topology = topology
+        self.network_generator = network_generator
+        self.network_params = network_params
+        if topology or network_params or network_generator:
+            self.create_network(topology, generator=network_generator, **network_params)
+        else:
+            self.G = nx.Graph()
+        super().__init__(*args, **kwargs, init=False)
+
+        self.agent_class = agent_class
+        if agent_class:
+            self.agent_class = serialization.deserialize(agent_class)
+        if self.agent_class:
+            self.populate_network(self.agent_class)
+        self._check_agent_nodes()
+        if init:
+            self.init()
+            self.datacollector.collect(self)
+
+    def add_agent(self, agent_class, *args, node_id=None, topology=None, **kwargs):
+        if node_id is None and topology is None:
+            return super().add_agent(agent_class, *args, **kwargs)
+        try:
+            a = super().add_agent(agent_class, *args, node_id=node_id, **kwargs)
+        except TypeError:
+            self.logger.warning(f"Agent constructor for {agent_class} does not have a node_id attribute. Might be a bug.")
+            a = super().add_agent(agent_class, *args, **kwargs)
+        self.G.nodes[node_id]["agent"] = a
+        return a
+
+    def add_agents(self, *args, k=None, **kwargs):
+        if not k and not self.G:
+            raise ValueError("Cannot add agents to an empty network")
+        super().add_agents(*args, k=k or len(self.G), **kwargs)
+
+    def create_network(self, topology=None, generator=None, path=None, **network_params):
+        if topology is not None:
+            topology = network.from_topology(topology, dir_path=self.dir_path)
+        elif path is not None:
+            topology = network.from_topology(path, dir_path=self.dir_path)
+        elif generator is not None:
+            topology = network.from_params(generator=generator, dir_path=self.dir_path, **network_params)
+        else:
+            raise ValueError("topology must be a networkx.Graph or a string, or network_generator must be provided")
+        self.G = topology
+
+    def init_agents(self, *args, **kwargs):
+        """Initialize the agents from a"""
+        super().init_agents(*args, **kwargs)
+
+    @property
+    def network_agents(self):
+        """Return agents still alive and assigned to a node in the network."""
+        for (id, data) in self.G.nodes(data=True):
+            if "agent" in data:
+                agent = data["agent"]
+                if getattr(agent, "alive", True):
+                    yield agent
+
+    def add_node(self, agent_class, unique_id=None, node_id=None, **kwargs):
+        if unique_id is None:
+            unique_id = self.next_id()
+        if node_id is None:
+            node_id = network.find_unassigned(
+                G=self.G, shuffle=True, random=self.random
+            )
+            if node_id is None:
+                node_id = f"node_for_{unique_id}"
+
+        if node_id not in self.G.nodes:
+            self.G.add_node(node_id)
+
+        assert "agent" not in self.G.nodes[node_id]
+
+        a = self.add_agent(
+            unique_id=unique_id,
+            agent_class=agent_class,
+            topology=self.G,
+            node_id=node_id,
+            **kwargs,
+        )
+        a["visible"] = True
+        return a
+
+    def _check_agent_nodes(self):
+        """
+        Detect nodes that have agents assigned to them.
+        """
+        for (id, data) in self.G.nodes(data=True):
+            if "agent_id" in data:
+                agent = self.agents(data["agent_id"])
+                self.G.nodes[id]["agent"] = agent
+                assert not getattr(agent, "node_id", None) or agent.node_id == id
+                agent.node_id = id
+        for agent in self.agents():
+            if hasattr(agent, "node_id"):
+                node_id = agent["node_id"]
+                if node_id not in self.G.nodes:
+                    raise ValueError(f"Agent {agent} is assigned to node {agent.node_id} which is not in the network")
+                node = self.G.nodes[node_id]
+                if node.get("agent") is not None and node["agent"] != agent:
+                    raise ValueError(f"Node {node_id} already has a different agent assigned to it")
+                self.G.nodes[node_id]["agent"] = agent
+
+    def add_agents(self, agent_classes: List[type], k=None, weights: Optional[List[float]] = None, **kwargs):
+        if k is None:
+            k = len(self.G)
+            if not k:
+                raise ValueError("Cannot add agents to an empty network")
+        super().add_agents(agent_classes, k=k, weights=weights, **kwargs)
+
+    def agent_for_node_id(self, node_id):
+        return self.G.nodes[node_id].get("agent")
+
+    def populate_network(self, agent_class: List[Model], weights: List[float] = None, **agent_params):
+        if isinstance(agent_class, type):
+            agent_class = [agent_class]
+        else:
+            agent_class = list(agent_class)
+        if not weights:
+            weights = [1] * len(agent_class)
+        assert len(self.G)
+        classes = self.random.choices(agent_class, weights, k=len(self.G))
+        toadd = []
+        for (cls, (node_id, node)) in zip(classes, self.G.nodes(data=True)):
+            if "agent" in node:
+                continue
+            node["agent"] = None # Reserve
+            toadd.append(dict(node_id=node_id, topology=self.G,  agent_class=cls, **agent_params))
+        for d in toadd:
+            a = self.add_agent(**d)
+            self.G.nodes[d["node_id"]]["agent"] = a
+        assert all("agent" in node for (_, node) in self.G.nodes(data=True))
+        assert len(list(self.network_agents))
+
+
+class EventedEnvironment(BaseEnvironment):
+    def broadcast(self, msg, sender=None, expiration=None, ttl=None, **kwargs):
+        for agent in self.agents(**kwargs):
+            if agent == sender:
+                continue
+            self.logger.debug(f"Telling {repr(agent)}: {msg} ttl={ttl}")
+            try:
+                inbox = agent._inbox
+            except AttributeError:
+                self.logger.info(
+                    f"Agent {agent.unique_id} cannot receive events because it does not have an inbox"
+                )
+                continue
+            # Allow for AttributeError exceptions in this part of the code
+            inbox.append(
+                events.Tell(
+                    payload=msg,
+                    sender=sender,
+                    expiration=expiration if ttl is None else self.now + ttl,
+                )
+            )
+
+
+class Environment(NetworkEnvironment, EventedEnvironment):
+    """Default environment class, has both network and event capabilities"""

soil/events.py

@@ -0,0 +1,56 @@
+from .time import BaseCond
+from dataclasses import dataclass, field
+from typing import Any
+from uuid import uuid4
+
+
+class Event:
+    pass
+
+
+@dataclass
+class Message:
+    payload: Any
+    sender: Any = None
+    expiration: float = None
+    timestamp: float = None
+    id: int = field(default_factory=uuid4)
+
+    def expired(self, when):
+        return self.expiration is not None and self.expiration < when
+
+
+class Reply(Message):
+    source: Message
+
+
+class ReplyCond(BaseCond):
+    def __init__(self, ask, *args, **kwargs):
+        self._ask = ask
+        super().__init__(*args, **kwargs)
+
+    def ready(self, agent, time):
+        return self._ask.reply is not None or self._ask.expired(time)
+
+    def return_value(self, agent):
+        if self._ask.expired(agent.now):
+            raise TimedOut()
+        return self._ask.reply
+
+    def __repr__(self):
+        return f"ReplyCond({self._ask.id})"
+
+
+class Ask(Message):
+    reply: Message = None
+
+    def replied(self, expiration=None):
+        return ReplyCond(self)
+
+
+class Tell(Message):
+    pass
+
+
+class TimedOut(Exception):
+    pass

soil/exporters.py

@@ -0,0 +1,282 @@
+import os
+import sys
+from time import time as current_time
+from io import BytesIO
+from sqlalchemy import create_engine
+from textwrap import dedent, indent
+
+
+import matplotlib.pyplot as plt
+import networkx as nx
+import pandas as pd
+
+
+from .serialization import deserialize, serialize
+from .utils import try_backup, open_or_reuse, logger, timer
+
+
+from . import utils, network
+
+
+class DryRunner(BytesIO):
+    def __init__(self, fname, *args, copy_to=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.__fname = fname
+        self.__copy_to = copy_to
+
+    def write(self, txt):
+        if self.__copy_to:
+            self.__copy_to.write("{}:::{}".format(self.__fname, txt))
+        try:
+            super().write(txt)
+        except TypeError:
+            super().write(bytes(txt, "utf-8"))
+
+    def close(self):
+        content = "(binary data not shown)"
+        try:
+            content = self.getvalue().decode()
+        except UnicodeDecodeError:
+            pass
+        logger.info(
+            "**Not** written to {} (no_dump mode):\n\n{}\n\n".format(
+                self.__fname, content
+            )
+        )
+        super().close()
+
+
+class Exporter:
+    """
+    Interface for all exporters. It is not necessary, but it is useful
+    if you don't plan to implement all the methods.
+    """
+
+    def __init__(self, simulation, outdir=None, dump=True, copy_to=None):
+        self.simulation = simulation
+        outdir = outdir or os.path.join(os.getcwd(), "soil_output")
+        self.outdir = os.path.join(outdir, simulation.group or "", simulation.name)
+        self.dump = dump
+        if copy_to is None and not dump:
+            copy_to = sys.stdout
+        self.copy_to = copy_to
+
+    def sim_start(self):
+        """Method to call when the simulation starts"""
+        pass
+
+    def sim_end(self):
+        """Method to call when the simulation ends"""
+        pass
+
+    def iteration_start(self, env):
+        """Method to call when a iteration start"""
+        pass
+
+    def iteration_end(self, env, params, params_id):
+        """Method to call when a iteration ends"""
+        pass
+
+    def output(self, f, mode="w", **kwargs):
+        if not self.dump:
+            f = DryRunner(f, copy_to=self.copy_to)
+        else:
+            try:
+                if not os.path.isabs(f):
+                    f = os.path.join(self.outdir, f)
+            except TypeError:
+                pass
+        return open_or_reuse(f, mode=mode, backup=self.simulation.backup, **kwargs)
+
+    def get_dfs(self, env, **kwargs):
+        yield from get_dc_dfs(env.datacollector,
+                              simulation_id=self.simulation.id,
+                              iteration_id=env.id,
+                              **kwargs)
+
+
+def get_dc_dfs(dc, **kwargs):
+    dfs = {}
+    dfe = dc.get_model_vars_dataframe()
+    dfe.index.rename("step", inplace=True)
+    dfs["env"] = dfe
+    try:
+        dfa = dc.get_agent_vars_dataframe() 
+        dfa.index.rename(["step", "agent_id"], inplace=True)
+        dfs["agents"] = dfa
+    except UserWarning:
+        pass
+    for table_name in dc.tables:
+        dfs[table_name] = dc.get_table_dataframe(table_name)
+    for (name, df) in dfs.items():
+        for (k, v) in kwargs.items():
+            df[k] = v
+        df.set_index(["simulation_id", "iteration_id"], append=True, inplace=True)
+    
+    yield from dfs.items()
+
+
+class SQLite(Exporter):
+    """Writes sqlite results"""
+    sim_started = False
+
+    def sim_start(self):
+        if not self.dump:
+            logger.debug("NOT dumping results")
+            return
+        self.dbpath = os.path.join(self.outdir, f"{self.simulation.name}.sqlite")
+        logger.info("Dumping results to %s", self.dbpath)
+        if self.simulation.backup:
+            try_backup(self.dbpath, remove=True)
+        
+        if self.simulation.overwrite:
+            if os.path.exists(self.dbpath):
+                os.remove(self.dbpath)
+        
+        self.engine = create_engine(f"sqlite:///{self.dbpath}", echo=False)
+
+        sim_dict = {k: serialize(v)[0] for (k,v) in self.simulation.to_dict().items()}
+        sim_dict["simulation_id"] = self.simulation.id
+        df = pd.DataFrame([sim_dict])
+        df.to_sql("configuration", con=self.engine, if_exists="append")
+
+    def iteration_end(self, env, params, params_id, *args, **kwargs):
+        if not self.dump:
+            logger.info("Running in NO DUMP mode. Results will NOT be saved to a DB.")
+            return
+
+        with timer(
+            "Dumping simulation {} iteration {}".format(self.simulation.name, env.id)
+        ):
+
+            pd.DataFrame([{"simulation_id": self.simulation.id,
+                           "params_id": params_id,
+                           "iteration_id": env.id,
+                           "key": k,
+                           "value": serialize(v)[0]} for (k,v) in params.items()]).to_sql("parameters", con=self.engine, if_exists="append")
+
+            for (t, df) in self.get_dfs(env, params_id=params_id):
+                df.to_sql(t, con=self.engine, if_exists="append")
+
+class csv(Exporter):
+    """Export the state of each environment (and its agents) a CSV file for the simulation"""
+
+    def sim_start(self):
+        super().sim_start()
+
+    def iteration_end(self, env, params, params_id, *args, **kwargs):
+        with timer(
+            "[CSV] Dumping simulation {} iteration {} @ dir {}".format(
+                self.simulation.name, env.id, self.outdir
+            )
+        ):
+            for (df_name, df) in self.get_dfs(env, params_id=params_id):
+                with self.output("{}.{}.csv".format(env.id, df_name), mode="a") as f:
+                    df.to_csv(f)
+
+
+# TODO: reimplement GEXF exporting without history
+class gexf(Exporter):
+    def iteration_end(self, env, *args, **kwargs):
+        if not self.dump:
+            logger.info("Not dumping GEXF (NO_DUMP mode)")
+            return
+
+        with timer(
+            "[GEXF] Dumping simulation {} iteration {}".format(self.simulation.name, env.id)
+        ):
+            with self.output("{}.gexf".format(env.id), mode="wb") as f:
+                network.dump_gexf(env.history_to_graph(), f)
+                self.dump_gexf(env, f)
+
+
+class dummy(Exporter):
+    def sim_start(self):
+        with self.output("dummy", "w") as f:
+            f.write("simulation started @ {}\n".format(current_time()))
+
+    def iteration_start(self, env):
+        with self.output("dummy", "w") as f:
+            f.write("iteration started@ {}\n".format(current_time()))
+
+    def iteration_end(self, env, *args, **kwargs):
+        with self.output("dummy", "w") as f:
+            f.write("iteration ended@ {}\n".format(current_time()))
+
+    def sim_end(self):
+        with self.output("dummy", "a") as f:
+            f.write("simulation ended @ {}\n".format(current_time()))
+
+
+class graphdrawing(Exporter):
+    def iteration_end(self, env, *args, **kwargs):
+        # Outside effects
+        f = plt.figure()
+        nx.draw(
+            env.G,
+            node_size=10,
+            width=0.2,
+            pos=nx.spring_layout(env.G, scale=100),
+            ax=f.add_subplot(111),
+        )
+        with open("graph-{}.png".format(env.id)) as f:
+            f.savefig(f)
+
+
+class summary(Exporter):
+    """Print a summary of each iteration to sys.stdout"""
+
+    def iteration_end(self, env, *args, **kwargs):
+        msg = ""
+        for (t, df) in self.get_dfs(env):
+            if not len(df):
+                continue
+            tabs = "\t" * 2
+            description = indent(str(df.describe()), tabs)
+            last_line = indent(str(df.iloc[-1:]), tabs)
+            # value_counts = indent(str(df.value_counts()), tabs)
+            value_counts = indent(str(df.apply(lambda x: x.value_counts()).T.stack()), tabs)
+
+            msg += dedent("""
+            Dataframe {t}:
+                Last line: :
+            {last_line}
+
+                Description:
+            {description}
+
+                Value counts:
+            {value_counts}
+
+            """).format(**locals())
+        logger.info(msg)
+
+class YAML(Exporter):
+    """Writes the configuration of the simulation to a YAML file"""
+
+    def sim_start(self):
+        if not self.dump:
+            logger.debug("NOT dumping results")
+            return
+        with self.output(self.simulation.id + ".dumped.yml") as f:
+            logger.info(f"Dumping simulation configuration to {self.outdir}")
+            f.write(self.simulation.to_yaml())
+
+class default(Exporter):
+    """Default exporter. Writes sqlite results, as well as the simulation YAML"""
+
+    def __init__(self, *args, exporter_cls=[], **kwargs):
+        exporter_cls = exporter_cls or [YAML, SQLite]
+        self.inner = [cls(*args, **kwargs) for cls in exporter_cls]
+
+    def sim_start(self, *args, **kwargs):
+        for exporter in self.inner:
+            exporter.sim_start(*args, **kwargs)
+
+    def sim_end(self, *args, **kwargs):
+        for exporter in self.inner:
+            exporter.sim_end(*args, **kwargs)
+
+    def iteration_end(self, *args, **kwargs):
+        for exporter in self.inner:
+            exporter.iteration_end(*args, **kwargs)

soil/network.py

@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+from typing import Dict
+import os
+import sys
+import random
+
+import networkx as nx
+
+from . import config, serialization, basestring
+
+
+def from_topology(topology, dir_path: str = None):
+    if topology is None:
+        return nx.Graph()
+    if isinstance(topology, nx.Graph):
+        return topology
+
+    # If it's a dict, assume it's a node-link graph
+    if isinstance(topology, dict):
+        try:
+            return nx.json_graph.node_link_graph(topology)
+        except Exception as ex:
+            raise ValueError("Unknown topology format")
+    
+    # Otherwise, treat like a path
+    path = topology
+    if dir_path and not os.path.isabs(path):
+        path = os.path.join(dir_path, path)
+    extension = os.path.splitext(path)[1][1:]
+    kwargs = {}
+    if extension == "gexf":
+        kwargs["version"] = "1.2draft"
+        kwargs["node_type"] = int
+    try:
+        method = getattr(nx.readwrite, "read_" + extension)
+    except AttributeError:
+        raise AttributeError("Unknown format")
+    return method(path, **kwargs)
+
+
+def from_params(generator, dir_path: str = None, **params):
+
+    if dir_path not in sys.path:
+        sys.path.append(dir_path)
+
+    method = serialization.deserializer(
+        generator,
+        known_modules=[
+            "networkx.generators",
+        ],
+    )
+    return method(**params)
+
+
+def find_unassigned(G, shuffle=False, random=random):
+    """
+    Link an agent to a node in a topology.
+
+    If node_id is None, a node without an agent_id will be found.
+    """
+    candidates = list(G.nodes(data=True))
+    if shuffle:
+        random.shuffle(candidates)
+    for next_id, data in candidates:
+        if "agent" not in data:
+            return next_id
+    return None
+
+
+def dump_gexf(G, f):
+    for node in G.nodes():
+        if "pos" in G.nodes[node]:
+            G.nodes[node]["viz"] = {
+                "position": {
+                    "x": G.nodes[node]["pos"][0],
+                    "y": G.nodes[node]["pos"][1],
+                    "z": 0.0,
+                }
+            }
+            del G.nodes[node]["pos"]
+
+    nx.write_gexf(G, f, version="1.2draft")

soil/parameters.py

@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+from typing_extensions import Annotated
+import annotated_types
+from typing import *
+
+from dataclasses import dataclass
+
+class Parameter:
+    pass
+
+
+def floatrange(
+    *,
+    gt: Optional[float] = None,
+    ge: Optional[float] = None,
+    lt: Optional[float] = None,
+    le: Optional[float] = None,
+    multiple_of: Optional[float] = None,
+) -> type[float]:
+    return Annotated[ 
+        float,
+        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
+        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
+    ]
+
+function = Annotated[Callable, Parameter]
+Integer = Annotated[int, Parameter]
+Float = Annotated[float, Parameter]
+
+
+probability = floatrange(ge=0, le=1)

soil/serialization.py

@@ -0,0 +1,276 @@
+import os
+import logging
+import ast
+import sys
+import re
+import importlib
+import importlib.machinery, importlib.util
+from glob import glob
+from itertools import product, chain
+
+from contextlib import contextmanager
+
+import yaml
+import networkx as nx
+
+from . import config
+
+from jinja2 import Template
+
+
+logger = logging.getLogger("soil")
+
+
+def load_file(infile):
+    folder = os.path.dirname(infile)
+    if folder not in sys.path:
+        sys.path.append(folder)
+    with open(infile, "r") as f:
+        return list(chain.from_iterable(map(expand_template, load_string(f))))
+
+
+def load_string(string):
+    yield from yaml.load_all(string, Loader=yaml.FullLoader)
+
+
+def expand_template(config):
+    if "template" not in config:
+        yield config
+        return
+    if "vars" not in config:
+        raise ValueError(
+            ("You must provide a definition of variables" " for the template.")
+        )
+
+    template = config["template"]
+
+    if not isinstance(template, str):
+        template = yaml.dump(template)
+
+    template = Template(template)
+
+    params = params_for_template(config)
+
+    blank_str = template.render({k: 0 for k in params[0].keys()})
+    blank = list(load_string(blank_str))
+    if len(blank) > 1:
+        raise ValueError("Templates must not return more than one configuration")
+    if "name" in blank[0]:
+        raise ValueError("Templates cannot be named, use group instead")
+
+    for ps in params:
+        string = template.render(ps)
+        for c in load_string(string):
+            yield c
+
+
+def params_for_template(config):
+    sampler_config = config.get("sampler", {"N": 100})
+    sampler = sampler_config.pop("method", "SALib.sample.morris.sample")
+    sampler = deserializer(sampler)
+    bounds = config["vars"]["bounds"]
+
+    problem = {
+        "num_vars": len(bounds),
+        "names": list(bounds.keys()),
+        "bounds": list(v for v in bounds.values()),
+    }
+    samples = sampler(problem, **sampler_config)
+
+    lists = config["vars"].get("lists", {})
+    names = list(lists.keys())
+    values = list(lists.values())
+    combs = list(product(*values))
+
+    allnames = names + problem["names"]
+    allvalues = [(list(i[0]) + list(i[1])) for i in product(combs, samples)]
+    params = list(map(lambda x: dict(zip(allnames, x)), allvalues))
+    return params
+
+
+def load_files(*patterns, **kwargs):
+    for pattern in patterns:
+        for i in glob(pattern, **kwargs, recursive=True):
+            for cfg in load_file(i):
+                path = os.path.abspath(i)
+                yield cfg, path
+
+
+def load_config(cfg):
+    if isinstance(cfg, dict):
+        yield config.load_config(cfg), os.getcwd()
+    else:
+        yield from load_files(cfg)
+
+
+builtins = importlib.import_module("builtins")
+
+KNOWN_MODULES = {
+    'soil': None,
+
+}
+
+MODULE_FILES = {}
+
+def _add_source_file(file):
+    """Add a file to the list of known modules"""
+    file = os.path.abspath(file)
+    if file in MODULE_FILES:
+        logger.warning(f"File {file} already added as module {MODULE_FILES[file]}. Reloading")
+        _remove_source_file(file)
+    modname = f"imported_module_{len(MODULE_FILES)}"
+    loader = importlib.machinery.SourceFileLoader(modname, file)
+    spec = importlib.util.spec_from_loader(loader.name, loader)
+    my_module = importlib.util.module_from_spec(spec)
+    loader.exec_module(my_module)
+    MODULE_FILES[file] = modname
+    KNOWN_MODULES[modname] = my_module
+
+def _remove_source_file(file):
+    """Remove a file from the list of known modules"""
+    file = os.path.abspath(file)
+    modname = None
+    try:
+        modname = MODULE_FILES.pop(file)
+        KNOWN_MODULES.pop(modname)
+    except KeyError as ex:
+        raise ValueError(f"File {file} had not been added as a module: {ex}")
+
+
+@contextmanager
+def with_source(file=None):
+    """Add a file to the list of known modules, and remove it afterwards"""
+    if file:
+        _add_source_file(file)
+    try:
+        yield
+    finally:
+        if file:
+            _remove_source_file(file)
+
+def get_module(modname):
+    """Get a module from the list of known modules"""
+    if modname not in KNOWN_MODULES or KNOWN_MODULES[modname] is None:
+        module = importlib.import_module(modname)
+        KNOWN_MODULES[modname] = module
+    return KNOWN_MODULES[modname]
+
+
+def name(value, known_modules=KNOWN_MODULES):
+    """Return a name that can be imported, to serialize/deserialize an object"""
+    if value is None:
+        return "None"
+    if not isinstance(value, type):  # Get the class name first
+        value = type(value)
+    tname = value.__name__
+    if hasattr(builtins, tname):
+        return tname
+    modname = value.__module__
+    if modname == "__main__":
+        return tname
+    if known_modules and modname in known_modules:
+        return tname
+    for kmod in known_modules:
+        module = get_module(kmod)
+        if hasattr(module, tname):
+            return tname
+    return "{}.{}".format(modname, tname)
+
+
+def serializer(type_):
+    if type_ != "str" and hasattr(builtins, type_):
+        return repr
+    return lambda x: x
+
+
+def serialize(v, known_modules=KNOWN_MODULES):
+    """Get a text representation of an object."""
+    tname = name(v, known_modules=known_modules)
+    func = serializer(tname)
+    return func(v), tname
+
+
+def serialize_dict(d, known_modules=KNOWN_MODULES):
+    try:
+        d = dict(d)
+    except (ValueError, TypeError) as ex:
+        return serialize(d)[0]
+    for (k, v) in reversed(list(d.items())):
+        if isinstance(v, dict):
+            d[k] = serialize_dict(v, known_modules=known_modules)
+        elif isinstance(v, list):
+            for ix in range(len(v)):
+                v[ix] = serialize_dict(v[ix], known_modules=known_modules)
+        elif isinstance(v, type):
+            d[k] = serialize(v, known_modules=known_modules)[1]
+    return d
+
+
+IS_CLASS = re.compile(r"<class '(.*)'>")
+
+
+def deserializer(type_, known_modules=KNOWN_MODULES):
+    if type(type_) != str:  # Already deserialized
+        return type_
+    if type_ == "str":
+        return lambda x="": x
+    if type_ == "None":
+        return lambda x=None: None
+    if hasattr(builtins, type_):  # Check if it's a builtin type
+        cls = getattr(builtins, type_)
+        return lambda x=None: ast.literal_eval(x) if x is not None else cls()
+    match = IS_CLASS.match(type_)
+    if match:
+        modname, tname = match.group(1).rsplit(".", 1)
+        module = get_module(modname)
+        cls = getattr(module, tname)
+        return getattr(cls, "deserialize", cls)
+
+    # Otherwise, see if we can find the module and the class
+    options = []
+
+    for mod in known_modules:
+        if mod:
+            options.append((mod, type_))
+
+    if "." in type_:  # Fully qualified module
+        module, type_ = type_.rsplit(".", 1)
+        options.append((module, type_))
+
+    errors = []
+    for modname, tname in options:
+        try:
+            module = get_module(modname)
+            cls = getattr(module, tname)
+            return getattr(cls, "deserialize", cls)
+        except (ImportError, AttributeError) as ex:
+            errors.append((modname, tname, ex))
+    raise ValueError('Could not find type "{}". Tried: {}'.format(type_, errors))
+
+
+def deserialize(type_, value=None, globs=None, **kwargs):
+    """Get an object from a text representation"""
+    if not isinstance(type_, str):
+        return type_
+    if globs and type_ in globs:
+        des = globs[type_]
+    else:
+        try:
+            des = deserializer(type_, **kwargs)
+        except ValueError as ex:
+            try:
+                des = eval(type_)
+            except Exception:
+                raise ex
+    if value is None:
+        return des
+    return des(value)
+
+
+def deserialize_all(names, *args, known_modules=KNOWN_MODULES, **kwargs):
+    """Return the list of deserialized objects"""
+    objects = []
+    for name in names:
+        mod = deserialize(name, known_modules=known_modules)
+        objects.append(mod(*args, **kwargs))
+    return objects

soil/settings.py

@@ -0,0 +1 @@
+# General configuration

soil/simulation.py

@@ -0,0 +1,384 @@
+import os
+from time import time as current_time, strftime
+import sys
+import yaml
+import hashlib
+
+import inspect
+import logging
+import networkx as nx
+
+from tqdm.auto import tqdm
+
+from textwrap import dedent
+
+from dataclasses import dataclass, field, asdict, replace
+from typing import Any, Dict, Union, Optional, List
+
+
+from functools import partial
+from contextlib import contextmanager
+from itertools import product
+import json
+
+
+from . import serialization, exporters, utils, basestring, agents
+from .environment import Environment
+from .utils import logger, run_and_return_exceptions
+from .debugging import set_trace
+
+_AVOID_RUNNING = False
+_QUEUED = []
+
+@contextmanager
+def do_not_run(): 
+    global _AVOID_RUNNING
+    _AVOID_RUNNING = True
+    try:
+        logger.debug("NOT RUNNING")
+        yield
+    finally:
+        logger.debug("RUNNING AGAIN")
+        _AVOID_RUNNING = False
+
+
+def _iter_queued():
+    while _QUEUED:
+        (cls, params) = _QUEUED.pop(0)
+        yield replace(cls, parameters=params)
+
+
+# TODO: change documentation for simulation
+# TODO: rename iterations to iterations
+# TODO: make parameters a dict of iterable/any
+@dataclass
+class Simulation:
+    """
+    A simulation is a collection of agents and a model. It is responsible for running the model and agents, and collecting data from them.
+
+    Args:
+        version: The version of the simulation. This is used to determine how to load the simulation.
+        name: The name of the simulation.
+        description: A description of the simulation.
+        group: The group that the simulation belongs to.
+        model: The model to use for the simulation. This can be a string or a class.
+        parameters: The parameters to pass to the model.
+        matrix: A matrix of values for each parameter.
+        seed: The seed to use for the simulation.
+        dir_path: The directory path to use for the simulation.
+        max_time: The maximum time to run the simulation.
+        max_steps: The maximum number of steps to run the simulation.
+        interval: The interval to use for the simulation.
+        iterations: The number of iterations (times) to run the simulation.
+        num_processes: The number of processes to use for the simulation. If greater than one, simulations will be performed in parallel. This may make debugging and error handling difficult.
+        tables: The tables to use in the simulation datacollector
+        agent_reporters: The agent reporters to use in the datacollector
+        model_reporters: The model reporters to use in the datacollector
+        dry_run: Whether or not to run the simulation. If True, the simulation will not be run.
+        backup: Whether or not to backup the simulation. If True, the simulation files will be backed up to a different directory.
+        overwrite: Whether or not to replace existing simulation data.
+        source_file: Python file to use to find additional classes.
+    """
+
+    version: str = "2"
+    source_file: Optional[str] = None
+    name: Optional[str] = None
+    description: Optional[str] = ""
+    group: str = None
+    backup: bool = False
+    overwrite: bool = False
+    dry_run: bool = False
+    dump: bool = False
+    model: Union[str, type] = "soil.Environment"
+    parameters: dict = field(default_factory=dict)
+    matrix: dict = field(default_factory=dict)
+    seed: str = "default"
+    dir_path: str = field(default_factory=lambda: os.getcwd())
+    max_time: float = None
+    max_steps: int = None
+    interval: int = 1
+    iterations: int = 1
+    num_processes: Optional[int] = 1
+    exporters: Optional[List[str]] = field(default_factory=lambda: [exporters.default])
+    model_reporters: Optional[Dict[str, Any]] = field(default_factory=dict)
+    agent_reporters: Optional[Dict[str, Any]] = field(default_factory=dict)
+    tables: Optional[Dict[str, Any]] = field(default_factory=dict)
+    outdir: str = field(default_factory=lambda: os.path.join(os.getcwd(), "soil_output"))
+    # outdir: Optional[str] = None
+    exporter_params: Optional[Dict[str, Any]] = field(default_factory=dict)
+    level: int = logging.INFO
+    skip_test: Optional[bool] = False
+    debug: Optional[bool] = False
+
+    def __post_init__(self):
+        if self.name is None:
+            if isinstance(self.model, str):
+                self.name = self.model
+            else:
+                self.name = self.model.__name__
+        self.logger = logger.getChild(self.name)
+        self.logger.setLevel(self.level)
+
+        if self.source_file and (not os.path.isabs(self.source_file)):
+            self.source_file = os.path.abspath(os.path.join(self.dir_path, self.source_file))
+        with serialization.with_source(self.source_file):
+
+            if isinstance(self.model, str):
+                self.model = serialization.deserialize(self.model)
+
+            def deserialize_reporters(reporters):
+                for (k, v) in reporters.items():
+                    if isinstance(v, str) and v.startswith("py:"):
+                        reporters[k] = serialization.deserialize(v.split(":", 1)[1])
+                return reporters
+
+            self.agent_reporters = deserialize_reporters(self.agent_reporters)
+            self.model_reporters = deserialize_reporters(self.model_reporters)
+            self.tables = deserialize_reporters(self.tables)
+            self.id = f"{self.name}_{current_time()}"
+
+    def run(self, **kwargs):
+        """Run the simulation and return the list of resulting environments"""
+        if kwargs:
+            return replace(self, **kwargs).run()
+
+        self.logger.debug(
+            dedent(
+                """
+        Simulation:
+        ---
+        """
+            )
+            + self.to_yaml()
+        )
+        param_combinations = self._collect_params(**kwargs)
+        if _AVOID_RUNNING:
+            _QUEUED.extend((self, param) for param in param_combinations)
+            return []
+
+        self.logger.debug("Using exporters: %s", self.exporters or [])
+
+        exporters = serialization.deserialize_all(
+            self.exporters,
+            simulation=self,
+            known_modules=[
+                "soil.exporters",
+            ],
+            dump=self.dump and not self.dry_run,
+            outdir=self.outdir,
+            **self.exporter_params,
+        )
+
+        results = []
+        for exporter in exporters:
+            exporter.sim_start()
+
+        for params in tqdm(param_combinations, desc=self.name, unit="configuration"):
+            for (k, v) in params.items():
+                tqdm.write(f"{k} = {v}")
+            sha = hashlib.sha256()
+            sha.update(repr(sorted(params.items())).encode())
+            params_id = sha.hexdigest()[:7]
+            for env in self._run_iters_for_params(params):
+                for exporter in exporters:
+                    exporter.iteration_end(env, params, params_id)
+                results.append(env)
+
+        for exporter in exporters:
+            exporter.sim_end()
+
+        return results
+
+    def _collect_params(self):
+
+        parameters = []
+        if self.parameters:
+            parameters.append(self.parameters)
+        if self.matrix:
+            assert isinstance(self.matrix, dict)
+            for values in product(*(self.matrix.values())):
+                parameters.append(dict(zip(self.matrix.keys(), values)))
+
+        if not parameters:
+            parameters = [{}]
+        if self.dump:
+            self.logger.info("Output directory: %s", self.outdir)
+
+        return parameters
+
+    def _run_iters_for_params(
+        self,
+        params
+    ):
+        """Run the simulation and yield the resulting environments."""
+
+        with serialization.with_source(self.source_file):
+            with utils.timer(f"running for config {params}"):
+                if self.dry_run:
+                    def func(*args, **kwargs):
+                        return None
+                else:
+                    func = self._run_model
+
+                for env in tqdm(utils.run_parallel(
+                    func=func,
+                    iterable=range(self.iterations),
+                    **params,
+                ), total=self.iterations, leave=False):
+                    if env is None and self.dry_run:
+                        continue
+
+                    yield env
+
+    def _get_env(self, iteration_id, params):
+        """Create an environment for a iteration of the simulation"""
+
+        iteration_id = str(iteration_id)
+
+        agent_reporters = self.agent_reporters
+        agent_reporters.update(params.pop("agent_reporters", {}))
+        model_reporters = self.model_reporters
+        model_reporters.update(params.pop("model_reporters", {}))
+
+        return self.model(
+            id=iteration_id,
+            seed=f"{self.seed}_iteration_{iteration_id}",
+            dir_path=self.dir_path,
+            interval=self.interval,
+            logger=self.logger.getChild(iteration_id),
+            agent_reporters=agent_reporters,
+            model_reporters=model_reporters,
+            tables=self.tables,
+            **params,
+        )
+
+    def _run_model(self, iteration_id, **params):
+        """
+        Run a single iteration of the simulation
+
+        """
+        # Set-up iteration environment and graph
+        model = self._get_env(iteration_id, params)
+        with utils.timer("Simulation {} iteration {}".format(self.name, iteration_id)):
+
+            max_time = self.max_time 
+            max_steps = self.max_steps
+
+            if (max_time is not None) and (max_steps is not None):
+                is_done = lambda model: (not model.running) or (model.schedule.time >= max_time) or (model.schedule.steps >= max_steps)
+            elif max_time is not None:
+                is_done = lambda model: (not model.running) or (model.schedule.time >= max_time)
+            elif max_steps is not None:
+                is_done = lambda model: (not model.running) or (model.schedule.steps >= max_steps)
+            else:
+                is_done = lambda model: not model.running
+            
+            if not model.schedule.agents:
+                raise Exception("No agents in model. This is probably a bug. Make sure that the model has agents scheduled after its initialization.")
+
+            newline = "\n"
+            self.logger.debug(
+                dedent(
+                    f"""
+    Model stats:
+    Agent count: { model.schedule.get_agent_count() }):
+    Topology size: { len(model.G) if hasattr(model, "G") else 0 }
+            """
+                )
+            )
+
+            if self.debug:
+                set_trace()
+
+            while not is_done(model):
+                self.logger.debug(
+                    f'Simulation time {model.schedule.time}/{max_time}.'
+                )
+                model.step()
+
+        return model
+
+    def to_dict(self):
+        d = asdict(self)
+        return serialization.serialize_dict(d)
+
+    def to_yaml(self):
+        return yaml.dump(self.to_dict())
+
+
+def iter_from_file(*files, **kwargs):
+    for f in files:
+        try:
+            yield from iter_from_py(f, **kwargs)
+        except ValueError as ex:
+            yield from iter_from_config(f, **kwargs)
+
+
+def from_file(*args, **kwargs):
+    return list(iter_from_file(*args, **kwargs))
+
+
+def iter_from_config(*cfgs, **kwargs):
+    for config in cfgs:
+        configs = list(serialization.load_config(config))
+        for config, path in configs:
+            d = dict(config)
+            d.update(kwargs)
+            if "dir_path" not in d:
+                d["dir_path"] = os.path.dirname(path)
+            yield Simulation(**d)
+
+
+def from_config(conf_or_path):
+    lst = list(iter_from_config(conf_or_path))
+    if len(lst) > 1:
+        raise AttributeError("Provide only one configuration")
+    return lst[0]
+
+
+def iter_from_py(pyfile, module_name='imported_file', **kwargs):
+    """Try to load every Simulation instance in a given Python file"""
+    import importlib
+    added = False
+    sims = []
+    assert not _AVOID_RUNNING
+    with do_not_run():
+        assert _AVOID_RUNNING
+        spec = importlib.util.spec_from_file_location(module_name, pyfile)
+        folder = os.path.dirname(pyfile)
+        if folder not in sys.path:
+            added = True
+            sys.path.append(folder)
+        if not spec:
+            raise ValueError(f"{pyfile} does not seem to be a Python module")
+        module = importlib.util.module_from_spec(spec)
+        sys.modules[module_name] = module
+        spec.loader.exec_module(module)
+        for (_name, sim) in inspect.getmembers(module, lambda x: isinstance(x, Simulation)):
+            sims.append(sim)
+        for sim in _iter_queued():
+            sims.append(sim)
+        if not sims:
+            for (_name, sim) in inspect.getmembers(module, lambda x: inspect.isclass(x) and issubclass(x, Simulation)):
+                sims.append(sim(**kwargs))
+        del sys.modules[module_name]
+    assert not _AVOID_RUNNING
+    if not sims:
+        raise AttributeError(f"No valid configurations found in {pyfile}")
+    if added:
+        sys.path.remove(folder)
+    for sim in sims:
+        yield replace(sim, **kwargs)
+
+
+def from_py(pyfile):
+    return next(iter_from_py(pyfile))
+
+
+def run_from_file(*files, **kwargs):
+    for sim in iter_from_file(*files):
+        logger.info(f"Using config(s): {sim.name}")
+        sim.run_simulation(**kwargs)
+
+def run(env, iterations=1, num_processes=1, dump=False, name="test", **kwargs):
+    return Simulation(model=env, iterations=iterations, name=name, dump=dump, num_processes=num_processes, **kwargs).run()

soil/time.py

@@ -0,0 +1,213 @@
+from mesa.time import BaseScheduler
+from queue import Empty
+from heapq import heappush, heappop, heapreplace
+import math
+
+from inspect import getsource
+from numbers import Number
+from textwrap import dedent
+
+from .utils import logger
+from mesa import Agent as MesaAgent
+
+
+INFINITY = float("inf")
+
+
+class DeadAgent(Exception):
+    pass
+
+
+class When:
+    def __init__(self, time):
+        if isinstance(time, When):
+            return time
+        self._time = time
+
+    def abs(self, time):
+        return self._time
+
+    def schedule_next(self, time, delta, first=False):
+        return (self._time, None)
+
+
+NEVER = When(INFINITY)
+
+
+class Delta(When):
+    def __init__(self, delta):
+        self._delta = delta
+
+    def abs(self, time):
+        return self._time + self._delta
+
+    def __eq__(self, other):
+        if isinstance(other, Delta):
+            return self._delta == other._delta
+        return False
+
+    def schedule_next(self, time, delta, first=False):
+        return (time + self._delta, None)
+
+    def __repr__(self):
+        return str(f"Delta({self._delta})")
+
+
+class BaseCond:
+    def __init__(self, msg=None, delta=None, eager=False):
+        self._msg = msg
+        self._delta = delta
+        self.eager = eager
+
+    def schedule_next(self, time, delta, first=False):
+        if first and self.eager:
+            return (time, self)
+        if self._delta:
+            delta = self._delta
+        return (time + delta, self)
+
+    def return_value(self, agent):
+        return None
+
+    def __repr__(self):
+        return self._msg or self.__class__.__name__
+
+
+class Cond(BaseCond):
+    def __init__(self, func, *args, **kwargs):
+        self._func = func
+        super().__init__(*args, **kwargs)
+
+    def ready(self, agent, time):
+        return self._func(agent)
+
+    def __repr__(self):
+        if self._msg:
+            return self._msg
+        return str(f'Cond("{dedent(getsource(self._func)).strip()}")')
+
+
+class TimedActivation(BaseScheduler):
+    """A scheduler which activates each agent when the agent requests.
+    In each activation, each agent will update its 'next_time'.
+    """
+
+    def __init__(self, *args, shuffle=True, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._next = {}
+        self._queue = []
+        self._shuffle = shuffle
+        # self.step_interval = getattr(self.model, "interval", 1)
+        self.step_interval = self.model.interval
+        self.logger = getattr(self.model, "logger", logger).getChild(f"time_{ self.model }")
+        self.next_time = self.time
+
+    def add(self, agent: MesaAgent, when=None):
+        if when is None:
+            when = self.time
+        elif isinstance(when, When):
+            when = when.abs()
+
+        self._schedule(agent, None, when)
+        super().add(agent)
+
+    def _schedule(self, agent, condition=None, when=None, replace=False):
+        if condition:
+            if not when:
+                when, condition = condition.schedule_next(
+                    when or self.time, self.step_interval
+                )
+        else:
+            if when is None:
+                when = self.time + self.step_interval
+            condition = None
+        if self._shuffle:
+            key = (when, self.model.random.random(), condition)
+        else:
+            key = (when, agent.unique_id, condition)
+        self._next[agent.unique_id] = key
+        if replace:
+            heapreplace(self._queue, (key, agent))
+        else:
+            heappush(self._queue, (key, agent))
+
+    def step(self) -> None:
+        """
+        Executes agents in order, one at a time. After each step,
+        an agent will signal when it wants to be scheduled next.
+        """
+
+        self.logger.debug(f"Simulation step {self.time}")
+        if not self.model.running or self.time == INFINITY:
+            return
+
+        self.logger.debug(f"Queue length: %s", len(self._queue))
+
+        while self._queue:
+            ((when, _id, cond), agent) = self._queue[0]
+            if when > self.time:
+                break
+
+            if cond:
+                if not cond.ready(agent, self.time):
+                    self._schedule(agent, cond, replace=True)
+                    continue
+                try:
+                    agent._last_return = cond.return_value(agent)
+                except Exception as ex:
+                    agent._last_except = ex
+            else:
+                agent._last_return = None
+                agent._last_except = None
+
+            self.logger.debug("Stepping agent %s", agent)
+            self._next.pop(agent.unique_id, None)
+
+            try:
+                returned = agent.step()
+            except DeadAgent:
+                agent.alive = False
+                heappop(self._queue)
+                continue
+
+            # Check status for MESA agents
+            if not getattr(agent, "alive", True):
+                heappop(self._queue)
+                continue
+
+            if returned:
+                next_check = returned.schedule_next(
+                    self.time, self.step_interval, first=True
+                )
+                self._schedule(agent, when=next_check[0], condition=next_check[1], replace=True)
+            else:
+                next_check = (self.time + self.step_interval, None)
+
+                self._schedule(agent, replace=True)
+
+        self.steps += 1
+
+        if not self._queue:
+            self.model.running = False
+            self.time = INFINITY
+            return
+
+        next_time = self._queue[0][0][0]
+
+        if next_time < self.time:
+            raise Exception(
+                f"An agent has been scheduled for a time in the past, there is probably an error ({when} < {self.time})"
+            )
+        self.logger.debug("Updating time step: %s -> %s ", self.time, next_time)
+
+        self.time = next_time
+
+
+class ShuffledTimedActivation(TimedActivation):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, shuffle=True, **kwargs)
+
+
+class OrderedTimedActivation(TimedActivation):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, shuffle=False, **kwargs)

soil/utils.py

@@ -0,0 +1,160 @@
+import logging
+from time import time as current_time, strftime, gmtime, localtime
+import os
+import traceback
+
+from functools import partial
+from shutil import copyfile, move
+from multiprocessing import Pool, cpu_count
+
+from contextlib import contextmanager
+
+logger = logging.getLogger("soil")
+logger.setLevel(logging.WARNING)
+
+timeformat = "%H:%M:%S"
+
+if os.environ.get("SOIL_VERBOSE", ""):
+    logformat = "[%(levelname)-5.5s][%(asctime)s][%(name)s]:  %(message)s"
+else:
+    logformat = "[%(levelname)-5.5s][%(asctime)s] %(message)s"
+
+logFormatter = logging.Formatter(logformat, timeformat)
+consoleHandler = logging.StreamHandler()
+consoleHandler.setFormatter(logFormatter)
+
+logging.basicConfig(
+    level=logging.INFO,
+    handlers=[
+        consoleHandler,
+    ],
+)
+
+
+@contextmanager
+def timer(name="task", pre="", function=logger.info, to_object=None):
+    start = current_time()
+    function("{}Starting {} at {}.".format(pre, name, strftime("%X", gmtime(start))))
+    yield start
+    end = current_time()
+    function(
+        "{}Finished {} at {} in {} seconds".format(
+            pre, name, strftime("%X", gmtime(end)), str(end - start)
+        )
+    )
+    if to_object:
+        to_object.start = start
+        to_object.end = end
+
+
+def try_backup(path, remove=False):
+    if not os.path.exists(path):
+        return None
+    outdir = os.path.dirname(path)
+    if outdir and not os.path.exists(outdir):
+        os.makedirs(outdir)
+    creation = os.path.getctime(path)
+    stamp = strftime("%Y-%m-%d_%H.%M.%S", localtime(creation))
+
+    backup_dir = os.path.join(outdir, "backup")
+    if not os.path.exists(backup_dir):
+        os.makedirs(backup_dir)
+    newpath = os.path.join(backup_dir, "{}@{}".format(os.path.basename(path), stamp))
+    if remove:
+        move(path, newpath)
+    else:
+        copyfile(path, newpath)
+    return newpath
+
+
+def safe_open(path, mode="r", backup=True, **kwargs):
+    outdir = os.path.dirname(path)
+    if outdir and not os.path.exists(outdir):
+        os.makedirs(outdir)
+    if backup and "w" in mode:
+        try_backup(path)
+    return open(path, mode=mode, **kwargs)
+
+
+@contextmanager
+def open_or_reuse(f, *args, **kwargs):
+    try:
+        with safe_open(f, *args, **kwargs) as f:
+            yield f
+    except (AttributeError, TypeError) as ex:
+        yield f
+
+
+def flatten_dict(d):
+    if not isinstance(d, dict):
+        return d
+    return dict(_flatten_dict(d))
+
+
+def _flatten_dict(d, prefix=""):
+    if not isinstance(d, dict):
+        # print('END:', prefix, d)
+        yield prefix, d
+        return
+    if prefix:
+        prefix = prefix + "."
+    for k, v in d.items():
+        # print(k, v)
+        res = list(_flatten_dict(v, prefix="{}{}".format(prefix, k)))
+        # print('RES:', res)
+        yield from res
+
+
+def unflatten_dict(d):
+    out = {}
+    for k, v in d.items():
+        target = out
+        if not isinstance(k, str):
+            target[k] = v
+            continue
+        tokens = k.split(".")
+        if len(tokens) < 2:
+            target[k] = v
+            continue
+        for token in tokens[:-1]:
+            if token not in target:
+                target[token] = {}
+            target = target[token]
+        target[tokens[-1]] = v
+    return out
+
+
+def run_and_return_exceptions(func, *args, **kwargs):
+    """
+    A wrapper for a function that catches exceptions and returns them.
+    It is meant for async simulations.
+    """
+    try:
+        return func(*args, **kwargs)
+    except Exception as ex:
+        if ex.__cause__ is not None:
+            ex = ex.__cause__
+        ex.message = "".join(
+            traceback.format_exception(type(ex), ex, ex.__traceback__)[:]
+        )
+        return ex
+
+
+def run_parallel(func, iterable, num_processes=1, **kwargs):
+    if num_processes > 1 and not os.environ.get("SOIL_DEBUG", None):
+        if num_processes < 1:
+            num_processes = cpu_count() - num_processes
+        p = Pool(processes=num_processes)
+        wrapped_func = partial(run_and_return_exceptions, func, **kwargs)
+        for i in p.imap_unordered(wrapped_func, iterable):
+            if isinstance(i, Exception):
+                logger.error("Trial failed:\n\t%s", i.message)
+                continue
+            yield i
+    else:
+        for i in iterable:
+            yield func(i, **kwargs)
+
+
+def int_seed(seed: str):
+    return int.from_bytes(seed.encode(), "little")

soil/version.py

@@ -0,0 +1,21 @@
+import os
+import logging
+
+logger = logging.getLogger(__name__)
+
+ROOT = os.path.dirname(__file__)
+DEFAULT_FILE = os.path.join(ROOT, "VERSION")
+
+
+def read_version(versionfile=DEFAULT_FILE):
+    try:
+        with open(versionfile) as f:
+            return f.read().strip()
+    except IOError:  # pragma: no cover
+        logger.error(
+            ("Running an unknown version of {}." "Be careful!.").format(__name__)
+        )
+        return "0.0"
+
+
+__version__ = read_version()

soil/web/.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+output/
+tests/
+soil_output/

soil/web/README.md

@@ -0,0 +1,59 @@
+# Graph Visualization with D3.js
+
+The aim of this software is to provide a useful tool for visualising and analysing the result of different simulations based on graph. Once you run the simulation, you will be able to interact with the simulation in real time.
+
+For this purpose, a model which tries to simulate the spread of information to comprehend the radicalism spread in a society is included. Whith all this, the main project goals could be divided in five as it is shown in the following.
+
+* Simulate the spread of information through a network applied to radicalism.
+* Visualize the results of the simulation.
+* Interact with the simulation in real time.
+* Extract data from the results.
+* Show data in a right way for its research.
+
+## Deploying the server
+
+For deploying the application, you will only need to run the following command.
+
+`python3 run.py [--name NAME] [--dump] [--port PORT] [--verbose]`
+
+Where the options are detailed in the following table.
+
+| Option | Description |
+| --- | --- |
+| `--name NAME` | The name of the simulation. It will appear on the app. |
+| `--dump` | For dumping the results in server side. |
+| `--port PORT` | The port where the server will listen. |
+| `--verbose` | Verbose mode. |
+
+> You can dump the results of the simulation in server side. Anyway, you will be able to download them in GEXF or JSON Graph format directly from the browser.
+
+## Visualization Params
+
+The configuration of the simulation is based on the simulator configuration. In this case, it follows the [SOIL](https://github.com/gsi-upm/soil) configuration syntax and for visualising the results in a more comfortable way, more params can be added in `visualization_params` dictionary.
+
+* For setting a background image, the tag needed is `background image`. You can also add a `background_opacity` and `background_filter_color` if the image is so clear than you can difficult view the nodes.
+* For setting colors to the nodes, you can do it based on their properties values. Using the `color` tag, you will need to indicate the attribute key and value, and then the color you want to apply.
+* The shapes applied to a group of nodes are always the same. This means than it won't change dynamically, so you will have to indicate the property with the `shape_property` tag and add a dictionary called `shapes` in which for each value, you indicate the shape.
+  All shapes have to had been downloaded before in SVG format and added to the server.
+
+An example of this configuration applied to the TerroristNetworkModel is presented.
+
+```yaml
+visualization_params:
+  # Icons downloaded from https://www.iconfinder.com/
+  shape_property: agent
+  shapes:
+    TrainingAreaModel: target
+    HavenModel: home
+    TerroristNetworkModel: person
+  colors:
+    - attr_id: 0
+      color: '#40de40'
+    - attr_id: 1
+      color: red
+    - attr_id: 2
+      color: '#c16a6a'
+  background_image: 'map_4800x2860.jpg'
+  background_opacity: '0.9'
+  background_filter_color: 'blue'
+```

soil/web/__init__.py

@@ -0,0 +1,334 @@
+import io
+import threading
+import asyncio
+import logging
+import networkx as nx
+import os
+import sys
+import tornado.ioloop
+import tornado.web
+import tornado.websocket
+import tornado.escape
+import tornado.gen
+import yaml
+import webbrowser
+from contextlib import contextmanager
+from time import sleep
+from xml.etree.ElementTree import tostring
+
+from tornado.concurrent import run_on_executor
+from concurrent.futures import ThreadPoolExecutor
+
+from ..simulation import Simulation
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+ROOT = os.path.abspath(os.path.dirname(__file__))
+
+MAX_WORKERS = 4
+LOGGING_INTERVAL = 0.5
+
+# Workaround to let Soil load the required modules
+sys.path.append(ROOT)
+
+
+class PageHandler(tornado.web.RequestHandler):
+    """Handler for the HTML template which holds the visualization."""
+
+    def get(self):
+        self.render(
+            "index.html", port=self.application.port, name=self.application.name
+        )
+
+
+class SocketHandler(tornado.websocket.WebSocketHandler):
+    """Handler for websocket."""
+
+    executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)
+
+    def open(self):
+        if self.application.verbose:
+            logger.info("Socket opened!")
+
+    def check_origin(self, origin):
+        return True
+
+    def on_message(self, message):
+        """Receiving a message from the websocket, parse, and act accordingly."""
+
+        msg = tornado.escape.json_decode(message)
+
+        if msg["type"] == "config_file":
+
+            if self.application.verbose:
+                print(msg["data"])
+
+            self.config = list(yaml.load_all(msg["data"]))
+
+            if len(self.config) > 1:
+                error = "Please, provide only one configuration."
+                if self.application.verbose:
+                    logger.error(error)
+                self.write_message({"type": "error", "error": error})
+                return
+
+            self.config = self.config[0]
+            self.send_log(
+                "INFO." + self.simulation_name,
+                "Using config: {name}".format(name=self.config["name"]),
+            )
+
+            if "visualization_params" in self.config:
+                self.write_message(
+                    {
+                        "type": "visualization_params",
+                        "data": self.config["visualization_params"],
+                    }
+                )
+            self.name = self.config["name"]
+            self.run_simulation()
+
+            settings = []
+            for key in self.config["environment_params"]:
+                if (
+                    type(self.config["environment_params"][key]) == float
+                    or type(self.config["environment_params"][key]) == int
+                ):
+                    if self.config["environment_params"][key] <= 1:
+                        setting_type = "number"
+                    else:
+                        setting_type = "great_number"
+                elif type(self.config["environment_params"][key]) == bool:
+                    setting_type = "boolean"
+                else:
+                    setting_type = "undefined"
+
+                settings.append(
+                    {
+                        "label": key,
+                        "type": setting_type,
+                        "value": self.config["environment_params"][key],
+                    }
+                )
+
+            self.write_message({"type": "settings", "data": settings})
+
+        elif msg["type"] == "get_trial":
+            if self.application.verbose:
+                logger.info("Trial {} requested!".format(msg["data"]))
+            self.send_log("INFO." + __name__, "Trial {} requested!".format(msg["data"]))
+            self.write_message(
+                {"type": "get_trial", "data": self.get_trial(int(msg["data"]))}
+            )
+
+        elif msg["type"] == "run_simulation":
+            if self.application.verbose:
+                logger.info(
+                    "Running new simulation for {name}".format(name=self.config["name"])
+                )
+            self.send_log(
+                "INFO." + self.simulation_name,
+                "Running new simulation for {name}".format(name=self.config["name"]),
+            )
+            self.config["environment_params"] = msg["data"]
+            self.run_simulation()
+
+        elif msg["type"] == "download_gexf":
+            G = self.trials[int(msg["data"])].history_to_graph()
+            for node in G.nodes():
+                if "pos" in G.nodes[node]:
+                    G.nodes[node]["viz"] = {
+                        "position": {
+                            "x": G.nodes[node]["pos"][0],
+                            "y": G.nodes[node]["pos"][1],
+                            "z": 0.0,
+                        }
+                    }
+                    del G.nodes[node]["pos"]
+            writer = nx.readwrite.gexf.GEXFWriter(version="1.2draft")
+            writer.add_graph(G)
+            self.write_message(
+                {
+                    "type": "download_gexf",
+                    "filename": self.config["name"] + "_trial_" + str(msg["data"]),
+                    "data": tostring(writer.xml).decode(writer.encoding),
+                }
+            )
+
+        elif msg["type"] == "download_json":
+            G = self.trials[int(msg["data"])].history_to_graph()
+            for node in G.nodes():
+                if "pos" in G.nodes[node]:
+                    G.nodes[node]["viz"] = {
+                        "position": {
+                            "x": G.nodes[node]["pos"][0],
+                            "y": G.nodes[node]["pos"][1],
+                            "z": 0.0,
+                        }
+                    }
+                    del G.nodes[node]["pos"]
+            self.write_message(
+                {
+                    "type": "download_json",
+                    "filename": self.config["name"] + "_trial_" + str(msg["data"]),
+                    "data": nx.node_link_data(G),
+                }
+            )
+
+        else:
+            if self.application.verbose:
+                logger.info("Unexpected message!")
+
+    def update_logging(self):
+        try:
+            if (
+                not self.log_capture_string.closed
+                and self.log_capture_string.getvalue()
+            ):
+                for i in range(len(self.log_capture_string.getvalue().split("\n")) - 1):
+                    self.send_log(
+                        "INFO." + self.simulation_name,
+                        self.log_capture_string.getvalue().split("\n")[i],
+                    )
+                self.log_capture_string.truncate(0)
+                self.log_capture_string.seek(0)
+        finally:
+            if self.capture_logging:
+                tornado.ioloop.IOLoop.current().call_later(
+                    LOGGING_INTERVAL, self.update_logging
+                )
+
+    def on_close(self):
+        if self.application.verbose:
+            logger.info("Socket closed!")
+
+    def send_log(self, logger, logging):
+        self.write_message({"type": "log", "logger": logger, "logging": logging})
+
+    @property
+    def simulation_name(self):
+        return self.config.get("name", "NoSimulationRunning")
+
+    @run_on_executor
+    def nonblocking(self, config):
+        simulation = Simulation(**config)
+        return simulation.run()
+
+    @tornado.gen.coroutine
+    def run_simulation(self):
+        # Run simulation and capture logs
+        logger.info("Running simulation!")
+        if "visualization_params" in self.config:
+            del self.config["visualization_params"]
+        with self.logging(self.simulation_name):
+            try:
+                config = dict(**self.config)
+                config["outdir"] = os.path.join(self.application.outdir, config["name"])
+                config["dump"] = self.application.dump
+                self.trials = yield self.nonblocking(config)
+
+                self.write_message(
+                    {
+                        "type": "trials",
+                        "data": list(trial.name for trial in self.trials),
+                    }
+                )
+            except Exception as ex:
+                error = "Something went wrong:\n\t{}".format(ex)
+                logging.info(error)
+                self.write_message({"type": "error", "error": error})
+                self.send_log("ERROR." + self.simulation_name, error)
+
+    def get_trial(self, trial):
+        logger.info("Available trials: %s " % len(self.trials))
+        logger.info("Ask for : %s" % trial)
+        trial = self.trials[trial]
+        G = trial.history_to_graph()
+        return nx.node_link_data(G)
+
+    @contextmanager
+    def logging(self, logger):
+        self.capture_logging = True
+        self.logger_application = logging.getLogger(logger)
+        self.log_capture_string = io.StringIO()
+        ch = logging.StreamHandler(self.log_capture_string)
+        self.logger_application.addHandler(ch)
+        self.update_logging()
+        yield self.capture_logging
+
+        sleep(0.2)
+        self.log_capture_string.close()
+        self.logger_application.removeHandler(ch)
+        self.capture_logging = False
+        return self.capture_logging
+
+
+class ModularServer(tornado.web.Application):
+    """Main visualization application."""
+
+    port = 8001
+    page_handler = (r"/", PageHandler)
+    socket_handler = (r"/ws", SocketHandler)
+    static_handler = (
+        r"/(.*)",
+        tornado.web.StaticFileHandler,
+        {"path": os.path.join(ROOT, "static")},
+    )
+    local_handler = (r"/local/(.*)", tornado.web.StaticFileHandler, {"path": ""})
+
+    handlers = [page_handler, socket_handler, static_handler, local_handler]
+    settings = {"debug": True, "template_path": ROOT + "/templates"}
+
+    def __init__(
+        self, dump=False, outdir="output", name="SOIL", verbose=True, *args, **kwargs
+    ):
+
+        self.verbose = verbose
+        self.name = name
+        self.dump = dump
+        self.outdir = outdir
+
+        # Initializing the application itself:
+        super().__init__(self.handlers, **self.settings)
+
+    def launch(self, port=None):
+        """Run the app."""
+
+        if port is not None:
+            self.port = port
+        url = "http://127.0.0.1:{PORT}".format(PORT=self.port)
+        print("Interface starting at {url}".format(url=url))
+        self.listen(self.port)
+        # webbrowser.open(url)
+        tornado.ioloop.IOLoop.instance().start()
+
+
+def run(*args, **kwargs):
+    asyncio.set_event_loop(asyncio.new_event_loop())
+    server = ModularServer(*args, **kwargs)
+    server.launch()
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Visualization of a Graph Model")
+
+    parser.add_argument(
+        "--name", "-n", nargs=1, default="SOIL", help="name of the simulation"
+    )
+    parser.add_argument(
+        "--dump", "-d", help="dumping results in folder output", action="store_true"
+    )
+    parser.add_argument(
+        "--port", "-p", nargs=1, default=8001, help="port for launching the server"
+    )
+    parser.add_argument("--verbose", "-v", help="verbose mode", action="store_true")
+    args = parser.parse_args()
+
+    run(
+        name=args.name,
+        port=(args.port[0] if isinstance(args.port, list) else args.port),
+        verbose=args.verbose,
+    )

soil/web/__main__.py

@@ -0,0 +1,5 @@
+from . import main
+
+
+if __name__ == "__main__":
+    main()