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

@@ -1,2 +1,7 @@
 **/soil_output
 .*
+**/.*
+**/__pycache__
+__pycache__
+*.pyc
+**/backup

.gitignore

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

.gitlab-ci.yml

@@ -1,9 +1,10 @@
 stages:
   - test
-  - build
+  - publish
+  - check_published
 
-build:
-  stage: build
+docker:
+  stage: publish
   image:
     name: gcr.io/kaniko-project/executor:debug
     entrypoint: [""]
@@ -16,13 +17,37 @@ build:
   only:
     - tags
 
-
 test:
-  except:
-    - tags  # Avoid running tests for tags, because they are already run for the branch
   tags:
     - docker
-  image: python:3.7
+  image: python:3.8
   stage: test
   script:
-    - python setup.py test
+    - 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`.

MANIFEST.in

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

Makefile

@@ -1,4 +1,7 @@
-test:
+quick-test:
 	docker-compose exec dev python -m pytest -s -v
 
-.PHONY: test
+test:
+	docker run -t -v $$PWD:/usr/src/app -w /usr/src/app python:3.7 python setup.py test
+
+.PHONY: test

README.md

@@ -1,9 +1,67 @@
 # [SOIL](https://github.com/gsi-upm/soil)
 
+
 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](examples/tutorial/soil_tutorial.ipynb) to develop your own agent models.
+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 
+
 
 If you use Soil in your research, don't forget to cite this paper:
 
@@ -28,7 +86,6 @@ If you use Soil in your research, don't forget to cite this paper:
 
 ```
 
-@Copyright GSI - Universidad Politécnica de Madrid 2017
-
-[![SOIL](logo_gsi.png)](https://www.gsi.dit.upm.es)
+@Copyright GSI - Universidad Politécnica de Madrid 2017-2021
 
+[![SOIL](logo_gsi.png)](https://www.gsi.upm.es)

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/configuration.rst

@@ -1,244 +0,0 @@
-Configuring a simulation
-------------------------
-
-There are two ways to configure a simulation: programmatically and with a configuration file.
-In both cases, the parameters used are the same.
-The advantage of a configuration file is that it is a clean declarative description, and it makes it easier to reproduce.
-
-Simulation configuration files can be formatted in ``json`` or ``yaml`` and they define all the parameters of a simulation.
-Here's an example (``example.yml``).
-
-.. code:: yaml
-          
-    ---
-    name: MyExampleSimulation
-    max_time: 50
-    num_trials: 3
-    interval: 2
-    network_params:
-        generator: barabasi_albert_graph
-        n: 100
-        m: 2
-    network_agents:
-        - agent_type: SISaModel
-           weight: 1
-            state:
-            id: content
-        - agent_type: SISaModel
-            weight: 1
-            state:
-            id: discontent
-        - agent_type: SISaModel
-            weight: 8
-            state:
-            id: neutral
-    environment_params:
-        prob_infect: 0.075
-
-
-This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
-The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil.
-10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
-All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
-The state of the agents will be updated every 2 seconds (``interval``).
-
-Now run the simulation with the command line tool:
-
-.. code:: bash
-
-   soil example.yml
-
-Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
-Three types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a ``sqlite`` file with the content of the state of every network node and the environment parameters at every step of the simulation.
-
-
-.. code::
-
-    soil_output
-    └── MyExampleSimulation
-        ├── MyExampleSimulation.dumped.yml
-        ├── MyExampleSimulation.simulation.pickle
-        ├── MyExampleSimulation_trial_0.db.sqlite
-        ├── MyExampleSimulation_trial_1.db.sqlite
-        └── MyExampleSimulation_trial_2.db.sqlite
-
-
-You may also ask soil to export the states in a ``csv`` file, and the network in gephi format (``gexf``).
-
-Network
-=======
-
-The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx.
-
-Loading a network
-#################
-
-To load an existing network, specify its path in the configuration:
-
-.. code:: yaml
-
-   ---
-   network_params:
-      path: /tmp/mynetwork.gexf
-
-Soil will try to guess what networkx method to use to read the file based on its extension.
-However, we only test using ``gexf`` files.
-
-For simple networks, you may also include them in the configuration itself using , using the ``topology`` parameter like so:
-
-.. code:: yaml
-
-   ---
-   topology:
-       nodes:
-          - id: First
-          - id: Second
-       links:
-          - source: First
-            target: Second
-
-
-Generating a random network
-###########################
-
-To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm <https://networkx.github.io/documentation/development/reference/generators.html>`_ and other parameters.
-For example, the following configuration is equivalent to :code:`nx.complete_graph(n=100)`:
-
-.. code:: yaml
-
-    network_params:
-        generator: complete_graph
-        n: 100
-
-Environment
-============
-The environment is the place where the shared state of the simulation is stored.
-For instance, the probability of disease outbreak.
-The configuration file may specify the initial value of the environment parameters:
-
-.. code:: yaml
-
-    environment_params:
-        daily_probability_of_earthquake: 0.001
-        number_of_earthquakes: 0
-
-All agents have access to the environment parameters.
-
-In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
-For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.
-
-
-Agents
-======
-Agents are a way of modelling behavior.
-Agents can be characterized with two variables: agent type (``agent_type``) and state.
-Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters.
-Through the environment, it can access the network topology and the state of other agents.
-
-There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
-
-Network Agents
-##############
-Network agents are attached to a node in the topology.
-The configuration file allows you to specify how agents will be mapped to topology nodes.
-
-The simplest way is to specify a single type of agent.
-Hence, every node in the network will be associated to an agent of that type.
-
-.. code:: yaml
-
-   agent_type: SISaModel
-
-It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property).
-For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
-
-.. code:: yaml
-
-    network_agents:
-          - agent_type: SISaModel
-            weight: 1
-          - agent_type: CounterModel
-            weight: 5
-
-The third option is to specify the type of agent on the node itself, e.g.:
-
-
-.. code:: yaml
-
-   topology:
-       nodes:
-           - id: first
-   agent_type: BaseAgent
-   states:
-       first:
-         agent_type: SISaModel
-          
-
-This would also work with a randomly generated network:
-
-
-.. code:: yaml
-
-   network:
-       generator: complete
-       n: 5
-   agent_type: BaseAgent
-   states:
-       - agent_type: SISaModel
-
-              
-
-In addition to agent type, you may add a custom initial state to the distribution.
-This is very useful to add the same agent type with different states.
-e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state:
-
-.. code:: yaml
-
-    network_agents:
-        - agent_type: SISaModel
-            weight: 9
-            state:
-            id: neutral
-        - agent_type: SISaModel
-            weight: 1
-            state:
-            id: discontent
-
-Lastly, the configuration may include initial state for one or more nodes.
-For instance, to add a state for the two nodes in this configuration:
-
-.. code:: yaml
-
-   agent_type: SISaModel
-   network:
-      generator: complete_graph
-      n: 2
-   states:
-     - id: content
-     - id: discontent
-
-
-Or to add state only to specific nodes (by ``id``).
-For example, to apply special skills to Linux Torvalds in a simulation:
-
-.. literalinclude:: ../examples/torvalds.yml
-   :language: yaml
-
-
-Environment Agents
-##################
-In addition to network agents, more agents can be added to the simulation.
-These agents are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes.
-
-
-.. code::
-
-   environment_agents:
-       - agent_type: MyAgent
-         state:
-           mood: happy
-       - agent_type: DummyAgent
-
-
-You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance.
-They are also useful to add behavior that has little to do with the network and the interactions within that network.

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

@@ -1,12 +1,21 @@
-.. 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 focused on 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:
 
@@ -38,9 +47,8 @@ If you use Soil in your research, do not forget to cite this paper:
    :caption: Learn more about soil:
 
    installation
-   quickstart
-   configuration
-   Tutorial <soil_tutorial>
+   Tutorial <tutorial/soil_tutorial>
+   notes_v1.0
 
 ..
 

docs/installation.rst

@@ -1,7 +1,10 @@
 Installation
 ------------
 
-The easiest way to install Soil is through pip, with Python >= 3.4:
+Through pip
+===========
+
+The easiest way to install Soil is through pip, with Python >= 3.8:
 
 .. code:: bash
 
@@ -14,11 +17,49 @@ Now test that it worked by running the command line tool
 
    soil --help
 
-Or using soil programmatically:
+   #or
+
+   python -m soil --help
+
+Or, if you're using using soil programmatically:
 
 .. code:: python
 
    import soil
    print(soil.__version__)
 
-The latest version can be installed through `GitLab <https://lab.cluster.gsi.dit.upm.es/soil/soil.git>`_.
+
+
+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/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/quickstart.rst

@@ -1,93 +0,0 @@
-Quickstart
-----------
-
-This section shows how to run your first simulation with Soil.
-For installation instructions, see :doc:`installation`.
-
-There are mainly two parts in a simulation: agent classes and simulation configuration.
-An agent class defines how the agent will behave throughout the simulation.
-The configuration includes things such as number of agents to use and their type, network topology to use, etc.
-
-
-.. image:: soil.png
-  :width: 80%
-  :align: center
-
-
-Soil includes several agent classes in the ``soil.agents`` module, and we will use them in this quickstart.
-If you are interested in developing your own agents classes, see :doc:`soil_tutorial`.
-
-Configuration
-=============
-To get you started, we will use this configuration (:download:`download the file <quickstart.yml>` directly):
-
-.. literalinclude:: quickstart.yml
-   :language: yaml
-
-The agent type used, SISa, is a very simple model.
-It only has three states (neutral, content and discontent),
-Its parameters are the probabilities to change from one state to another, either spontaneously or because of contagion from neighboring agents.
-
-Running the simulation
-======================
-
-To see the simulation in action, simply point soil to the configuration, and tell it to store the graph and the history of agent states and environment parameters at every point.
-
-.. code::
-
-    ❯ soil --graph --csv quickstart.yml                                                          [13:35:29]
-    INFO:soil:Using config(s): quickstart
-    INFO:soil:Dumping results to soil_output/quickstart : ['csv', 'gexf']
-    INFO:soil:Starting simulation quickstart at 13:35:30.
-    INFO:soil:Starting Simulation quickstart trial 0 at 13:35:30.
-    INFO:soil:Finished Simulation quickstart trial 0 at 13:35:49 in 19.43677067756653 seconds
-    INFO:soil:Starting Dumping simulation quickstart trial 0 at 13:35:49.
-    INFO:soil:Finished Dumping simulation quickstart trial 0 at 13:35:51 in 1.7733407020568848 seconds
-    INFO:soil:Dumping results to soil_output/quickstart
-    INFO:soil:Finished simulation quickstart at 13:35:51 in 21.29862952232361 seconds
-
-
-The ``CSV`` file should look like this:
-
-.. code::
-
-   agent_id,t_step,key,value
-   env,0,neutral_discontent_spon_prob,0.05
-   env,0,neutral_discontent_infected_prob,0.1
-   env,0,neutral_content_spon_prob,0.2
-   env,0,neutral_content_infected_prob,0.4
-   env,0,discontent_neutral,0.2
-   env,0,discontent_content,0.05
-   env,0,content_discontent,0.05
-   env,0,variance_d_c,0.05
-   env,0,variance_c_d,0.1
-
-Results and visualization
-=========================
-
-The environment variables are marked as ``agent_id`` env.
-Th exported values are only stored when they change.
-To find out how to get every key and value at every point in the simulation, check out the :doc:`soil_tutorial`.
-
-The dynamic graph is exported as a .gexf file which could be visualized with
-`Gephi <https://gephi.org/users/download/>`__.
-Now it is your turn to experiment with the simulation.
-Change some of the parameters, such as the number of agents, the probability of becoming content, or the type of network, and see how the results change.
-
-
-Soil also includes a web server that allows you to upload your simulations, change parameters, and visualize the results, including a timeline of the network.
-To make it work, you have to install soil like this:
-
-.. code::
-
-  pip install soil[web]
-
-Once installed, the soil web UI can be run in two ways:
-
-.. code::
-
-  soil-web
-
-  # OR
-
-  python -m soil.web

docs/quickstart.yml

@@ -1,30 +0,0 @@
----
-name: quickstart
-num_trials: 1
-max_time: 1000
-network_agents:
-  - agent_type: SISaModel
-    state:
-      id: neutral
-    weight: 1
-  - agent_type: SISaModel
-    state:
-      id: content
-    weight: 2
-network_params:
-  n: 100
-  k: 5
-  p: 0.2
-  generator: newman_watts_strogatz_graph
-environment_params:
-    neutral_discontent_spon_prob: 0.05
-    neutral_discontent_infected_prob: 0.1
-    neutral_content_spon_prob: 0.2
-    neutral_content_infected_prob: 0.4
-    discontent_neutral: 0.2
-    discontent_content: 0.05
-    content_discontent: 0.05
-    variance_d_c: 0.05
-    variance_c_d: 0.1
-    content_neutral: 0.1
-    standard_variance: 0.1

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/tutorial/soil_tutorial.html

@@ -12330,11 +12330,11 @@ Notice how node 0 is the only one with a TV.</p>
 <span class="n">sim</span> <span class="o">=</span> <span class="n">soil</span><span class="o">.</span><span class="n">Simulation</span><span class="p">(</span><span class="n">topology</span><span class="o">=</span><span class="n">G</span><span class="p">,</span>
                                      <span class="n">num_trials</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                      <span class="n">max_time</span><span class="o">=</span><span class="n">MAX_TIME</span><span class="p">,</span>
-                                     <span class="n">environment_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
+                                     <span class="n">environment_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
                                                          <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                                                              <span class="s1">&#39;event_time&#39;</span><span class="p">:</span> <span class="n">EVENT_TIME</span>
                                                          <span class="p">}}],</span>
-                                     <span class="n">network_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+                                     <span class="n">network_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
                                                       <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}],</span>
                                      <span class="n">states</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">}},</span>
                                      <span class="n">default_state</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">},</span>
@@ -12468,14 +12468,14 @@ For this demo, we will use a python dictionary:</p>
     <span class="p">},</span>
     <span class="s1">&#39;network_agents&#39;</span><span class="p">:</span> <span class="p">[</span>
         <span class="p">{</span>
-            <span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+            <span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
             <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
             <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                 <span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">False</span>
             <span class="p">}</span>
         <span class="p">},</span>
         <span class="p">{</span>
-            <span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+            <span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
             <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
             <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                 <span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">True</span>
@@ -12483,7 +12483,7 @@ For this demo, we will use a python dictionary:</p>
         <span class="p">}</span>
     <span class="p">],</span>
     <span class="s1">&#39;environment_agents&#39;</span><span class="p">:[</span>
-        <span class="p">{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
+        <span class="p">{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
          <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
              <span class="s1">&#39;event_time&#39;</span><span class="p">:</span> <span class="mi">10</span>
          <span class="p">}</span>

examples/complete.yml

@@ -1,28 +0,0 @@
----
-name: simple
-dir_path: "/tmp/"
-num_trials: 3
-dry_run: True
-max_time: 100
-interval: 1
-seed: "CompleteSeed!"
-dump: false
-network_params:
-  generator: complete_graph
-  n: 10
-network_agents:
-  - agent_type: CounterModel
-    weight: 1
-    state:
-      id: 0
-  - agent_type: AggregatedCounter
-    weight: 0.2
-environment_agents: []
-environment_class: Environment
-environment_params:
-  am_i_complete: true
-default_state:
-  incidents: 0
-states:
-  - name: 'The first node'
-  - name: 'The second node'

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.ipynb

@@ -2,13 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:30.732107Z",
      "start_time": "2017-11-08T17:22:30.059855+01:00"
-    },
-    "collapsed": true
+    }
    },
    "outputs": [],
    "source": [
@@ -28,24 +27,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:35.580593Z",
      "start_time": "2017-11-08T17:22:35.542745+01:00"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%pylab inline\n",
+    "%matplotlib inline\n",
     "\n",
     "from soil import *"
    ]
@@ -66,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:37.242327Z",
@@ -86,14 +77,14 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_dumb\r\n",
       "network_agents:\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -110,22 +101,22 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_half_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -142,18 +133,18 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
       "network_params:\r\n",
       "  generator: barabasi_albert_graph\r\n",
@@ -169,15 +160,15 @@
       "  prob_tv_spread: 0.01\r\n",
       "  prob_neighbor_cure: 0.1\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_wise_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -195,15 +186,15 @@
       "  prob_tv_spread: 0.01\r\n",
       "  prob_neighbor_cure: 0.1\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_wise\r\n",
       "network_agents:\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -225,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T18:07:46.781745Z",
@@ -233,7 +224,24 @@
     },
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "No objects to concatenate",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m evodumb \u001b[38;5;241m=\u001b[39m \u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msoil_output/Sim_all_dumb/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_count\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mid\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m;\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/soil/analysis.py:14\u001b[0m, in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m iterable \u001b[38;5;241m=\u001b[39m _read_data(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m group:\n\u001b[0;32m---> 14\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgroup_trials\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterable\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(iterable)\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/soil/analysis.py:201\u001b[0m, in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m    199\u001b[0m trials \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(trials)\n\u001b[1;32m    200\u001b[0m trials \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x, trials))\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrials\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgroupby(level\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39magg(aggfunc)\u001b[38;5;241m.\u001b[39mreorder_levels([\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m] ,axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/core/reshape/concat.py:368\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjs\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconcat\u001b[39m(\n\u001b[1;32m    148\u001b[0m     objs: Iterable[NDFrame] \u001b[38;5;241m|\u001b[39m Mapping[HashableT, NDFrame],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    157\u001b[0m     copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    158\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m    159\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;124;03m    Concatenate pandas objects along a particular axis.\u001b[39;00m\n\u001b[1;32m    161\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;124;03m    1   3   4\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 368\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    381\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/core/reshape/concat.py:425\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    422\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m    424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 425\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    428\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs))\n",
+      "\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
+     ]
+    }
+   ],
    "source": [
     "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', process=analysis.get_count, group=True, keys=['id']);"
    ]
@@ -721,9 +729,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-soil",
    "language": "python",
-   "name": "python3"
+   "name": "venv-soil"
   },
   "language_info": {
    "codemirror_mode": {
@@ -735,7 +743,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.10"
   },
   "toc": {
    "colors": {

examples/newsspread/NewsSpread.yml

@@ -1,138 +0,0 @@
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_time: 30
-name: Sim_all_dumb
-network_agents:
-- agent_type: DumbViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_type: DumbViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_time: 30
-name: Sim_half_herd
-network_agents:
-- agent_type: DumbViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_type: DumbViewer
-  state:
-    has_tv: true
-  weight: 1
-- agent_type: HerdViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_time: 30
-name: Sim_all_herd
-network_agents:
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    id: neutral
-  weight: 1
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    id: neutral
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-  prob_neighbor_cure: 0.1
-interval: 1
-max_time: 30
-name: Sim_wise_herd
-network_agents:
-- agent_type: HerdViewer
-  state:
-    has_tv: true
-    id: neutral
-  weight: 1
-- agent_type: WiseViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-load_module: newsspread
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-  prob_neighbor_cure: 0.1
-interval: 1
-max_time: 30
-name: Sim_all_wise
-network_agents:
-- agent_type: WiseViewer
-  state:
-    has_tv: true
-    id: neutral
-  weight: 1
-- agent_type: WiseViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50

examples/newsspread/newsspread.py

@@ -1,81 +0,0 @@
-from soil.agents import FSM, state, default_state, prob
-import logging
-
-
-class DumbViewer(FSM):
-    '''
-    A viewer that gets infected via TV (if it has one) and tries to infect
-    its neighbors once it's infected.
-    '''
-    defaults = {
-        'prob_neighbor_spread': 0.5,
-        'prob_tv_spread': 0.1,
-    }
-
-    @default_state
-    @state
-    def neutral(self):
-        if self['has_tv']:
-            if prob(self.env['prob_tv_spread']):
-                self.set_state(self.infected)
-
-    @state
-    def infected(self):
-        for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
-            if prob(self.env['prob_neighbor_spread']):
-                neighbor.infect()
-
-    def infect(self):
-        self.set_state(self.infected)
-
-
-class HerdViewer(DumbViewer):
-    '''
-    A viewer whose probability of infection depends on the state of its neighbors.
-    '''
-
-    level = logging.DEBUG
-
-    def infect(self):
-        infected = self.count_neighboring_agents(state_id=self.infected.id)
-        total = self.count_neighboring_agents()
-        prob_infect = self.env['prob_neighbor_spread'] * infected/total
-        self.debug('prob_infect', prob_infect)
-        if prob(prob_infect):
-            self.set_state(self.infected.id)
-
-
-class WiseViewer(HerdViewer):
-    '''
-    A viewer that can change its mind.
-    '''
-
-    defaults = {
-        'prob_neighbor_spread': 0.5,
-        'prob_neighbor_cure': 0.25,
-        'prob_tv_spread': 0.1,
-    }
-
-    @state
-    def cured(self):
-        prob_cure = self.env['prob_neighbor_cure']
-        for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
-            if prob(prob_cure):
-                try:
-                    neighbor.cure()
-                except AttributeError:
-                    self.debug('Viewer {} cannot be cured'.format(neighbor.id))
-
-    def cure(self):
-        self.set_state(self.cured.id)
-
-    @state
-    def infected(self):
-        cured = max(self.count_neighboring_agents(self.cured.id),
-                    1.0)
-        infected = max(self.count_neighboring_agents(self.infected.id),
-                       1.0)
-        prob_cure = self.env['prob_neighbor_cure'] * (cured/infected)
-        if prob(prob_cure):
-            return self.cure()
-        return self.set_state(super().infected)

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/pubcrawl.py

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

examples/pubcrawl/pubcrawl.yml

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

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_agents.py

@@ -1,120 +0,0 @@
-from soil.agents import FSM, state, default_state, BaseAgent
-from enum import Enum
-from random import random, choice
-from itertools import islice
-import logging
-import math
-
-
-class Genders(Enum):
-    male = 'male'
-    female = 'female'
-
-
-class RabbitModel(FSM):
-
-    level = logging.INFO
-
-    defaults = {
-        'age': 0,
-        'gender': Genders.male.value,
-        'mating_prob': 0.001,
-        'offspring': 0,
-    }
-
-    sexual_maturity = 4*30
-    life_expectancy = 365 * 3
-    gestation = 33
-    pregnancy = -1
-    max_females = 5
-
-    @default_state
-    @state
-    def newborn(self):
-        self['age'] += 1
-
-        if self['age'] >= self.sexual_maturity:
-            return self.fertile
-
-    @state
-    def fertile(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        if self['gender'] == Genders.female.value:
-            return
-
-        # Males try to mate
-        females = self.get_agents(state_id=self.fertile.id, gender=Genders.female.value, limit_neighbors=False)
-        for f in islice(females, self.max_females):
-            r = random()
-            if r < self['mating_prob']:
-                self.impregnate(f)
-                break # Take a break
-
-    def impregnate(self, whom):
-        if self['gender'] == Genders.female.value:
-            raise NotImplementedError('Females cannot impregnate')
-        whom['pregnancy'] = 0
-        whom['mate'] = self.id
-        whom.set_state(whom.pregnant)
-        self.debug('{} impregnating: {}. {}'.format(self.id, whom.id, whom.state))
-
-    @state
-    def pregnant(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        self['pregnancy'] += 1
-        self.debug('Pregnancy: {}'.format(self['pregnancy']))
-        if self['pregnancy'] >= self.gestation:
-            number_of_babies = int(8+4*random())
-            self.info('Having {} babies'.format(number_of_babies))
-            for i in range(number_of_babies):
-                state = {}
-                state['gender'] = choice(list(Genders)).value
-                child = self.env.add_node(self.__class__, state)
-                self.env.add_edge(self.id, child.id)
-                self.env.add_edge(self['mate'], child.id)
-                # self.add_edge()
-                self.debug('A BABY IS COMING TO LIFE')
-                self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.global_topology.number_of_nodes())+1
-                self.debug('Rabbits alive: {}'.format(self.env['rabbits_alive']))
-                self['offspring'] += 1
-                self.env.get_agent(self['mate'])['offspring'] += 1
-            del self['mate']
-            self['pregnancy'] = -1
-            return self.fertile
-
-    @state
-    def dead(self):
-        self.info('Agent {} is dying'.format(self.id))
-        if 'pregnancy' in self and self['pregnancy'] > -1:
-            self.info('A mother has died carrying a baby!!')
-        self.die()
-        return
-
-
-class RandomAccident(BaseAgent):
-
-    level = logging.DEBUG
-
-    def step(self):
-        rabbits_total = self.global_topology.number_of_nodes()
-        rabbits_alive = self.env.get('rabbits_alive', rabbits_total)
-        prob_death = self.env.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.env.network_agents:
-            if i.state['id'] == i.dead.id:
-                continue
-            r = random()
-            if r < prob_death:
-                self.debug('I killed a rabbit: {}'.format(i.id))
-                rabbits_alive = self.env['rabbits_alive'] = rabbits_alive -1
-                self.log('Rabbits alive: {}'.format(self.env['rabbits_alive']))
-                i.set_state(i.dead)
-        self.log('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
-        if self.count_agents(state_id=RabbitModel.dead.id) == self.global_topology.number_of_nodes():
-            self.die()

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.yml

@@ -1,23 +0,0 @@
----
-load_module: rabbit_agents
-name: rabbits_example
-max_time: 500
-interval: 1
-seed: MySeed
-agent_type: RabbitModel
-environment_agents:
-    - agent_type: RandomAccident
-environment_params:
-  prob_death: 0.001
-default_state:
-  mating_prob: 0.01
-topology:
-  nodes:
-    - id: 1
-      state:
-        gender: female
-    - id: 0
-      state:
-        gender: male
-  directed: true
-  links: []

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.yml

@@ -1,14 +0,0 @@
----
-name: torvalds_example
-max_time: 10
-interval: 2
-agent_type: CounterModel
-default_state:
-  skill_level: 'beginner'
-network_params:
-  path: 'torvalds.edgelist'
-states:
-  Torvalds:
-    skill_level: 'God'
-  balkian:
-    skill_level: 'developer'

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)

requirements.txt

@@ -1,7 +1,13 @@
-nxsim
-simpy
-networkx>=2.0
+networkx>=2.5
 numpy
 matplotlib
-pyyaml
-pandas
+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

setup.cfg

@@ -1,3 +1,7 @@
+[metadata]
+long_description = file: README.md
+long_description_content_type = text/markdown
+
 [aliases]
 test=pytest
 [tool:pytest]

setup.py

@@ -16,6 +16,12 @@ def parse_requirements(filename):
 
 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(
@@ -38,17 +44,20 @@ setup(
         'Operating System :: MacOS :: MacOS X',
         'Operating System :: Microsoft :: Windows',
         'Operating System :: POSIX',
-        'Programming Language :: Python :: 3'],
+        "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={
-        'web': ['tornado']
-
-    },
+    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.__init__:main',
+        ['soil = soil.__main__:main',
         'soil-web = soil.web.__init__:main']
     })

soil/VERSION

@@ -1 +1 @@
-0.13.4
+1.0.0rc2

soil/__init__.py

@@ -1,8 +1,12 @@
+from __future__ import annotations
+
 import importlib
+from importlib.resources import path
 import sys
 import os
-import pdb
 import logging
+import traceback
+from contextlib import contextmanager
 
 from .version import __version__
 
@@ -11,66 +15,273 @@ try:
 except NameError:
     basestring = str
 
+from pathlib import Path
+from .analysis import *
+from .agents import *
 from . import agents
 from .simulation import *
-from .environment import Environment
-from . import utils
-from . import analysis
+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
 
-def main():
     import argparse
     from . import simulation
 
-    logging.basicConfig(level=logging.INFO)
-    logging.info('Running SOIL version: {}'.format(__version__))
+    logger.info("Running SOIL version: {}".format(__version__))
 
-    parser = argparse.ArgumentParser(description='Run a SOIL simulation')
-    parser.add_argument('file', type=str,
-                        nargs="?",
-                        default='simulation.yml',
-                        help='python module containing the simulation configuration.')
-    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 store the results of the simulation.')
-    parser.add_argument('--pdb', action='store_true',
-                        help='Use a pdb console in case of exception.')
-    parser.add_argument('--graph', '-g', action='store_true',
-                        help='Dump GEXF graph. Defaults to false.')
-    parser.add_argument('--csv', action='store_true',
-                        help='Dump history in CSV format. Defaults to false.')
-    parser.add_argument('--output', '-o', type=str, default="soil_output",
-                        help='folder to write results to. It defaults to the current directory.')
-    parser.add_argument('--synchronous', action='store_true',
-                        help='Run trials serially and synchronously instead of in parallel. Defaults to false.')
+    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
 
-    logging.info('Loading config file: {}'.format(args.file))
+    debug = debug or args.debug
 
+    if args.pdb or debug:
+        args.synchronous = True
+        os.environ["SOIL_POSTMORTEM"] = "true"
+
+    res = []
     try:
-        dump = []
-        if not args.dry_run:
-            if args.csv:
-                dump.append('csv')
-            if args.graph:
-                dump.append('gexf')
-        simulation.run_from_config(args.file,
-                                   dry_run=args.dry_run,
-                                   dump=dump,
-                                   parallel=(not args.synchronous),
-                                   results_dir=args.output)
-    except Exception:
+        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:
-            pdb.post_mortem()
+            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
 
 
-if __name__ == '__main__':
+@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

@@ -1,4 +1,9 @@
-from . import main
+from . import main as init_main
 
-if __name__ == '__main__':
-    main()
+
+def main():
+    init_main()
+
+
+if __name__ == "__main__":
+    init_main()

soil/agents/BassModel.py

@@ -1,40 +1,31 @@
-import random
-from . import BaseAgent
+from . import FSM, state, default_state
 
 
-class BassModel(BaseAgent):
+class BassModel(FSM):
     """
     Settings:
         innovation_prob
         imitation_prob
     """
 
-    def __init__(self, environment, agent_id, state):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        env_params = environment.environment_params
-        self.state['sentimentCorrelation'] = 0
+    sentimentCorrelation = 0
 
     def step(self):
         self.behaviour()
 
-    def behaviour(self):
-        # Outside effects
-        if random.random() < self.state_params['innovation_prob']:
-            if self.state['id'] == 0:
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
-            else:
-                pass
-
-            return
-
-        # Imitation effects
-        if self.state['id'] == 0:
-            aware_neighbors = self.get_neighboring_agents(state_id=1)
+    @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 random.random() < (self.state_params['imitation_prob']*num_neighbors_aware):
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
+            if self.prob((self.imitation_prob * num_neighbors_aware)):
+                self.sentimentCorrelation = 1
+                return self.aware
 
-            else:
-                pass
+    @state
+    def aware(self):
+        self.die()

soil/agents/BigMarketModel.py

@@ -1,102 +0,0 @@
-import random
-from . import BaseAgent
-
-
-class BigMarketModel(BaseAgent):
-    """
-    Settings:
-        Names:
-            enterprises [Array]
-            
-            tweet_probability_enterprises [Array]
-        Users:
-            tweet_probability_users
-            
-            tweet_relevant_probability
-            
-            tweet_probability_about [Array]
-            
-            sentiment_about [Array]
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.enterprises = environment.environment_params['enterprises']
-        self.type = ""
-        self.number_of_enterprises = len(environment.environment_params['enterprises'])
-
-        if self.id < self.number_of_enterprises:  # Enterprises
-            self.state['id'] = self.id
-            self.type = "Enterprise"
-            self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id]
-        else:  # normal users
-            self.state['id'] = self.number_of_enterprises
-            self.type = "User"
-            self.tweet_probability = environment.environment_params['tweet_probability_users']
-            self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability']
-            self.tweet_probability_about = environment.environment_params['tweet_probability_about']  # List
-            self.sentiment_about = environment.environment_params['sentiment_about']  # List
-
-    def step(self):
-
-        if self.id < self.number_of_enterprises:  # Enterprise
-            self.enterpriseBehaviour()
-        else:  # Usuario
-            self.userBehaviour()
-            for i in range(self.number_of_enterprises):  # So that it never is set to 0 if there are not changes (logs)
-                self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
-
-    def enterpriseBehaviour(self):
-
-        if random.random() < self.tweet_probability:  # Tweets
-            aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbour users
-            for x in aware_neighbors:
-                if random.uniform(0,10) < 5:
-                    x.sentiment_about[self.id] += 0.1  # Increments for enterprise
-                else:
-                    x.sentiment_about[self.id] -= 0.1  # Decrements for enterprise
-
-                # Establecemos limites
-                if x.sentiment_about[self.id] > 1:
-                    x.sentiment_about[self.id] = 1
-                if x.sentiment_about[self.id]< -1:
-                    x.sentiment_about[self.id] = -1
-
-                x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
-
-    def userBehaviour(self):
-
-        if random.random() < self.tweet_probability:  # Tweets
-            if random.random() < self.tweet_relevant_probability:  # Tweets something relevant
-                # Tweet probability per enterprise
-                for i in range(self.number_of_enterprises):
-                    random_num = random.random()
-                    if random_num < self.tweet_probability_about[i]:
-                        # The condition is fulfilled, sentiments are evaluated towards that enterprise
-                        if self.sentiment_about[i] < 0:
-                            # NEGATIVO
-                            self.userTweets("negative",i)
-                        elif self.sentiment_about[i] == 0:
-                            # NEUTRO
-                            pass
-                        else:
-                            # POSITIVO
-                            self.userTweets("positive",i)
-
-    def userTweets(self,sentiment,enterprise):
-        aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbours users
-        for x in aware_neighbors:
-            if sentiment == "positive":
-                x.sentiment_about[enterprise] +=0.003
-            elif sentiment == "negative":
-                x.sentiment_about[enterprise] -=0.003
-            else:
-                pass
-
-            # Establecemos limites
-            if x.sentiment_about[enterprise] > 1:
-                x.sentiment_about[enterprise] = 1
-            if x.sentiment_about[enterprise] < -1:
-                x.sentiment_about[enterprise] = -1
-
-            x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]

soil/agents/CounterModel.py

@@ -1,32 +1,46 @@
-from . import BaseAgent
+from . import BaseAgent, NetworkAgent
 
 
-class CounterModel(BaseAgent):
+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.get_all_agents()))
-        neighbors = len(list(self.get_neighboring_agents()))
-        self['times'] = self.get('times', 0) + 1
-        self['neighbors'] = neighbors
-        self['total'] = total
+        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(BaseAgent):
+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
-        total = len(list(self.get_all_agents()))
-        neighbors = len(list(self.get_neighboring_agents()))
-        self['times'] = self.get('times', 0) + 1
-        self['neighbors'] = self.get('neighbors', 0) + neighbors
-        self['total'] = total = self.get('total', 0) + total
-        self.debug('Running for step: {}. Total: {}'.format(self.now, total))
+        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/DrawingAgent.py

@@ -1,18 +0,0 @@
-from . import BaseAgent
-
-import os.path
-import matplotlib
-import matplotlib.pyplot as plt
-import networkx as nx
-
-
-class DrawingAgent(BaseAgent):
-    """
-    Agent that draws the state of the network.
-    """
-
-    def step(self):
-        # Outside effects
-        f = plt.figure()
-        nx.draw(self.env.G, node_size=10, width=0.2, pos=nx.spring_layout(self.env.G, scale=100), ax=f.add_subplot(111))
-        f.savefig(os.path.join(self.env.get_path(), "graph-"+str(self.env.now)+".png"))

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

@@ -1,8 +1,7 @@
-import random
-from . import BaseAgent
+from . import Agent, state, default_state
 
 
-class IndependentCascadeModel(BaseAgent):
+class IndependentCascadeModel(Agent):
     """
     Settings:
         innovation_prob
@@ -10,40 +9,22 @@ class IndependentCascadeModel(BaseAgent):
         imitation_prob
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.innovation_prob = environment.environment_params['innovation_prob']
-        self.imitation_prob = environment.environment_params['imitation_prob']
-        self.state['time_awareness'] = 0
-        self.state['sentimentCorrelation'] = 0
+    time_awareness = 0
+    sentimentCorrelation = 0
 
-    def step(self):
-        self.behaviour()
+    # 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
 
-    def behaviour(self):
-        aware_neighbors_1_time_step = []
-        # Outside effects
-        if random.random() < self.innovation_prob:
-            if self.state['id'] == 0:
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
-                self.state['time_awareness'] = self.env.now  # To know when they have been infected
-            else:
-                pass
+    @state
+    def imitate(self):
+        aware_neighbors = self.get_neighbors(state_id=1, time_awareness=self.now-1)
 
-            return
-
-        # Imitation effects
-        if self.state['id'] == 0:
-            aware_neighbors = self.get_neighboring_agents(state_id=1)
-            for x in aware_neighbors:
-                if x.state['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() < (self.imitation_prob*num_neighbors_aware):
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
-            else:
-                pass
-
-            return
+        if self.prob(self.model.imitation_prob * len(aware_neighbors)):
+            self.sentimentCorrelation = 1
+            return self.outside

soil/agents/ModelM2.py

@@ -1,242 +0,0 @@
-import random
-import numpy as np
-from . import BaseAgent
-
-
-class SpreadModelM2(BaseAgent):
-    """
-    Settings:
-        prob_neutral_making_denier
-
-        prob_infect
-
-        prob_cured_healing_infected
-
-        prob_cured_vaccinate_neutral
-
-        prob_vaccinated_healing_infected
-
-        prob_vaccinated_vaccinate_neutral
-
-        prob_generate_anti_rumor
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
-                                                           environment.environment_params['standard_variance'])
-
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
-                                            environment.environment_params['standard_variance'])
-
-        self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
-                                                            environment.environment_params['standard_variance'])
-        self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
-                                                             environment.environment_params['standard_variance'])
-
-        self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
-                                                                 environment.environment_params['standard_variance'])
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
-                                                                  environment.environment_params['standard_variance'])
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
-                                                         environment.environment_params['standard_variance'])
-
-    def step(self):
-
-        if self.state['id'] == 0:  # Neutral
-            self.neutral_behaviour()
-        elif self.state['id'] == 1:  # Infected
-            self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
-            self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
-            self.vaccinated_behaviour()
-
-    def neutral_behaviour(self):
-
-        # Infected
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        if len(infected_neighbors) > 0:
-            if random.random() < self.prob_neutral_making_denier:
-                self.state['id'] = 3   # Vaccinated making denier
-
-    def infected_behaviour(self):
-
-        # Neutral
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
-                neighbor.state['id'] = 1  # Infected
-
-    def cured_behaviour(self):
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-    def vaccinated_behaviour(self):
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Generate anti-rumor
-        infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
-
-
-class ControlModelM2(BaseAgent):
-    """
-    Settings:
-        prob_neutral_making_denier
-
-        prob_infect
-        
-        prob_cured_healing_infected
-        
-        prob_cured_vaccinate_neutral
-        
-        prob_vaccinated_healing_infected
-        
-        prob_vaccinated_vaccinate_neutral
-        
-        prob_generate_anti_rumor
-    """
-
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
-                                                           environment.environment_params['standard_variance'])
-
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
-                                            environment.environment_params['standard_variance'])
-
-        self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
-                                                            environment.environment_params['standard_variance'])
-        self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
-                                                             environment.environment_params['standard_variance'])
-
-        self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
-                                                                 environment.environment_params['standard_variance'])
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
-                                                                  environment.environment_params['standard_variance'])
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
-                                                         environment.environment_params['standard_variance'])
-
-    def step(self):
-
-        if self.state['id'] == 0:  # Neutral
-            self.neutral_behaviour()
-        elif self.state['id'] == 1:  # Infected
-            self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
-            self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
-            self.vaccinated_behaviour()
-        elif self.state['id'] == 4:  # Beacon-off
-            self.beacon_off_behaviour()
-        elif self.state['id'] == 5:  # Beacon-on
-            self.beacon_on_behaviour()
-
-    def neutral_behaviour(self):
-        self.state['visible'] = False
-
-        # Infected
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        if len(infected_neighbors) > 0:
-            if random.random() < self.prob_neutral_making_denier:
-                self.state['id'] = 3   # Vaccinated making denier
-
-    def infected_behaviour(self):
-
-        # Neutral
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
-                neighbor.state['id'] = 1  # Infected
-        self.state['visible'] = False
-
-    def cured_behaviour(self):
-
-        self.state['visible'] = True
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-    def vaccinated_behaviour(self):
-        self.state['visible'] = True
-
-        # Cure
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
-
-        # Generate anti-rumor
-        infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
-
-    def beacon_off_behaviour(self):
-        self.state['visible'] = False
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        if len(infected_neighbors) > 0:
-            self.state['id'] == 5  # Beacon on
-
-    def beacon_on_behaviour(self):
-        self.state['visible'] = False
-        # Cure (M2 feature added)
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        for neighbor in infected_neighbors:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
-            neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
-            for neighbor in neutral_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
-                    neighbor.state['id'] = 3  # Vaccinated
-            infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
-            for neighbor in infected_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
-                    neighbor.state['id'] = 2  # Cured
-
-        # Vaccinate
-        neutral_neighbors = self.get_neighboring_agents(state_id=0)
-        for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated

soil/agents/SISaModel.py

@@ -1,93 +1,110 @@
-import random
 import numpy as np
-from . import FSM, state
+from hashlib import sha512
+from . import Agent, state, default_state
 
 
-class SISaModel(FSM):
+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, environment, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
-        self.neutral_discontent_spon_prob = np.random.normal(self.env['neutral_discontent_spon_prob'],
-                                                             self.env['standard_variance'])
-        self.neutral_discontent_infected_prob = np.random.normal(self.env['neutral_discontent_infected_prob'],
-                                                                 self.env['standard_variance'])
-        self.neutral_content_spon_prob = np.random.normal(self.env['neutral_content_spon_prob'],
-                                                          self.env['standard_variance'])
-        self.neutral_content_infected_prob = np.random.normal(self.env['neutral_content_infected_prob'],
-                                                              self.env['standard_variance'])
+        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')
 
-        self.discontent_neutral = np.random.normal(self.env['discontent_neutral'],
-                                                   self.env['standard_variance'])
-        self.discontent_content = np.random.normal(self.env['discontent_content'],
-                                                   self.env['variance_d_c'])
+        random = np.random.default_rng(seed=seed)
 
-        self.content_discontent = np.random.normal(self.env['content_discontent'],
-                                                   self.env['variance_c_d'])
-        self.content_neutral = np.random.normal(self.env['content_neutral'],
-                                                self.env['standard_variance'])
+        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 random.random() < self.neutral_discontent_spon_prob:
+        if self.prob(self.neutral_discontent_spon_prob):
             return self.discontent
-        if random.random() < self.neutral_content_spon_prob:
+        if self.prob(self.neutral_content_spon_prob):
             return self.content
 
         # Infected
-        discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent)
-        if random.random() < discontent_neighbors * self.neutral_discontent_infected_prob:
+        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_neighboring_agents(state_id=self.content.id)
-        if random.random() < content_neighbors * self.neutral_content_infected_prob:
+        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 random.random() < self.discontent_neutral:
+        if self.prob(self.discontent_neutral):
             return self.neutral
 
         # Superinfected
-        content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
-        if random.random() < content_neighbors * self.discontent_content:
+        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 random.random() < self.content_neutral:
+        if self.prob(self.content_neutral):
             return self.neutral
 
         # Superinfected
-        discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent.id)
-        if random.random() < discontent_neighbors * self.content_discontent:
+        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/SentimentCorrelationModel.py

@@ -1,102 +0,0 @@
-import random
-from . import BaseAgent
-
-
-class SentimentCorrelationModel(BaseAgent):
-    """
-    Settings:
-        outside_effects_prob
-        
-        anger_prob
-        
-        joy_prob
-        
-        sadness_prob
-        
-        disgust_prob
-    """
-
-    def __init__(self, environment, 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.state['time_awareness'] = []
-        for i in range(4):  # In this model we have 4 sentiments
-            self.state['time_awareness'].append(0)  # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
-        self.state['sentimentCorrelation'] = 0
-
-    def step(self):
-        self.behaviour()
-
-    def behaviour(self):
-
-        angry_neighbors_1_time_step = []
-        joyful_neighbors_1_time_step = []
-        sad_neighbors_1_time_step = []
-        disgusted_neighbors_1_time_step = []
-
-        angry_neighbors = self.get_neighboring_agents(state_id=1)
-        for x in angry_neighbors:
-            if x.state['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.state['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.state['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.state['time_awareness'][3] > (self.env.now-500):
-                disgusted_neighbors_1_time_step.append(x)
-        num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
-
-        anger_prob = self.anger_prob+(len(angry_neighbors_1_time_step)*self.anger_prob)
-        joy_prob = self.joy_prob+(len(joyful_neighbors_1_time_step)*self.joy_prob)
-        sadness_prob = self.sadness_prob+(len(sad_neighbors_1_time_step)*self.sadness_prob)
-        disgust_prob = self.disgust_prob+(len(disgusted_neighbors_1_time_step)*self.disgust_prob)
-        outside_effects_prob = self.outside_effects_prob
-
-        num = random.random()
-
-        if num<outside_effects_prob:
-            self.state['id'] = random.randint(1, 4)
-
-            self.state['sentimentCorrelation'] = self.state['id'] # It is stored when it has been infected for the dynamic network
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-            self.state['sentiment'] = self.state['id']
-
-
-        if(num<anger_prob):
-
-            self.state['id'] = 1
-            self.state['sentimentCorrelation'] = 1
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-        elif (num<joy_prob+anger_prob and num>anger_prob):
-
-            self.state['id'] = 2
-            self.state['sentimentCorrelation'] = 2
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-        elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
-
-            self.state['id'] = 3
-            self.state['sentimentCorrelation'] = 3
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-        elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
-
-            self.state['id'] = 4
-            self.state['sentimentCorrelation'] = 4
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-
-        self.state['sentiment'] = self.state['id']

soil/agents/__init__.py

@@ -1,185 +1,237 @@
-# 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.
-    
+from __future__ import annotations
 
-import nxsim
 import logging
-from collections import OrderedDict
-from copy import deepcopy
-from functools import partial
-import json
+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 functools import wraps
+from typing import Any
 
-from .. import utils, history
+from mesa import Agent as MesaAgent, Model
+from typing import Dict, List
+
+from .. import serialization, network, utils, time, config
 
 
-class BaseAgent(nxsim.BaseAgent):
+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 simpy BaseAgent that keeps track of its state history.
+    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.
     """
 
-    defaults = {}
+    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)
 
-    def __init__(self, environment, agent_id, state=None,
-                 name='network_process', interval=None, **state_params):
-        # Check for REQUIRED arguments
-        assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. '
-                                                  'Cannot be NoneType.')
-        # Initialize agent parameters
-        self.id = agent_id
-        self.name = name
-        self.state_params = state_params
+        self.name = (
+            str(name) if name else "{}[{}]".format(type(self).__name__, self.unique_id)
+        )
 
-        # Register agent to environment
-        self.env = environment
-
-        self._neighbors = None
         self.alive = True
-        real_state = deepcopy(self.defaults)
-        real_state.update(state or {})
-        self.state = real_state
-        self.interval = interval
 
-        if not hasattr(self, 'level'):
-            self.level = logging.DEBUG
-        self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name,
-                                                             self.id))
-        self.logger.setLevel(self.level)
+        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})
 
-        # initialize every time an instance of the agent is created
-        self.action = self.env.process(self.run())
+        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 state(self):
-        '''
-        Return the agent itself, which behaves as a dictionary.
-        Changes made to `agent.state` will be reflected in the history.
+    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
 
-        This method shouldn't be used, but is kept here for backwards compatibility.
-        '''
-        return self
-
-    @state.setter
-    def state(self, value):
-        self._state = {}
-        for k, v in value.items():
-            self[k] = v
-
-    @property
-    def global_topology(self):
-        return self.env.G
-    
-    @property
-    def environment_params(self):
-        return self.env.environment_params
-    
-    @environment_params.setter
-    def environment_params(self, value):
-        self.env.environment_params = value
+    @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):
-        if isinstance(key, tuple):
-            key, t_step = key
-            k = history.Key(key=key, t_step=t_step, agent_id=self.id)
-            return self.env[k]
-        return self._state.get(key, None)
+        try:
+            return getattr(self, key)
+        except AttributeError:
+            raise KeyError(f"key {key}  not found in agent")
 
     def __delitem__(self, key):
-        self._state[key] = None
+        return delattr(self, key)
 
     def __contains__(self, key):
-        return key in self._state
+        return hasattr(self, key)
 
     def __setitem__(self, key, value):
-        self._state[key] = value
-        k = history.Key(t_step=self.now,
-                        agent_id=self.id,
-                        key=key)
-        self.env[k] = value
+        setattr(self, key, value)
 
-    def items(self):
-        return self._state.items()
+    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):
-        return self[key] if key in self else default
+        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.env.now
+            return self.model.now
         except AttributeError:
             # No environment
             return None
 
-    def run(self):
-        if self.interval is not None:
-            interval = self.interval
-        elif 'interval' in self:
-            interval = self['interval']
-        else:
-            interval = self.env.interval
-        while self.alive:
-            res = self.step()
-            yield res or self.env.timeout(interval)
-
-    def die(self, remove=False):
+    def die(self, msg=None):
+        if msg:
+            self.info("Agent dying:", msg)
+        self.debug(f"agent dying")
         self.alive = False
-        if remove:
-            super().die()
+        try:
+            self.model.schedule.remove(self)
+        except KeyError:
+            pass
+        return time.NEVER
 
     def step(self):
-        pass
+        raise NotImplementedError("Agent must implement step method")
+    
+    def _check_alive(self):
+        if not self.alive:
+            raise time.DeadAgent(self.unique_id)
 
-    def count_agents(self, state_id=None, limit_neighbors=False):
-        if limit_neighbors:
-            agents = self.global_topology.neighbors(self.id)
-        else:
-            agents = self.global_topology.nodes()
-        count = 0
-        for agent in agents:
-            if state_id and state_id != self.global_topology.node[agent]['agent']['id']:
-                continue
-            count += 1
-        return count
-
-    def count_neighboring_agents(self, state_id=None):
-        return len(super().get_agents(state_id, limit_neighbors=True))
-
-    def get_agents(self, state_id=None, agent_type=None, limit_neighbors=False, iterator=False, **kwargs):
-        agents = self.env.agents
-        if limit_neighbors:
-            agents = super().get_agents(state_id, limit_neighbors)
-
-        def matches_all(agent):
-            if state_id is not None:
-                if agent.state.get('id', None) != state_id:
-                    return False
-            if agent_type is not None:
-                if type(agent) != agent_type:
-                    return False
-            state = agent.state
-            for k, v in kwargs.items():
-                if state.get(k, None) != v:
-                    return False
-            return True
-
-        f = filter(matches_all, agents)
-        if iterator:
-            return f
-        return list(f)
-
-    def log(self, message, *args, level=logging.INFO, **kwargs):
-        message = message + " ".join(str(i) for i in args)
-        message = "\t@{:>5}:\t{}".format(self.now, message)
+    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['id'] = self.id
+        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):
@@ -187,141 +239,64 @@ class BaseAgent(nxsim.BaseAgent):
 
     def info(self, *args, **kwargs):
         return self.log(*args, level=logging.INFO, **kwargs)
-    
-    def __getstate__(self):
-        '''
-        Serializing an agent will lose all its running information (you cannot
-        serialize an iterator), but it keeps the state and link to the environment,
-        so it can be used for inspection and dumping to a file
-        '''
-        state = {}
-        state['id'] = self.id
-        state['environment'] = self.env
-        state['_state'] = self._state
-        return state
 
-    def __setstate__(self, state):
-        '''
-        Get back a serialized agent and try to re-compose it
-        '''
-        self.id = state['id']
-        self._state = state['_state']
-        self.env = state['environment']
+    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 state(func):
-    '''
-    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 nevironment.
-    '''
-
-    @wraps(func)
-    def func_wrapper(self):
-        next_state = func(self)
-        when = None
-        if next_state is None:
-            return when
-        try:
-            next_state, when = next_state
-        except (ValueError, TypeError):
-            pass
-        if next_state:
-            self.set_state(next_state)
-        return when
-
-    func_wrapper.id = func.__name__
-    func_wrapper.is_default = False
-    return func_wrapper
-
-
-def default_state(func):
-    func.is_default = True
-    return func
-
-
-class MetaFSM(type):
-    def __init__(cls, name, bases, nmspc):
-        super(MetaFSM, cls).__init__(name, bases, nmspc)
-        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 name, func in nmspc.items():
-            if hasattr(func, 'id'):
-                if func.is_default:
-                    default_state = func
-                states[func.id] = func
-        cls.default_state = default_state
-        cls.states = states
-
-
-class FSM(BaseAgent, metaclass=MetaFSM):
-    def __init__(self, *args, **kwargs):
-        super(FSM, self).__init__(*args, **kwargs)
-        if 'id' not in self.state:
-            if not self.default_state:
-                raise ValueError('No default state specified for {}'.format(self.id))
-            self['id'] = self.default_state.id
-
-    def step(self):
-        if 'id' in self.state:
-            next_state = self['id']
-        elif self.default_state:
-            next_state = self.default_state.id
-        else:
-            raise Exception('{} has no valid state id or default state'.format(self))
-        if next_state not in self.states:
-            raise Exception('{} is not a valid id for {}'.format(next_state, self))
-        self.states[next_state](self)
-
-    def set_state(self, state):
-        if hasattr(state, 'id'):
-            state = state.id
-        if state not in self.states:
-            raise ValueError('{} is not a valid state'.format(state))
-        self['id'] = state
-        return state
-
-
-def prob(prob=1):
-    '''
+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_type=None):
-    '''
+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_type': 'agent_type_1',
+            {'agent_class': 'agent_class_1',
                 'weight': 0.2,
                 'state': {
                     'id': 0
                 }
             },
-            {'agent_type': 'agent_type_2',
+            {'agent_class': 'agent_class_2',
                 'weight': 0.8,
                 'state': {
                     'id': 1
@@ -330,99 +305,368 @@ def calculate_distribution(network_agents=None,
             ]
 
     In this example, 20% of the nodes will be marked as type
-    'agent_type_1'.
-    '''
+    'agent_class_1'.
+    """
     if network_agents:
-        network_agents = deepcopy(network_agents)
-    elif agent_type:
-        network_agents = [{'agent_type': agent_type}]
+        network_agents = [
+            deepcopy(agent) for agent in network_agents if not hasattr(agent, "id")
+        ]
+    elif agent_class:
+        network_agents = [{"agent_class": agent_class}]
     else:
-        return []
+        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.get('weight', 1) for x in network_agents)
+    total = sum(x["weight"] for x in network_agents)
     acc = 0
     for v in network_agents:
-        upper = acc + (v.get('weight', 1)/total)
-        v['threshold'] = [acc, upper]
+        if "ids" in v:
+            continue
+        upper = acc + (v["weight"] / total)
+        v["threshold"] = [acc, upper]
         acc = upper
     return network_agents
 
 
-def serialize_type(agent_type, known_modules=[], **kwargs):
-    if isinstance(agent_type, str):
-        return agent_type
-    known_modules += ['soil.agents']
-    return utils.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class
+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 serialize_distribution(network_agents, known_modules=[]):
-    '''
-    When serializing an agent distribution, remove the thresholds, in order
-    to avoid cluttering the YAML definition file.
-    '''
-    d = deepcopy(list(network_agents))
-    for v in d:
-        if 'threshold' in v:
-            del v['threshold']
-        v['agent_type'] = serialize_type(v['agent_type'],
-                                         known_modules=known_modules)
-    return d
+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
 
 
-def deserialize_type(agent_type, known_modules=[]):
-    if not isinstance(agent_type, str):
-        return agent_type
-    known = known_modules + ['soil.agents', 'soil.agents.custom' ]
-    agent_type = utils.deserializer(agent_type, known_modules=known)
-    return agent_type
+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 deserialize_distribution(ind, **kwargs):
-    d = deepcopy(ind)
-    for v in d:
-        v['agent_type'] = deserialize_type(v['agent_type'], **kwargs)
-    return d
+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 = []
 
-def _validate_states(states, topology):
-    '''Validate states to avoid ignoring states during initialization'''
-    states = states or []
-    if isinstance(states, dict):
-        for x in states:
-            assert x in topology.node
+    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:
-        assert len(states) <= len(topology)
-    return states
+        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 _convert_agent_types(ind, to_string=False, **kwargs):
-    '''Convenience method to allow specifying agents by class or class name.'''
-    if to_string:
-        return serialize_distribution(ind, **kwargs)
-    return deserialize_distribution(ind, **kwargs)
+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 _agent_from_distribution(distribution, value=-1):
-    """Used in the initialization of agents given an agent distribution."""
-    if value < 0:
-        value = random.random()
-    for d in distribution:
-        threshold = d['threshold']
-        if value >= threshold[0] and value < threshold[1]:
-            state = {}
-            if 'state' in d:
-                state = deepcopy(d['state'])
-            return d['agent_type'], state
+def _from_fixed(
+    lst: List[config.FixedAgentConfig],
+    topology: bool,
+    default: config.SingleAgentConfig,
+) -> List[Dict[str, Any]]:
+    agents = []
 
-    raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
+    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 .BigMarketModel import *
 from .IndependentCascadeModel import *
-from .ModelM2 import *
-from .SentimentCorrelationModel import *
 from .SISaModel import *
 from .CounterModel import *
-from .DrawingAgent 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)